The objectives of this paper are to describe the planned implementation and evaluation of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care (BETTER 2) program which originated from the BETTER trial. The pragmatic trial, informed by the Chronic Care Model, demonstrated the effectiveness of an approach to Chronic Disease Prevention and Screening (CDPS) involving the use of a new role, the prevention practitioner. The desired goals of the program are improved clinical outcomes, reduction in the burden of chronic disease, and improved sustainability of the health-care system through improved CDPS in primary care.The BETTER 2 program aims to expand the implementation of the intervention used in the original BETTER trial into communities across Canada . This proactive approach provides at-risk patients with an intervention from the prevention practitioner, a health-care professional. Using the BETTER toolkit, the prevention practitioner determines which CDPS actions the patient is eligible to receive, and through shared decision-making and motivational interviewing, develops a unique and individualized ‘prevention prescription’ with the patient. This intervention is 1) personalized; 2) addressing multiple conditions; 3) integrated through linkages to local, regional, or national resources; and 4) longitudinal by assessing patients over time. The BETTER 2 program brings together primary care providers, policy/decision makers and researchers to work towards improving CDPS in primary care. The target patient population is adults aged 40–65. The reach, effectiveness, adoption, implementation, maintain (RE-AIM) framework will inform the evaluation of the program through qualitative and quantitative methods. A composite index will be used to quantitatively assess the effectiveness of the prevention practitioner intervention. The CDPS actions comprising the composite index include the following: process measures, referral/treatment measures, and target/change outcome measures related to cardiovascular disease, diabetes, cancer and associated lifestyle factors.integration not creation). The program evaluation is designed to provide an understanding of issues impacting the implementation of an effective approach for CDPS within primary care that may be adapted to become sustainable in the non-research setting.The BETTER 2 program is a collaborative approach grounded in practice and built from existing work (i.e.,  The purpose of this paper is to describe the planned implementation and evaluation of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care (BETTER 2) program. We will describe the unique approach the BETTER 2 program takes to address chronic disease prevention and screening (CDPS) in primary care settings including the frameworks and evaluation metrics that will be used to measure the effectiveness of the program in achieving its goals.The paper is divided into the following three broad sections: 1) the ‘Background’ section provides an overview of CDPS in primary care and discusses the BETTER approach to CDPS and the integral role that the prevention practitioner plays in promoting CDPS. This section further reviews the effectiveness of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial and describes the evolution of the BETTER 2 program including the specific objectives it seeks to accomplish. 2) The ‘Methods/Design’ section explains the prevention practitioner role and how the BETTER 2 program will expand and adapt to novel jurisdictions. This section further includes a description of the planned evaluation of the BETTER 2 program, which involves qualitative, quantitative, and health economic components. 3) Lastly, the ‘Discussion’ section highlights the unique approach utilized for knowledge integration and the potential outcomes of the proposed program evaluation.The prevalence of chronic disease is steadily increasing ,2. TheseThe BETTER trial, a pragmatic randomized controlled trial, previously demonstrated the effectiveness of an approach to CDPS that involved 1) a clinical working group (CWG) which identified and harmonized the evidence for CDPS actions in patients aged 40–65 .Figure 1The prevention practitioner was a new role introduced to the primary care practice setting . In the The original BETTER trial demonstrated that a prevention practitioner ‘can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner’  in urbanThe overarching aims of the BETTER 2 program are to improve clinical outcomes, reduce the burden of chronic disease, and improve the sustainability of the health-care system through improved CDPS in primary care. The program targets those chronic diseases that have strong evidence for prevention and screening, specifically cancer, cardiovascular disease, diabetes, and their associated lifestyle risk factors in patients aged 40–65.To introduce the BETTER approach to new jurisdictions and deepen the impact in the two original participating provincesTo adapt, revise, and tailor the BETTER individual patient-level CDPS intervention including the survey, tools, actions, and resources for uptake in a variety of settings including remote, rural, aboriginal populations, and disadvantaged populationsTo synthesize BETTER knowledge products for translation, dissemination and exchange through the development of a web resource with updated CDPS tools, actions, and training resourcesTo evaluate the adapted BETTER intervention using a mixed methods approachSpecific objectives of the BETTER 2 program are the following:Experts in research, practice, and policy worked together to develop the BETTER 2 program with the aim of addressing CDPS from a policy and practice perspective including the needs of the end users, such as health-care professionals, a key to integrated knowledge translation –11. SincWe propose to introduce an intervention that is proactive at both the population and an individual patient-level. The patient intervention consists of a dedicated prevention visit with the prevention practitioner . Activated communities and partners will be the ones to support policies and organizational structures and engage in the BETTER 2 program.Informed, activated individuals and families through patient participation. The sites will select and identify at-risk populations such as adult patients of the participating primary care providers who are between the ages of 40 and 65, and data will be obtained from those who provide written informed consent to participate. We chose this age group because this is the age group for which most of the CDPS actions can be applied [Prepared proactive practice teams including primary care providers, researchers, and policy/decision makers who will work together with the BETTER 2 team to develop a comprehensive source of tools aimed to transform practice through a patient-level intervention by a health-care professional within the practice, the prevention practitioner.The target populations include  did not attend any visits . In our The number of patients approached to participate in the program (denominator)The number of patients who agree to participate and who follow-through to actually have a visit with the prevention practitionerThe number of primary care providers/sites approached and educated about BETTER and the proportion that have adopted the tools or adapted the prevention practitioner role into their practicesDemographic information (if available) of participating patients including ethnicity, gender, age, socioeconomic status, and primary care siteThe reach of the program will include the following:The effectiveness of the program will be evaluated using the explicitly defined summary statistics defined below as well as via the use of a more comprehensive composite index .The proportion of individual CDPS activities achieved at any point in time. We will identify specific CDPS action information that we can capture in the population at six-month intervals . We will track the proportion of patients completing these maneuvers longitudinally at each of these observation points.An evaluation of the CDPS achieved using a composite index  developeSpecifically, effectiveness measures will include the following:The composite index is comprised of process outcome measures , referral and treatment measures , and target/change outcomes . The CDPS actions in each patient eligible to complete as part of the program are determined at baseline, and the achievement of these eligible actions is assessed at follow-up. Where possible, a composite index for each patient will be calculated at baseline and then at six-month intervals. If a patient is ineligible (up to date) for all CDPS maneuvers, then his/her contribution to the aggregate composite index is non-calculable as the denominator (the number of eligible items at baseline) is zero.The composite index will allow for measures of change over time, that is, a gross indicator of effectiveness. The composite index essentially provides an average measure of effectiveness over all people enrolled in the study, and suggests the proportion of conditionally eligible maneuvers (as defined at baseline) which are achieved at follow-up. It acts to average over the heterogeneity of the patients and the maneuvers they are specifically eligible for; hence, it is referred to as a gross measure of effectiveness. For any given patient, the composite index can also act as a benchmark of how the patient is doing with respect to CDPS at any given moment in time. We will use the composite index as a comprehensive measure of CDPS to describe the proportion of eligible actions achieved at six-month intervals, and it will also allow researchers the opportunity to investigate certain comparisons of CDPS effectiveness between group factors, such as jurisdiction, age, sex, etc.Adoption will be assessed by describing the proportion, characteristics, and representativeness of Canadian settings approached and those willing to participate in the BETTER 2 program. We will describe the national/provincial/territorial characteristics of the sites, including their similarities and differences relative to other communities in a larger geographic area or region.Implementation of BETTER 2 in the diverse primary care settings will be captured by detailed description of the contextual adaptations of the program, the time required for intervention, and the costs of the intervention. In addition, the ‘Qualitative Evaluation’ section details the qualitative approach aimed to capture implementation information including process, facilitators and barriers, and contextual issues.A description of the implementation cost of the intervention in various settings including the amount of time the prevention practitioners spend with individual patients and the improvement in CDPS activities achieved by the various patient populations in the intense intervention groups.We will explore the impact of the different levels of interventions on the costs of the program and examine patterns in costs over time.An economic assessment will include the following:We will explore the extent that the program becomes part of a routine practice and policy over time including how and if the program can be delivered over the long term. Most decisions about maintaining a program are influenced not only by the overall impact of a treatment but also by its costs . The timThe proportion of primary care providers introduced to the BETTER 2 approach who use the BETTER tools/activities calculated at three time points  with participants (as identified above) by a trained researcher at approximately three time periods throughout the project   national and provincial (macro), 2) regional or program (meso), and 3) site or clinic (micro) levels . There aThe TDF is a comprehensive framework that includes all of the important constructs of implementation ,29. SincThe BETTER 2 is a program evaluation and not a research trial. The BETTER 2 program has received approval for the various research components of the program evaluation through the University of Alberta, and St. John’s Newfoundland ethics boards. The program also has a scientific research license with the Northwest Territories. The registration number of the original RCT BETTER trial was ISRCTN07170460.The prevalence of chronic disease is steadily increasing ,2, and tOur ultimate long-term outcomes are improved clinical outcomes and reduced burden of chronic disease through improved early detection of cancers ; lower incidence of cancers through modification of lifestyle ; and reduction of chronic disease through prevention . Cost-effective analyses of the original BETTER trial demonstrated that a prevention practitioner ‘can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner’ . We expe


Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks. The limited availability of detailed biological data has always posed a major challenge to the advance of neuroscientific understanding. The formulation of theories about information processing in the brain has therefore been predominantly model-driven, with much freedom of choice in model architecture and parameters. As more powerful mathematical and computational tools became available, increasingly detailed and complex cortical models have been proposed. However, because of the manifest nonlinearity and sheer complexity of interactions that take place in the nervous system, analytically treatable ensemble-based models can only partly cover the vast range of activity patterns and behavioral phenomena that are characteristic for biological nervous systems The feasibility of the computational approach has been facilitated by the development of the hardware devices used to run neural network simulations. The brisk pace at which available processing speed has been increasing over the past few decades, as allegorized by Moore's Law, as well as the advancement of computer architectures in general, closely correlate to the size and complexity of simulated models. Today, network models with tens of thousands of neurons are routinely simulated on desktop machines, with supercomputers allowing several orders of magnitude more Conventional simulation becomes especially restrictive when considering long time scales, such as are required for modeling long-term network dynamics or when performing statistics-intensive experiments. Additionally, power consumption can quickly become prohibitive at these scales in silico: instead of calculating the dynamics of neural networks, neuromorphic devices contain physical representations of the networks themselves, behaving, by design, according to the same dynamic laws. An immediate advantage of this approach is its inherent parallelism , which is particularly advantageous in terms of scalability. First proposed by Mead in the 1980s The above issues can, however, be eluded by reconsidering the fundamental design principles of conventional computer systems. The core idea of the so-called neuromorphic approach is to implement features (such as connectivity) or components  of neural networks directly By far the largest number of neuromorphic systems developed thus far are highly application-specific, such as visual processing systems These devices, however, being rather specialized, can not match the flexibility of traditional software simulations. Adding configurability comes at a high price in terms of hardware resources, due to various hardware-specific limitations, such as physical size and essentially two-dimensional structure. So far there have only been few attempts at realizing highly configurable hardware emulators A very efficient way of interconnecting multiple VLSI  modules is offered by so-called wafer-scale integration. This implies the realization of both the modules in question and their communication infrastructure on the same silicon wafer, the latter being done in a separate, post-processing step. The BrainScaleS wafer-scale hardware distortion mechanisms. The neural network emulated by the hardware device can therefore differ significantly from the original model, be it in terms of pulse transmission, connectivity between populations or individual neuron or synapse parameters. We refer to all the changes in network dynamics  caused by hardware-specific effects as hardware-induced distortions.Along with the many advantages it offers, the neuromorphic approach also comes with limitations of its own. These have various causes that lie both in the hardware itself and the control software. We will later identify these causes, which we henceforth refer to as Due to the complexity of state-of-the-art neuromorphic platforms and their control software, as well as the vast landscape of emulable neural network models, a thorough and systematic approach is essential for providing reliable information about causal mechanisms and functional effects of hardware-induced distortions in model dynamics and for ultimately designing effective compensation methods. In this article, we design and perform such a systematic analysis and compensation for several hardware-specific distortion mechanisms.First and foremost, we identify and quantify the most important sources of model distortions. We then proceed to investigate their effect on network functionality. In order to cover a wide range of possible network dynamics, we have chosen three very different cortical network models to serve as benchmarks. In particular, these models implement several prototypical cortical paradigms of computation, relying on winner-take-all structures (attractor networks), precise spike timing correlations (synfire chains) or balanced activity (self-sustained asynchronous irregular states).For every emulated model, we define a set of functionality criteria, based on specific aspects of the network dynamics. This set should be complex enough to capture the characteristic network behavior, from a microscopic  to a mesoscopic level  and, where suitable, computational performance at a specific task. Most importantly, these criteria need to be precisely quantified, in order to facilitate an accurate comparison between software simulations and hardware emulations or between different simulation/emulation back-ends in general. The chosen functionality criteria should also be measured, if applicable, for various relevant realizations  of the considered network.Because multiple distortion mechanisms occur simultaneously in hardware emulations, it is often difficult, if not impossible, to understand the relationship between the observed effects  and their potential underlying causes. Therefore, we investigate the effects of individual distortion mechanisms by implementing them, separately, in software simulations. As before, we perform these analyses over a wide range of network realizations, since - as we will show later - these may strongly influence the effects of the examined mechanisms.After having established the relationship between structural distortions caused by hardware-specific factors and their consequences for network dynamics, we demonstrate various compensation techniques in order to restore the original network behavior.In the final stage, for each of the studied models, we simulate an implementation on the hardware back-end by running an appropriately configured executable system specification, which includes the full panoply of hardware-specific distortion mechanisms. Using the proposed compensation techniques, we then attempt to deal with all these effects simultaneously. The results from these experiments are then compared to results from software simulations, thus allowing a comprehensive assertion of the effectivity of our proposed compensation techniques, as well as of the capabilities and limitations of the neuromorphic emulation device.In section Neuromorphic testbench and investigated distortion mechanisms, we describe our testbench neuromorphic modeling platform with its most relevant components, as well as the essential layers of the operation workflow. We continue by explaining the causes of various network-level distortions that are expected to be common for similar mixed-signal neuromorphic devices. In the same section, we also introduce the executable system specification of the hardware, which we later use for experimental investigations.Section Hardware-induced distortions and compensation strategies contains the description of the three benchmark models. We start the section on each of the models with a short summary of all the relevant findings. We then describe its architecture and characteristic aspects of its dynamics which we later use as quality controls. We continue by discussing the effects of individual hardware-specific distortion mechanisms as observed in software simulations, propose various compensation strategies and investigate their efficacy in restoring the functionality of the network model in question. Subsequently, we apply these methods to large-scale neuromorphic emulations and examine the results.Finally, we summarize and discuss our findings in the Conclusions section.In this section we introduce the BrainScaleS neuromorphic wafer-scale hardware system and its executable system specification, henceforth called the ESS, as the testbench for our studies. The system's hardware and software components are only described on an abstract level, while highlighting the mechanisms responsible for distortions of the emulated networks. Finally, we identify the three most relevant causes of distortion as being synapse loss, synaptic weight noise and non-configurable axonal delays.synapse driver that forms the input for pre-synaptic pulses to the synapse array. Synapse drivers are evenly distributed to the left and right side of one synapse array (56 per side). A grid of horizontal and vertical buses enables the routing of spikes from neuron circuits to synapse drivers.On the HICANN chip  Here, synaptic weights are adjustable by a combination of analog and digital memories. The synaptic weight In contrast to neurons, where each parameter is fully configurable within the specified ranges, the Long-term learning is incorporated in every synapse through spike-timing-dependent plasticity (STDP) short-term plasticity mechanism (STP) decays over several hundreds of milliseconds. It is motivated by the phenomenological model by nth and (n+1)st afferent spike. In contrast to the original Tsodyks-Markram (TSO) mechanism, the hardware implementation does not allow simultaneous depression and facilitation In contrast to the long-term learning, the implemented All of the neuron and synapse parameters mentioned above are affected by fixed-pattern noise due to transistor-level mismatch in the manufacturing process. Additionally, the floating gate analog parameter storage reproduces the programmed voltage with a limited precision on each re-write. This leads to trial-to-trial variation for each experiment  has been measured as 120 ns for a recurrent connection on a HICANN. The additional time needed to transmit a pulse across the entire wafer is typically less than 100 ns off- and inter-wafer communication. All HICANNs on the wafer are connected to the surrounding system and to other wafers via 12 pulse communication subgroups (PCS). Each PCS consists of one FPGA (Field Programmable Gate Array) and 4 ASICs (Application Specific Integrated Circuits) that were designed for high-bandwidth pulse-event communication . Being the only communication link to/from the wafer, the off-wafer network also transports the configuration and control information for all the circuits on the wafer. As depicted in A hierarchical packet-based network provides the infrastructure for Although the off-wafer communication interface allows the interconnection of multiple wafers, we restrict our studies here to the use of a single wafer.The utilized software stack The network definition is accomplished by using PyNN The mapping process placement. Subsequently, a routing step is executed for establishing connections in between the placed components. During the final parameter transformation step, all parameters of the network components (neurons and synapses) are translated into hardware parameters. First, the model parameters are transformed to the voltage and time domain of the hardware, taking into account the acceleration and the voltage range of 0 V to 1.8 V calibration data is used to reduce mismatches between ideal neuromorphic circuitry behavior and real analogue signal hardware behavior.The first step of the mapping process is to allocate static structural neural network elements to particular neuromorphic components during the so-called The objective of the mapping process is to find a configuration of the hardware that best reproduces the neural network experiment specified in PyNN. The most relevant constraints are sketched in the following:limited number of inputs to one HICANN that becomes even more restricting, as there are only 224 synapse drivers , with the trade-off that the overall number of neurons is reduced. Still, each hardware synapse can not be used to implement a connection from an arbitrary neuron but only from a subset of neurons, namely the 64 source neurons whose pulses arrive at the corresponding synapse driver. For networks larger than 10 000 neurons it is the vers cf. , yieldinsynapses are lost during the mapping process; in other words, some synapses of a network defined in PyNN will be inexistent in the corresponding network emulated on the hardware. For an estimation of the amount of synapse loss, we first scaled all three benchmark models to sizes between 1000 and 100 000 neurons and mapped them onto the hardware using a simple, not optimized placement strategy. The results strongly depend on the size and the connectivity structure of the emulated network. In order to allow a comprehensive discussion within this study, we then used various placement strategies, sometimes optimizing the mapping by hand to minimize the synapse loss, or purposely using a wasteful allocation of resources to generate synapse loss.All of the above, as well as the limited number of on-wafer routing resources (section 2.1.2) make the mapping optimization an NP-hard problem. The used placement and routing algorithms, which improve upon the ones described in The ESS is a detailed simulation of the hardware platform specification of the hardware in the sense that its configuration space faithfully maps the possible interconnection topologies, parameter limits, parameter discretization and shared parameters. Being executable, the ESS also covers dynamic constraints, such as the consecutive processing of spikes which can lead to spike time jitter or spike loss. Variations in the analog circuits due to production variations are not simulated at transistor level but are rather artificially imposed on ideal hardware parameters. In this article, only synaptic weight noise is considered, as detailed in section 2.4. All of this allows to simultaneously capture the complex dynamic behavior of the hardware and comply with local bandwidth limitations, while allowing relatively quick simulations due to the high level of abstraction. Simulations on the ESS can be controlled using PyNN (Section 2.2), similarly to any other PyNN-compatible back-end. Both for the real hardware and for the ESS, the mapping process translates a PyNN network into a device configuration, which is then used as an input for the respective back-end. One particular advantage of the ESS is that it allows access to state variables which can otherwise not be read out from the real hardware, such as the logging of lost or jittered time events.The simulation encompasses a numerical solution of the equations that govern the hardware neuron and synapse dynamics  and a deReviewing the hardware and software components of the BrainScaleS wafer-scale system (Section 2.1 and 2.2) leaves us with a number of mechanisms that can affect or impede the emulation of neural network models:neuron and synapse models are cast into silicon and can not be altered after chip productionlimited ranges for neuron and synapse parametersdiscretized and shared parameterslimited number of neurons and synapsesrestricted connectivitysynapse loss due to non-optimal algorithms for NP-hard mappingparameter variations due to transistor level mismatch and limited re-write precisionnon-configurable pulse delays and jitterlimited bandwidth for stimulation and recording of spikesIt is clear that, for all of the above distortion mechanisms, it is possible to find a corner case where network dynamics are influenced strongly. However, a few of these effects stand out: on one hand, they are of such fundamental nature to mixed-signal VLSI that they are likely to play some role in any similar neuromorphic device; on the other hand, they are expected to influence any kind of emulated network to some extent. We have therefore directed our focus towards these particular effects, which we summarize in the following. In order to allow general assessments, we investigate various magnitudes of these effects, also beyond the values we expect for our particular hardware implementation.While some network architectures employ relatively simple neuron and synapse models for analytical and/or numerical tractability, others rely on more complex components in order to remain more faithful to their biological archetypes. Such models may not allow a straightforward translation to those available on the hardware, requiring a certain amount of fitting. In our particular case, we search for parameters to Above a certain network size or density, the mapping process may not be able to find enough hardware resources to realize every single synapse. We use the term “synapse loss” to describe this process, which causes a certain portion of synaptic connections to be lost after mapping. In a first stage, we model synapse loss as homogeneous, i.e., each synapse is deleted with a fixed probability between 0 and 50%. To ease the analysis of distortions, we make an exception for synapses that mediate external input, since, in principle, they can be prioritized in the mapping process such that the probability of losing them practically vanishes. Ultimately however, the compensation methods designed for homogeneous synapse loss are validated against a concrete mapping scenario.Axonal delays on the wafer are not configurable and depend predominantly on the processing speed of digital spikes within one HICANN, but also on the physical distance of the neurons on the wafer. In all simulations, we assume a constant delay of 1.5 ms for all synaptic connections in the network, which represents an average of the expected delays when running the hardware with a speedup of As described in section 2.1.1, the variation of synaptic weights is assumed to be the most significant source of parameter variation within the network. This is due to the coarser discretization  as well as the large number of available synapses, which prohibits the storage of calibration data for each individual synapse. The quality of the calibration only depends on the available time and number of parameter settings, while the trial-to-trial variability and the limited setting resolution remains. To restrict the parameter space of the following investigations, only the synaptic weights are assumed to be affected by noise. In both software and ESS simulations, we model this effect by drawing synaptic weights from a Gaussian centered on the target synaptic weight with a standard-deviation-to-mean-ratio between 0 and 50%. Occasionally, this leads to excitatory synapses becoming inhibitory and vice versa, which can not happen on the hardware. Such weights are clipped to zero. Note that this effectively leads to an increase of the mean of the distribution, which however can be neglected, e.g., for 50% noise the mean is increased by 0.425%. For ESS simulations we assume a synaptic weight noise of 20%, as test measurements on the hardware indicate that the noise level can not be reduced to below this number.It has to be noted that the mechanism of distortion plays a role in the applicability of the compensation mechanisms. The iterative compensation in section 3.3.6 is only applicable when the dominant distortion mechanism is fixed-pattern noise. The other compensation methods, which do not rely on any kind of knowledge of the fixed-pattern distribution, function independently of the distortion mode.In the following, we analyze the effects of hardware-specific distortion mechanisms on a set of neuronal network models and propose adequate compensation mechanisms for restoring the original network dynamics. The aim of these studies is twofold. On one hand, we propose a generic workflow which can be applied for different neural network models regardless of the neuromorphic substrate, assuming it posesses a certain degree of configurability . On the In order to allow a comprehensive overview, the set of benchmark experiments is required to cover a broad range of possible network architectures, parameters and function modi. To this end, we have chosen three very different network models, each of which highlights crucial aspects of the biology-to-hardware mapping procedure and poses unique challenges for the hardware implementation. In order to facilitate the comparison between simulations of the original model and their hardware implementation, all experimental setups were implemented in PyNN, running the same set of instructions on either simulation back-end.For each of our benchmark models we define a set of specific well-quantifiable functionality criteria. These criteria are measured in software simulations of the ideal, i.e., undistorted network, which is then further referenced as the “original”.Assuming that the broad range of hardware-specific distortion mechanisms affects various network parameters, their impact on these measures are investigated in software simulations and various changes to the model structure are proposed in order to recover the original functionality. The feasibility of these compensation methods is then studied for the BrainScaleS neuromorphic platform with the help of the ESS described in Section 2.3.All software simulations were performed with NEST As our first benchmark, we have chosen an attractor network model of the cerebral cortex which exihbits characterisic and well-quantifiable dynamics, both at the single-cell level (membrane voltage UP and DOWN states) and for entire populations . For this model, the mapping to the hardware was particularly challenging, due to the complex neuron and synapse models required by the original architecture on the one hand, as well as its dense connectivity on the other. In particular, we observed that the shape of synaptic conductances strongly affects the duration of the attractor states. As expected for a model with relatively large populations as functional units, it exhibits a pronounced robustness to synaptic weight noise. Homogeneous synapse loss, on the other hand, has a direct impact on single-cell dynamics, resulting in significant deviations from the expected high-level functionality, such as the attenuation of attentional blink. As a compensation for synapse loss, we suggest two methods: increasing the weights of the remaining synapses in order to maintain the total average synaptic conductance and reducing the size of certain populations and thereby decreasing the total number of required synapses. After mapping to the hardware substrate, synapse loss is not homogeneous, due to the different connectivity patterns of the three neuron types required by the model. However, we were able to apply a population-wise version of the suggested compensation methods and demonstrate their effectiveness in recovering the previously defined target functionality measures.As described in The functional units of the network, the MCs, are connected in global, distributed patterns containing a set of MCs in the network . Here thPattern rivalry results from competition between attractors mediated by short and long-range connections via inhibitory interneurons. Each HC can be viewed as a soft winner-take-all (WTA) module which normalizes activity among its constituent MCs When a pattern receives enough excitation, its PYR cells enter a state reminiscent of a so-called local UP-state The inputs to the layer 2/3 PYR cells arrive from the cortical layer 4, which is represented by 5 cells per MC. The layer 4 cells project onto the layer 2/3 PYR cells and can be selectively activated by external Poisson spike trains. Additionally, the network receives unspecific input representing activity in various connected cortical areas outside the modeled patch. This input is modeled as diffuse noise and generates a background activity of several Hz.More details on the model architecture, as well as neuron and synapse parameters, can be found in A) or voltage star plots , since the scaling rules ensure a constant average fan-in for each neuron type. Conversely, this makes When an attractor becomes active, it remains that way for a characteristic dwell time H). One can observe that the competition time is a monotonically increasing function of both We describe the periods between active attractors as competition phases and the time spent therein as the total competition time. The competition time varies strongly depending on the network size (J). Two independent mechanisms are the cause of this decay: neuron adaptation and synaptic depression. The characteristic time course of the spike rate depends only weakly on the size of the network.When an attractor becomes active, the average spike rate of its constituent PYR cells rises sharply and then decays slowly until the attractor becomes inactive again (K). While inactive or inhibited by other active attractors, PYR cells are in a DOWN state, with low average membrane potential and almost no spiking at all (L). In addition to these characteristic states, the average PYR membrane potential exhibits oscillations with a period close to As described in Section 3.1.1, PYR cells within active attractors enter a so-called local UP state, with an increased average membrane potential and an elevated firing rate (M). The frequency of these oscillations are independent of size and rather depend on excitation levels in the network An essential emerging feature of this model are oscillations of the instantaneous PYR spike rate in the gamma band within active attractors (N). Similarly, the attentional blink phenomenon can also be quantified by the successful activation rate of an attractor as a function of the number of stimulated MCs if it is preceded by the activation of some other attractor with a time lag of O). For small Apart from these statistical measures, two behavioral properties are essential for defining the functionality of the network: the pattern completion and attentional blink mentioned above. The pattern completion ability of the network can be described as the successful activation probability of individual patterns as a function of the number of stimulated MCs  were therefore determined by fitting the membrane potential time course under stimulation by a step current which was not strong enough to elicit spikes.Adaptation is an essential feature of both the PYR and the RSNP cells in the original model, where it is generated by voltage-dependent Synapses. We have performed an initial estimation of synaptic weights and time constants by fitting the membrane potential time course of the corresponding neurons in a subthreshold regime. However, two important differences remain between the synapses in the original model and the ones available on our hardware.B). While, in priniciple, the HICANN supports several excitatory time constants per neuron (Section 2.1.1), the PyNN API as well as the mapping process support only one excitatory time constant per neuron. With this limitation the PSP shape can not be precisely reproduced.In the original model, PYR-PYR and PYR-RSNP synapses contain two types of neurotransmitters: Kainate/AMPA and NMDA . Star plots exhibit the same overall traits, with well-defined attractors, characterized by state space trajectories situated close to the axes and low trajectory velocities within attractors (E). Attractor dwell times remain relatively stable for different network sizes, while the competition times increase along with the network size (G and I). The average value of dwell times, however, lies significantly lower than in the original (C). The reason for this lies mainly in the shape of EPSPs: the long EPSP tails enabled by the large NMDA time constants in the original model caused a higher average membrane potential, thereby prolonging the activity of PYR cells.When subject to diffuse background noise only, the default size network clearly exhibits its characteristic spontanous attractors (K and L). Before and after active attractors, the dampened oscillations described in section 3.1.2 can be observed. In the adapted model, attenuation is stronger due to a higher coefficient of variation of the dwell times , with a small difference in amplitude, which can be attributed to the difference in EPSP shapes discussed earlier. Both low-frequency switches between attractors  and high-frequency gamma oscillations arising from synchronous PYR firing (with a peak around 25 Hz) can be clearly seen in a power spectrum of the PYR firing rate (M).Average PYR firing rates within active attractors have very similar time courses (N). Attentional blink follows the same qualitative pattern , although with a slightly more pronounced dominance of the first activated attractor in the case of the adapted network, which happens due to the slightly higher firing rates discussed above.Pattern completion occurs similarly early, with a steep rise and nearly 100% success rate starting at 25% of stimulated MCs per attractor .R, S). Additionally, since all connections become weaker, the variance of the membrane potential becomes smaller, as observed in the corresponding star plots as well (E). The weaker connections also decrease the self-excitation of active attractors while decreasing the inhibition of inactive ones, thereby leading to shorter attractor dwell times (P). Somewhat surprisingly, the maximum average PYR firing rate in active attractors remains almost unchanged when subjected to synapse loss. However, the temporal evolution of the PYR firing rate changes significantly (Q).While average excitatory conductances are only affected linearly by synaptic loss, inhibitory conductances feel a compound effect of synapse loss, as it affects both afferent and efferent connections of inhibitory interneurons. Therefore, synapse loss has a stronger effect on inhibition, leading to a net increase in the average PYR membrane potential (T). Due to the decreased stability of individual attractors discussed above, rival attractors are easier to excite, thereby significantly suppressing the attentional blink phenomenon (U).The pattern completion ability of the network suffers particularly in the region of weak stimuli, due to weaker internal excitation of individual attractors. The probability of triggering a partially stimulated pattern can drop by more than 50%  by changing the size of the PYR cell populations. For this approach, however, the standard scaling rules . Once this limit is reached, the number of noise inputs per PYR cell can still be increased even further if PYR cells are allowed to share noise sources. Given a total number of available Poisson sources T, U) and appear mainly due to the affected RSNPIn our example . Their higher firing rate in turn causes shorter attractor dwell times, due to their spike frequency adaptation mechanism . During an active attractor, the elevated firing rate of its constituent PYR cells causes a higher firing rate of the inhibitory interneurons belonging to all other attractors. This, in turn, leads to a lower membrane potential for PYR cells during inactive periods of their parent attractor . As these effects are not fundamentally disruptive and also difficult to counter without significantly changing other functional characteristics of the network, we chose not to design a compensation strategy for this distortion mechanism in the L2/3 network.Fixed delays, on the other hand, promote synchronicity, thereby inducing subtle changes to the network dynamics . The synIn a final step, we emulate the L2/3 model on the ESS (Section 2.3), and compensate simultaneously for all of the effects discussed above. We first investigate how much synapse loss to expect for different network sizes, and then realize the network at two different scales in order to investigate all of the chosen functionality criteria. The default network , which can be mainly attributed to the loss of over 32% of the background synpases. To recover the original network behavior, we first increased the number of background neurons per cell from 9 to 50 to compensate for synaptic weight noise, and also scaled the weights by G), which can be ascribed to the non-configurable delays (Section 3.1.6).strength . Also ex values  , followi network . The attLarge-scale model. The ability of the network to perform pattern completion and exhibit pattern rivalry was tested on the ESS for the large-scale model with 25 HCs and 25 MCs per HC. From the start, we use a background pool with 5000 Poisson sources and 100 sources per neuron to model the diffusive background noise, as used for the compensation of synaptic weight noise (Section 3.1.5). As with the small-scale network, the synapse loss of 17.9% shows significant heterogeneity (K and L (red curves). In both cases the network functionality is clearly impaired. In particular, the ability of an active pattern to suppress other patterns is noticeably detoriated, which can be traced back to the loss of 37% of PYR-RNSP connections.ogeneity , and affIn order to restore the functionality of the network we used a two-fold approach: First, we attempted to reduce the binary loss of PYR-RSNP projections by reducing the number of PYR cells per MC from 30 to 20, which decreases the total number of neurons in the network, as well as the number of potential pre-synaptic neurons per RNSP cell. The synapse loss was thereby reduced to 28.8% for PYR-RSNP projections and was eliminated almost completely for all other projections . SecondlAfter application of these compensation mechanisms, we were able to effectively restore the original functionality of the network. Both pattern completion and attentional blink can be clearly observed. The small remaining deviations from the default model can be attributed to the inhomogeneity of the synapse loss and the fixed delays on the wafer.synfire chain from here on Our second benchmark network is a model of a series of consecutive neuron groups with feed-forward inhibition, called Feed-forward networks with a convergent-divergent connection scheme provide an ideal substrate for the investigation of activity transport. Insights have been gained regarding the influence of network characteristics on its response to different types of stimulus The feed-forward network comprises a series of successive neuron groups, each group containing one excitatory and one inhibitory population. The excitatory population consists of 100 regular-spiking (RS), the inhibitory of 25 fast-spiking (FS) cells. Both cell types are modeled as LIF neurons with exponentially shaped synaptic conductance without adaptation, as described in section 2.1.1. Both RS and FS neurons are parameterized using identical values (Table S3.1 in A). There are no recurrent connections within the RS or FS populations. In the original publication Each excitatory population projects to both populations of the consecutive group while the inhibitory population projects to the excitatory population in its local group A. Therepulse packet was used as a stimulus, whereby each of the 100 spike sources emitted ith group of the chain, referring to the temporal pulse width and number of spike pulses per neuron, respectively.The initial synchronous stimulus pulse is emitted by a population of spike sources, which has the same size and connection properties as a single RS population within the network. A temporally localized B). The propagation is quantified by applying initial stimuli of varying strength A).The functionality of the feed-forward network is assessed by examining the propagation of a synchronous pulse after the stimulus is applied to the first group in the chain B. The pe  space  is attraut e.g., A.separatrix from here on. The differentiation between extinguished and successful propagation is defined by The network behavior is characterized by the separating line between successful and extinguished propagation in the state space The background stimulus is adjusted such that the spontaneous firing rate in the network is below A shows a single experiment with synapse loss of 37.5%, contrasting with the undistorted case (A). Above a certain value of synapse loss, the signal fails to propagate to the last group. As shown in C and E for one stimulus parameter set, successful propagation stops at a synapse loss value between 30% and 40%. The pulse width increases with rising synapse loss due to the increasing variation of synaptic conductance for individual neurons (E). The effect is reversed by increasing all synaptic weights in the network by a factor of B, D) and the pulse width increase is shifted to larger values of synapse loss (F). The distortion mechanism has only a minor effect on the G). However, the location of the separatrix at Homogeneous synapse loss affects the strength of excitatory and inhibitory projections equally on average. Additionally, the number of incoming spikes seen by a single neuron varies as synapses are removed probabilistically, in contrast to the undistorted model with a fixed number of incoming connections for each neuron type (Table S3.2 in ted case A. AboveB); additionally, the border between the regions of stable and extinguished propagations becomes less sharp (A). This is caused by two effects: Varying strength of the background stimulus, and varying strength of the synaptic connections within the network. The first effect is significant because the background stimulus to each neuron is provided through a single synapse. Thus, the effective resting potential of each neuron is shifted, significantly changing its excitability and, in some cases, inducing spontaneous activity. One possibility of countering this effect is to utilize several synapses for background stimulus thereby averaging out the effect of individual strong or weak synapses, as has been done in the case of the L2/3 model in section 3.1.5. Here, a different method was employed: The resting potential The effect of synaptic weight noise is shown in C shows that removing the effect of background stimulus noise alone is sufficient to counteract synaptic noise values of up to 50%.The effect of synaptic weight noise within the network itself is less significant compared to its impact on the noise stimulus. A shows the effect of varying axonal delays between the inhibitory and excitatory population of a single synfire group. As was shown in B shows the result of the compensation for 1.5 ms local inhibition delay. For both values of axonal delay, the location of the separatrix can be controlled by changing the weight of inhibition. However, its shape differs from the delay-induced case because of the modified delay mechanism of inhibition. Reduction of the weight beyond a certain point is not possible, as balanced inhibition is required for network functionality At last, we simulate the synfire chain with the ESS and compensate simultaneously for all the causes of distortions addressed above. Before running ESS simulations, we have verified the compatibility of the proposed compensation strategies for different distortion mechanisms in software simulations dealing with the simultaneous incidence of synaptic weight noise, synapse loss and non-configurable axonal delays . For this purpose we developed network scaling rules that depend on the number and the size of the synfire groups , we reduce the total number of background sources from 750 to 192 and let each neuron receive input from 8 sources, while decreasing the Poisson rate by a factor of 8, using the same mechanism as for the compensation of synaptic weight noise in the L2/3 model (cf. section 3.1.5). For the same reason, the network was emulated with a speedup factor of 5000 compared to biological real-time, whereby the effective bandwidth for stimulation and recording is doubled with respect to the normal operation with a speedup of 10 000. As seen before, no synapse loss occurs for small networks. However, as discussed for the L2/3 model in Section 3.1.7, one can consider situations where only a small part of the wafer is available for experiments, or where some neurons or synaptic elements are defective or missing a calibration. Therefore, in order to generate synapse loss in the feed-forward network, we limited the network to only 8 out of 48 reticles of the wafer and furthermore declare half of the synapse drivers as not available. This resulted in a total synapse loss of 27.4%. As with the L2/3 model, the synapse loss was not homogeneous but depended strongly on the projection type (ion type .B) shows no stable point of propagation. This can be mainly attributed to the small and non-configurable axonal delays which are in the range of 0.6 ms to 1.1 ms for the chosen speedup factor of 5000.We simulated the synfire chain with default neuron and synapse parameters on the ESS with 20% synaptic weight noise and the above synapse loss. The te space B shows C).In order to recover the original behavior, we applied the previously developed compensation methods described in Sections 3.2.3 to 3.2.5. Synapse loss was compensated separately for each projection type using D showing the D. In that particular case the large majority of input spikes were lost in the off-wafer communication network so that they did not even reach the first synfire group. We remark that this effect only appeared for However, we encountered some abnormalities as can be seen in Our third benchmark is a cortically inspired network with random, distance-dependent connectivity which displays self-sustained asynchronous and irregular firing (short: “AI network”). We define functionality measures on several levels of abstraction, starting from single network observables such as the network firing rate, the correlation coefficient and the coefficient of variation, the properties of the power spectrum of the network activity, up to global behavior such as the dependence of network dynamics on the internal synaptic weights Self-sustained states in spiking neural networks are known to be exquisitely sensitive to the correlation dynamics generated by recurrent activity The self-sustained regime constitutes an attractor of a dynamical system −1Here, we used a reduced model based on that published in The global functionality criterion for this network consists of the ability to sustain activity in an asynchronous and irregular activity regime. The activity is considered self-sustained upon persistence to the end of the chosen simulation period. The activity characteristics are quantified for the pyramidal cells using the mean and variance of the firing rates, the irregularity of individual spike trains (A) shows the region in the (G).These criteria were evaluated for a range of excitatory and inhibitory synaptic weights B), as all neurons have identical numbers of afferent synapses with identical weights in each network realization. In addition to the parameter space plots in the top row of The coefficient of variation of the firing rates across neurons (C) and is mainly determined by the network firing rate: the H). Over the entire parameter space, the spike trains of the pyramidal cells are only weakly correlated, with a CC between 0.01 and 0.03.The activity is irregular (F). As a comparison for further studies we follow E) and as a function of the firing rate (J): The position of the power spectrum peak frequency  state in  marking states that survived with 50% synaptic weight noise but not in the undistorted case. The firing rate increases with the degree of noise (A): the change is the stronger the lower C). Synaptic weight noise leads to an increase of the variation of firing rates (B). The E). Furthermore, weight noise introduces randomness into the network, thereby reducing synchrony: The pairwise correlation between neurons decreases linearly with the amount of weight noise (F). The power spectrum of the global activity is not affected by this distortion mechanism.The effects of synaptic weight noise between 10% and 50% (cf. Section 2.4) on the AI network are shown in C). The firing rate increases with synapse loss (A): Compared to the change caused by synaptic weight noise, however, the effect is much stronger for synapse loss. The same holds for the variance of the firing rates across the pyramidal neurons, which again increases with synapse loss, as can be seen in (B). Note that the E and F). Synapse loss shows no effect on the power spectrum of global pyramidal activity.Synapse loss has a similar influence on the network behavior as synaptic weight noise: The hardware-induced distortions on the AI network analyzed in the previous sections leave two major criteria that need to be recovered: The population firing rate and the variation of firing rates across the population. We consider the other effects C. The results also show that the variance of the firing rates across neurons grows with rising synapse loss due to the increasing difference in connectivity within the networks. An extension of the mean-field-based compensation to this kind of inhomogeneous connectivity would be impractical, as it requires knowledge of the actual network realization (which is available only after the mapping step) and the measurement of A for all occuring counts of presynaptic inhibitory and excitatory neurons. Thus, a different method is considered in section 3.3.6.The resulting firing rate with and without compensation is shown in In conclusion, this method can be applied when the actual synapse loss value and the mean response function of a single neuron is known. It only depends on the single neuron response properties; the amount of synapse loss has to be known a priori, but not the complete network dynamics. The method depends on the ability to modify synaptic delays according to the scaling rule. However, for the given network, this scaling has only a minimal effect on the mean firing rate.Iterative compensation. The iterative compensation method aims at reducing two distortion effects: the change of the mean firing rate of the pyramidal neurons and its variance across neurons, which are both apparent for synapse loss and synaptic weight noise. It relies on the controlability of the hardware neuron parameters allowing to fine tune the AdEx parameters for every individual neuron (Section 2.1.1). The iterative compensation functions as follows: We start with the results of the reference and the distorted network. From the reference simulation we extract the target mean rate Synaptic weight noise. In order to verify the iterative compensation strategy we applied it to the distorted parameter space with 50% synaptic weight noise. Note that, here and in section 3.3.8, weight noise was implemented persistently, being always the same in all iterations, representing the case where fixed-pattern noise, and not trial-to-trial variability, determines the synaptic weight noise (cf. section 2.4). Accordingly, the following findings are not applicable to the opposite case. The results of the iterative compensation are shown in D and H. The region of sustained activity in the (Synapse loss. The results of the application of the iterative compensation strategy to the (D and H), displaying the relative difference of mean and variance of firing rates. The compensation was not as effective as for synaptic weight noise: Some states with a low base firing rate were unstable (marked with a cross), i.e. the network did not survive until the end of simulation. As before, the mean and variance of firing rates can be successfully restored for low and medium base firing rates. Again, for high firing rates, the iterative compensation only performed moderately (upper left regions in the parameter spaces D and H). The other criteria show the same behavior as in the weight noise compensation, i.e. the peak frequency and Conclusion. We conclude that the iterative compensation of distorted networks works for both synapse loss and fixed-pattern synaptic weight noise. The compensation also works when both are present at the same time, see section S4.4.3 in In a last step the iterative compensation method designed for the AI network was tested in ESS simulations. Like for the other two models we forced distortions to test the developed compensation strategies. Therefore, we scaled up the network such that a significant fraction of synapses was lost during the mapping process. This large-scale network was then emulated on the ESS and compared to the undistorted reference simulation with NEST. Afterwards, we applied the compensation strategy developed in the previous section to restore the original behavior of the AI network.Synapse loss. Mapping such homogeneous networks that lack any modularity represents the worst-case scenario for the mapping process, as they have little room for optimization. In A the relative synapse loss is plotted for various network sizes using the scaling method described in section S4.1.2 of Large-scale network. In order to produce a demanding scenario, we scaled the model to a size of 22 445 neurons  list might include mismatch of neuron and synapse parameters, shared parameter values  or limited parameter programming resolution. These mechanisms are highly back-end-specific and therefore difficult to generalize. However, although they are likely to pose individual challenges by themselves, some of their ultimate effects on the target network functionality can be alleviated with the compensation strategies proposed here.Our proposed strategies aim at neuromorphic implementations that compete in terms of network functionality with conventional computers but offer major potential advantages in terms of power comsumption, simulation speed and fault tolerance of the used hardware components. If implemented successfully, such neuromorphic systems would serve as fast and efficient simulation engines for computational neuroscience. Their potential advantages would then more than make up for the overhead imposed by the requirement of compensation.From this point of view, hardware-induced distortions are considered a nuisance, as they hinder precise and reproducible computation. In an alternative approach, one might consider the performance of the system itself at some computational task as the “fitness function” to be maximized. In this context, some particular architecture of an embedded model, together with an associated target behavior, would then become less relevant. Instead, one would design the network structure specifically for the neuromorphic substrate or include training algorithms that are suitable for such an inherently imperfect back-end. The use of particular, “ideal” software models as benchmarks might then given up altogether in favor of a more hardware-oriented, stand-alone approach. Here, too, the proposed compensation strategies can be actively embedded in the design of the models or their training algorithms.The hardware architecture used for our studies is, indeed, suited for both approaches. It will be an important aspect of future research with neuromorphic systems to develop procedures that tolerate or even actively embrace the temporal and spatial imperfections inherent to all electronic circuits. These questions need to be addressed by both model and hardware developers, in a common effort to determine which architectural aspects are important for the studied computational problems, both from a biological and a machine learning perspective.Appendix S1Neuromorphic hardware. Details about the short-term plasticity mechanism, parameter ranges, and parameter variation measurements of the BrainScales neuromorphic hardware system.(PDF)Click here for additional data file.Appendix S2Cortical layer 2/3 attractor memory. Methods, model parameters, and detailed simulation results of the Layer 2/3 model.(PDF)Click here for additional data file.Appendix S3Synfire chain with feed-forward inhibition. Model parameters and additional simulations of the synfire chain network.(PDF)Click here for additional data file.Appendix S4Self-sustained asynchronous irregular activity. Methods, model parameters, and further simulations of the AI network.(PDF)Click here for additional data file.


Contaminant transport in subsurface water is the major pathway for contamination spread from contaminated sites to groundwater supplies, to remediate a contaminated site. The aim of this paper was to set up the groundwater contaminant transport model for the Wang-Tien landfill site, in southwestern Taiwan, which exhibits high contamination of soil and groundwater and therefore represents a potential threat for the adjacent Hsu-Hsian Creek. Groundwater Modeling System software, which is the most sophisticated groundwater modeling tool available today, was used to numerically model groundwater flow and contaminant transport. In the simulation, the total mass of pollutants in the aquifer increased by an average of 72% (65% for ammonium nitrogen and 79% for chloride) after 10 years. The simulation produced a plume of contaminated groundwater that extends 80 m in length and 20 m in depth northeastward from the landfill site. Although the results show that the concentrations of ammonium nitrogen and chlorides in most parts are low, they are 3.84 and 467 mg/L, respectively, in the adjacent Hsu-Hsian Creek. Among all these, wastewater leakage affects the surrounding environment the most, especially the groundwater quality because wastewater leakage consists of high concentrations of organic compounds, heavy metal ions and toxic hazards. Recently, a large number of landfill-caused groundwater pollution cases have been reported around the world .The contaminants of municipal solid waste derived from landfill leaks into groundwater aquifers because of rainfalls, and by groundwater flow they spread into the adjacent river system and pollute the ecosystem. However, this mechanism does not stop even after landfills have stopped receiving waste. Therefore, it is necessary to keep investigating and monitoring the surroundings of decommissioned landfill sites.et al. [et al. [et al. [Today, many specialized computer software packages have been created and used to solve contaminant transport problems in groundwater system. However, Groundwater Modeling System (GMS), is the most powerful software package using the modular finite-difference flow model (MODFLOW), the particle-tracking post-processing model (MODPATH), the modular three-dimensional transport model (MT3DMS), the multi-species reactive transport model (RT3D), the finite element groundwater model (FEMWATER), the two-dimensional finite element model (SPEED2D), the sequential electron acceptor model (SEAM3D), the multi-phase flow transport model (UTCHEM), the general purpose parameter estimation (PEST), the various transport tools (UCODE) and the transition probability geostatistic software (T-PROGS) to work out groundwater contamination transport and the interaction between surface water and groundwater. The purpose of GMS is to predict the spreading of contaminant concentration by inputting initial conditions of hydraulic head, groundwater flow direction and the concentration of contaminant. Many studies have shown that the contamination transport in groundwater aquifers by using the GMS software package at the present time. . As GMSetc. on landfill site were used in GMS to simulate and predict the contamination transport status. Decommissioned landfills are generally not protected the leachate spreading to underlying aquifers, which are required to be managed and controlled to avoid negative impact on the environment.In this paper, in order to discuss the possibility of the land reusing in the decommissioned landfill site, Wang-Tien, the data of geology, hydraulic head, and contamination concentrations, Numerical method can effectively solve not only groundwater flow problem, but also contaminant transport of groundwater. By the rapid development of calculator technology, groundwater model has been applied to groundwater resource assessments, predictions and managements. GMS is an advanced and based on concept model groundwater aquifers simulation software, which provides lots of methodologies to establish groundwater flow numerical model. The following is the simulation process with explanations for each of the steps of this study.The procedure for applying to a groundwater flow model includes the following steps shown in .in-situ information collection and measurement are necessary to collect information such as the annual rainfall and the annual evaporation from the Central Weather Bureau, Taiwan [Step 1 Data Collection: During this step, , Taiwan , and theStep 2 Construction of a Conceptual Model: The conceptual model incorporates the information in Step 1 to establish a groundwater flow model which can be used to test and verify.in-situ observations to be optimized.Step 3 Simulation and Calibration of Groundwater Flow Model: To perform the MODFLOW package to simulate groundwater flow on each month, then to calibrate the groundwater flow directions and the hydraulic heads until the simulation matches the in-situ observations to a reasonable degree.Step 4 Simulation and Calibration of the Contaminant Transport Model: After the groundwater flow model is calibrated, the contaminant source locations and contaminant concentration values are input into the model. Then the MT3DMS package is run to simulate the transport of contaminants in groundwater aquifer and subsequently to calibrate contaminant concentrations until the simulation matches Step 5 Predictive Simulations: After calibration, the model can be used to predict future groundwater flow and contaminant transport. The model may be used to estimate various remediation alternatives, such as risk assessment.The partial differential equations, describing the groundwater flow, velocity and contaminant transport, can be expressed as follows . In ordIn the numerical simulation, a correct description of hydrological and geological conditions at the study site is necessary which can be used to numerically model the groundwater flow and contaminant transport processes. In the conceptual model in which data describing site conditions are assembled in a systematic approach to indicate groundwater flow and simulate contaminant transport processes of the ammonium nitrogen and chlorides at the study site.The numerical model of Wang-Tien landfill site was constructed by using package MODFLOW in this study. A rectilinear grid pattern was used to divide the model domain both horizontally and vertically into rectangular cells in package MODFLOW to calculate groundwater table conditions in each cell of the model domain.To avoid an inaccurate simulation from the boundary effect, it is necessary to construct a larger grid model with boundaries far enough from the study site. Thus, the model domain boundary is extended about 750 m north-south and about 1000 m east-west and the model domain is discretized into a grid of 75 rows and 100 columns. Each layer in the numerical model contains 7500 grid cells as shown in The northern (downriver boundaries) and southern boundaries (upriver boundaries) of the model domain were represented as no flow boundaries, where the assumption that flow in these outlying areas was generally east-westward and parallel to the Hsu-Hsian creek, due to recharge in high elevation areas and discharge to Hsu-Hsian creek and the constant head boundaries (CHB) were applied to the eastern and western boundaries of the model domain. The values of the boundary hydraulic head, determined from the topographic survey data, were assigned to be a constant with the Hsu-Hsian creek elevations.in-situ measurements from all monitoring boreholes , above the groundwater flow was subjected to a transient recharge. The value of transient recharge was based on the variable data of the monthly rainfall in 2013 . In the numerical domain, the recharge surface was divided into 30 partial recharge zones, and each borehole was located at different zones as shown in 3/day with the ammonium nitrogen concentration of 35 mg/L and the chloride concentration of 4240 mg/L for contaminant transport simulation. The dispersivity can be expressed as follows empirical equation :(5)αL=0in-situ conditions, we defined the one year groundwater flow model according to varying hydraulic heads with the monthly measurement data from 2013. There are 12 serial simulations indicating the groundwater flow situation for each month in this model, and the groundwater flow of those serial simulations is continuously via repeatedly failing the calibration process. Furthermore, the one year groundwater flow model was recorded and it can use to simulate and predict the contaminant transport in this study.In order to obtain the simulation result more in line with the The calibration of the groundwater flow model, constructed in the most time-consuming effort within the modeling procedure, is a key to its applicability. The calibration procedure could be done manually by adjusting input variables to find an optimal scheme which is the closet agreement between the simulation and measurement. In this study a threshold value for the hydraulic head was equal to 2 m. The hydraulic head simulations were considered than ±2 m and the adjusting input variables were hydraulic conductivities and recharge rates changing in each partial recharge zone during the calibration process.in-situ measurement data. A residual is the difference between the value of measured and simulated. During the calibration process, several attempts have to be done to minimize these differences. The perfect calibrations were shown from Comparison between The MT3DMS simulation was incorporated into the transient MODFLOW simulation model by assuming the ammonium nitrogen concentration of 35 mg/L and the chloride concentration of 4240 mg/L injected according to the one year groundwater flow model described in the transient MODFLOW simulation above. The ammonium nitrogen and chloride concentrations detected through sampling and analysis at monitoring boreholes in November 2013 listed in Manual adjustment and inverse modeling were also used in contaminant transport model calibration. The perfect calibrations were shown in After calibration, the contaminant transport model was used to predict potential plume movement 10 years into the future. In the simulations, the spreading concentration contour of ammonium nitrogen and chlorides were shown in The predicting results also showed that the contamination plume has moved in the direction of the northeast (into the creek which is located in the east of the map) instead of moving along with groundwater. The results also gave a plume of contaminated groundwater that extends 80 m in length and 20 m in depth northeastward from the landfill site. The results concentration of ammonium nitrogen and chlorides in the Hsu-Hsian Creek of the Wang-Tien landfill is 467 mg/L and 3.84 mg/L, respectively, when the Monitoring Standard Value for Taiwan declared by Environmental Protection Administration (EPA in Taiwan) is 250 mg/L and 0.05 mg/L, respectively for Class A, which means that the Hsu-Hsian Creek is highly polluted and not suitable for drinking.A great number of outputs can be exported into text files. Among the text file data, all concentrations of contaminants in the aquifer for each year are available. According to the result, it shows that contaminant transport is a slow process because of sorption of pollutants in the soil layer. In 10 years, the total concentration of pollutants in the aquifer has increased by an average of 72%, including 65% of the ammonium nitrogen and 79% of the chloride. Since the Hsu-Hsian Creek is the only sink for surface flow, the entire injection of increasing concentrations from the Wang-Tien landfill site corresponds to the contaminant discharge to the Hsu-Hsian Creek. The contaminant transport toward the Hsu-Hsian Creek could be calculated by dividing the subjoint mass of contaminants with time. Both contaminant properties of the ammonium nitrogen and chlorides are entirely different, so the results of concentration need to be normalized to compare the increasing concentration difference with both. GMS, a numerical calculator for simulation of groundwater flow and contaminant transport, could be used to deal with complex groundwater flow problems for contaminant transport predictions which is more convenient and suitable than analytical calculations.In order to propose remedial solutions for a decommissioned landfill site, Wang-Tien, this study used GMS to simulate groundwater flow by incorporating the information of geology, hydrology and rainfall of the study site, and then combined contaminant transport modeling to predict ammonium nitrogen and chlorides of the site for 10 years after spreading.According to the results of contaminant transport modeling, not only is the Wang-Tien landfill site heavily polluted both in the soil and groundwater, but also the adjacent Hsu-Hsian Creek where its concentration of chlorides is 467 mg/L  and ammonium nitrogen is 3.84 mg/L .When pollutants in groundwater aquifers spread into surface water systems, the contaminant transport velocity changes its speed from slow to fast, which will spread the pollutants more quickly from the adjacent river to the whole water system and would cause irreversible damage. In the current situation, nothing has been done to the Wang-Tien landfill site where the pollution may be prolonged for centuries. In order to stop the spread of pollution and the ecosystem damage, it is obviously necessary to take some remedial actions, such as removing the whole landfill.


We design and experimentally demonstrate a broadband yet compact acoustic diode (AD) by using an acoustic nonlinear material and a pair of gain and lossy materials. Due to the capabilities of maintaining the original frequency and high forward transmission while blocking backscattered wave, our design is closer to the desired features of a perfect AD and is promising to play the essential diode-like role in realistic acoustic systems, such as ultrasound imaging, noise control and nondestructive testing. Furthermore, our design enables improving the sensitivity and the robustness of device simultaneously by tailoring an individual structural parameter. We envision our design will take a significant step towards the realization of applicable acoustic one-way devices, and inspire the research of non-reciprocal wave manipulation in other fields. Our proposed model has a sandwich structure that consists of a pair of acoustic gain and lossy media and another acoustical nonlinear material inserted in between. The introduction of gain and lossy effects enables asymmetric manipulation of amplitudes for incident waves from two opposite directions. On other hand, we employ the nonlinear material to provide pressure-dependent response to the incident wave, apart from its fundamental necessity for breaking the reciprocal principle1516171842021A large gap still needs to be filled, however, between such an asymmetric transmission and the desired one-way effect. For the purpose of achieving a perfect non-reciprocal transmission for which the forward transmitted waves will keep the original amplitude and frequency characteristics, it is vital to delicately tailor the structural parameters in the system to meet two crucial requirements: the gain and loss must be sufficiently high and, meanwhile, satisfy the complementary conditionFor a clear physics picture, we will inspect the time-domain waveform of the wave entering and leaving the nonlinear medium, which is the key part providing pressure-dependent property crucial for achieving the nonreciprocal transmission, and analyze the variation in the amplitude of spectrum components as the wave travels through this part along the two opposite directions. As a typical nonlinear circuit, a limiter circuit allows signals below a specified input power to pass unaffected while attenuating the peaks of stronger signals exceeding this input power. Here, we assume the nonlinear medium have the property of limiter circuit that has a ‘turn on’ value to distinguish the linear and nonlinear zones. Then, we will have three typical signals in this system, as shown in For verifying the effectiveness of our scheme, we have fabricated a prototype of the designed device and measured its non-reciprocal performance experimentally. For natural acoustic materials, the refractive index should have a negative imaginary part, indicating that the material is lossy with inherent damping. In contrary, acoustic gain materials with a positive imaginary part of the effective refractive index are usually unavailable in nature, which have recently attracted considerable attentions2021234We have measured the performance of the prototype within a broad frequency band ranging from 1 kHz to 8 kHz. The speaker is controlled by a signal generator and launches sinusoid signal for which the frequency is swept with a step of 500 Hz. To characterize the non-reciprocal transmission efficiency, we measure the amplitude of the incident signals and the transmitted signals along the two opposite directions, and evaluate the discrepancy between the transmissions of forward and reverse waves. For a quantitative estimation, we define the transmission along the forward direction as Realization of a perfect AD has long been challenging, to which considerable efforts have been and continue to be devoted. By exploiting the characteristic of nonlinear medium that it has a nonlinear response to the input signals with different amplitude, our proposed structure consists of acoustic nonlinear medium, acoustic gain and lossy materials to realize the nonreciprocal transmission. Compared the previous designs, the transmitted waves have an invariant frequency exactly the same as the incident waves and the amplitude can be tuned to unity, which are very close to what a perfect AD is expected to offer. Furthermore, it is observable from How to cite this article: Gu, Z.-m. et al. Broadband non-reciprocal transmission of sound with invariant frequency. Sci. Rep.6, 19824; doi: 10.1038/srep19824 (2016).


Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both  To fully understand how the brain works in both normal and pathological conditions, we need both functional and anatomical maps. The endeavor of describing the anatomy of the brain has been greatly advanced during the last century. The brain is a complex electrochemical device and, as such, it comprises several fundamental components , which are highly interconnected. To reverse engineer any device, we often need to identify its main constituent parts and how these are connected to each other. The same rationale can apply to the study of the nervous system. Therefore, one way to increase our understanding of the brain is to identify its different components and map their afferent and efferent connections.Importantly, the nature of the components and connections depends on the spatial scale at which the brain is investigated. At the macroscopic level (thousands of microns) the brain is made of different cortical areas and subcortical structures, which are connected to each other via short and long axonal bundles. Zooming in and looking at the mesoscopic organization of the cortical sheet (hundreds of microns), it can be observed that every cortical area shows a different layered organization of cells and neurites and that every layer has its own intracortical connectivity pattern. Finally, from the microscopic viewpoint (tens of microns), axons in white matter are not equally distributed everywhere, but have different packing densities and diameters.in vivo at sub-millimeter resolution. One MRI method in particular is gaining importance for the study of anatomy and structural connectivity of the brain: diffusion magnetic resonance imaging (dMRI). This technique is sensitive to the diffusion of water molecules within the different biological compartments in the brain, such as intracellular and extracellular compartments. This enables it to delineate with high accuracy the areas that have been affected by a stroke as well as to identify the main axonal orientation of neuronal fibers within an imaging voxel. Moreover, although dMRI has been mostly used to probe macroscale brain connectivity within white matter, it has recently also been used to highlight mesoscopic differences in lamination patterns within human cortical gray matter. Finally, dMRI-based techniques have also shown the potential to study the microscopic organization of white matter by estimating axonal packing densities and diameters.Recent developments in the field of magnetic resonance imaging (MRI) have made it possible to probe both the function and the anatomy of the brain Given these premises, the aim of this work is to review current knowledge about the structural architecture of the brain at different scales and how such knowledge can be advanced using dMRI. Diffusion MRI and dMRI-based techniques are first introduced together with their capability to resolve the anatomical connectivity patterns of the brain. After this, the power of dMRI to probe neuroanatomy at three different scales, namely the macro-, meso-, and microscopic ones, is discussed. This is done by reviewing both some of the most prominent neuroanatomical studies based on histological techniques and recent works coming from the dMRI field. The ultimate aim of this review is to provide the neuroimaging community, which comprises both neuroscientists and clinicians, with a description of tools and evidence that can help to understand the structural architecture of the brain.connectivity between different cortical regions. Diffusion MRI (dMRI) overcomes some of these limitations. Diffusion MRI encodes the displacement of water molecules within the different brain compartments, which can be used to characterize the brain’s structural organization.The development of MR neuroimaging techniques has made it possible to reach sub-millimeter resolution to map both the anatomy and the function of the brain. However, standard MRI acquisitions based on T1 and T2 contrasts give only a partial description of the brain’s microstructural composition. Moreover, these contrasts contain little information about the anatomical Free-floating molecules in a gaseous or fluid medium display thermal motion in a random walk pattern: a behavior known as Brownian motion or passive diffusion. Figure 1). Spins that are moving along the direction of the applied diffusion gradient will show bulk dephasing and, consequently, signal loss. The signal loss is proportional to the amount of diffusion, the area under the diffusion encoding gradients and to the time interval between the two gradients. In a mono-exponential signal decay model, the b-value is used as a measure of diffusion encoding in the diffusion gradients:To measure the displacement of moving spins with dMRI, the pulsed gradient spin echo (PGSE) sequence is mostly used . In thisb is the b-value measured in s/mm2, γ is the gyromagnetic ratio, δ is the duration of the diffusion pulse, | G| is the amplitude of the diffusion gradients and Δ is the time interval between the two diffusion gradients . The monoexponential approximation is valid at low b-values and models the signal as:where Si is the measured signal with the ith diffusion encoding gradient in direction i= , S0 is the measured signal without diffusion encoding, b is the b-value and Di is the ADC. A dMRI experiment will consist of a few unweighted S0 measurements and a larger number of measurements with different diffusion gradient directions. Under the narrow-pulse approximation (δ<<Δ), the ADC Di can then be estimated in every voxel of the image and for every sampled diffusion direction iwhere Figure 2; :The diffusion gradients could be said to probe a three-dimensional space called q-space  the tensorial matrix into so-called eigenvalues and eigenvectors:e1, e2, and e3 are the eigenvectors, λ1, λ2, and λ3 are the eigenvalues and T the transpose operator. Intuitively, this decomposition reflects the amount of displacement due to diffusion  along three orthogonal orientations (the eigenvectors). This means that in a highly isotropic voxel  the eigenvalues will roughly be the same and have a relatively high value. On the other hand, in anisotropic voxels  the eigenvalues will be different from each other, with the largest eigenvalue  much larger than the other two. The corresponding major eigenvector e1 then represents the main direction of diffusion. The complete eigenvalue decomposition allows the representation of the water molecules’ displacement within a voxel as a three-dimensional equiprobability surface, the well-known diffusion tensor ellipsoid .where e1 of this ellipsoid will correspond to the orientation of the axons in those voxels with simple geometry and a high degree of coherence of axonal bundles. Furthermore, several scalar indices can be estimated from the diffusion tensor. Mean diffusivity (MD) measures the average degree of water diffusion within a voxel and is computed as the average of the three eigenvalues. For low b-values , MD does not clearly highlight differences between white and gray matter, but it is very useful to discriminate pathological diffusion behavior due to, e.g., ischemic stroke. Fractional anisotropy . In the case of a 90° crossing, for example, there is an offset of 45° . A simplification to this model is represented by high angular resolution diffusion imaging (HARDI) acquisitions. By probing a high number of diffusion encoding directions only on one spherical shell in q-space , it is possible to infer non-Gaussian effects in the reconstruction of ADC profiles . The dODF peaks can then be assumed to represent the orientations of multiple fiber bundles within a specific voxel. By mapping the water displacement, QBI dODFs are not very sharp as water will still have some directional components orthogonal to the main axon directions. Spherical deconvolution . Further model-free approaches are such as the diffusion orientation transform , more than 90% of voxels contains more than one fiber bundle . Therefot of 45° . Furtheron. DSI,  reconstrprofiles . Q-ball profiles  was the volution  and, subvolution , aims atransform  and persransform  can be cb-value) HARDI-based techniques has been proposed. Generalized q-sampling imaging . A streamline is initiated at a certain seed point and follows the main eigenvector of the diffusion tensor, switching orientation as soon as it has entered a new voxel . As a result, the estimation of the diffusion tensor contains a certain amount of uncertainty, which is then reflected in its eigenvalues and eigenvectors . The result of a probabilistic tractography algorithm for every single well-defined seed point is a three-dimensional map of visitation counts for fibers through a voxel. The noisy streamline or diffusing particle principles have been applied both to tensor models and to complex local architecture models  in columns and tangentially  in layers. A single cortical column spans the cortical layers vertically, from the pial surface to the border between white and gray matter and, when combined with other columns, forms a so-called macrocolumn . The defSince both cyto- and myeloarchitectonics show a layered organization of the cortical mantle, the delineation of cortical areas can be inferred from different patterns of cell bodies as well as from changes in intracortical fiber configurations. However, the relationship between cytoarchitecture and myeloarchitecture in the cortex is still not clear. Each cortical layer tends to be characterized by different afferent and efferent projections. Moreover, the brain can be seen as a hierarchically organized system, where the hierarchical level of a cortical area is determined by the distance (in number of synaptic connections) from sensory areas. There are three main patterns of laminar projections within the hierarchically organized cortical system, namely ascending, lateral, and descending projections . These pex vivo acquisitions to increase SNR, different temperature conditions and tissue fixation effects have an influence on dMRI contrast. This is usually reflected in lower FA and lower MD values, as well as a greater challenges in the tracking of axonal fibers . Furthermore, the organization of primary visual cortex, where the highly myelinated stria of Gennari can be found, was investigated using post-mortem dMRI  and selected model, it is then possible to infer the microstructural characteristics of the tissue from model parameters such as compartment volume fractions  at the population level and at very high resolution. Making these data available to the whole scientific community must become a fundamental prior to any future study aimed at mapping the structural architecture of the brain.Finally, consortium projects including large population studies have recently started mapping structural and functional macroscale connectomes of adult human brains  and newbThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.