This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights . Intelligent headlight control (IHC) is important for advancing the driver assistance system (ADAS) for drivers who drive at night . IHC sysA hyperspectral camera sensor must be commercialized to apply the IHC technique using the hyperspectral imaging technology. In particular, the lens design is a very important part. The projected area of a lamp can be calculated geometrically from a pixel illumination model. The larger the lens magnification, the larger the projected area of the lamp in an image. If the projected area of the lamp is less than 1, the lamp fits only into a single pixel . This paNormalization techniques are used to solve the problems generated by different spectral profiles from the same material due to shading and shadow effects . A rangean image . HoweverI=c0This study used three detection metrics, SAM, SCM, and EDM. These methods were compared in each headlight type .The SAM algorithm is based on the ideal assumption that a single pixel of remotely sensed images represents a certain ground cover material, and can be assigned uniquely to only one ground cover class. The SAM algorithm is just a simple measurement of the spectral similarity between two spectra . The SAMGeometrically, at a lower angle  spectral feature space. The hyperspectral image acquisition system consisted of a SPECIM VNIR  camera mounted on a rotary tripod . The oriHeadlamps were prepared such as Mercedes-Benz′s E-class (LED lamp) , Hyundai′s Grandeur-HG (HID lamp) , Kia′s Sorento  , Hyundai′s Sonata  and Kia′s Cerato . Unlike the detection of reflecting light from a material, this experiment minimized the aperture size of the hyperspectral camera, to detect the light directly from the lamp. Matlab R2015b  was used to analyze the spectral profiles and process the headlamp detection. Three types of evaluations were used for each method combination for the detection of each headlight . This experiment can distinguish between each lamp and streetlamps. In the experiments, preprocessing and detection methods were used.First, a range of methods were evaluated using the Receiver Operating Curve (ROC) and Area Under Curve (AUC); therefore, the best combination can be found by comparing with the previous method to detect a headlight . In the experiment, the detection result can be compared with the AUC in the ROC analysis of detectors. A mask image was made, as shown in In the case of the LED headlight, the best detection metric was the SCM and the AUC values were higher than with previous methods using the SCM  and the EDM (Raw). In the case of the HID headlight, the best combination method was the SAM detection metric and Mean-Norm preprocessing. In addition, all AUC values were higher than with previous methods. In the case of the halogen headlight, the best combination method was the EDM detection metric and raw data.The second experiment was conducted to distinguish headlights from the reflection of ego-car in the infrastructure. The halogen lamp and the reflection of the ego-car show different spectral information. In particular, the yellow reflection, whose spectra is shown in This experiment compares the detection headlight from the near distance and far distance. The proposed hyperspectral image-based spectral analysis method can detect near and far distant headlights, simultaneously. Almost no degradation of the detection performance depending on the headlight distance was observed because the hyperspectral lamp signatures show consistent spectral profiles.Rear lamps are usually in red color, and there are two types of lamps, such as Halogen and LED. The detection of rear lamps is very important in the development of an IHC system.The rear lamp also can distinguish other lights, such as street lamps or other headlights. The proposed hyperspectral image-based spectral analysis method also can detect a rear lamp.This paper proposed a new detection method using a hyperspectral image to detect automotive headlights effective for IHC. The experiments were conducted using the SAM, SCM, and EDM distance metrics and the detection performance was assessed using the ROC and AUC methods on real headlight images. In addition, there were two additional experiments. The threshold sensitivity from the previous methods was compared with that of the proposed method. According to the results, the best combination is the SCM detection metric with the raw or Mean-Norm in the case of LED. The best combination is the SAM detection metric with Mean-Norm in the case of HID. The best combination in the case of the halogen lamp was the EDM metric with raw data. The detection performance of the proposed method was lower at the same FPR (


Saccharomyces cerevisiae, Cdc14. Transient inactivation is expected to better mimic the pharmacological action of drugs. Interestingly, we have found that yeast cells tolerate badly a relatively brief inactivation of Cdc14 when cells are already committed into anaphase, the first cell cycle stage where this phosphatase plays important roles. First, we noticed that the segregation of distal regions in the chromosome arm that carries the ribosomal DNA array was irreversibly impaired, leading to an anaphase bridge (AB). Next, we found that this AB could eventually be severed by cytokinesis and led to two different types of genetically compromised daughter cells. All these previous studies were done in haploid cells. We have now recently expanded this analysis to diploid cells and used the advantage of making hybrid diploids to study chromosome rearrangements and changes in the ploidy of the surviving progeny. We have found that the consequences for the genome integrity were far more dramatic than originally envisioned.Cycling events in nature start and end to restart again and again. In the cell cycle, whose purpose is to become two where there was only one, cyclin-dependent kinases (CDKs) are the beginning and, therefore, phosphatases must play a role in the ending. Since CDKs are drivers of the cell cycle and cancer cells uncontrollably divide, much attention has been put into knocking down CDK activity. However, much less is known on the consequences of interfering with the phosphatases that put an end to the cell cycle. We have addressed in recent years the consequences of transiently inactivating the only master cell cycle phosphatase in the model yeast  Thus, Cdc14 actions at the end of mitosis, coupled to the overall CDK inactivation, changes the phosphorylation/dephosphorylation ratio of most CDK targets in favor of the latter. Cdc14 is first activated shortly after anaphase starts by the action of APCCdc20. Before that, Cdc14 is kept fully inactive through its binding to the nucleolar protein Net1 (Cfi1). This first wave of Cdc14 activation occurs through the FEAR (Cdc Fourteen Early Anaphase Release) network and takes place while the CDK activity is still high, contributing to the beginning of its decline. Shortly afterwards, a second and more potent Cdc14 activation by the Mitotic Exit Network (MEN) greatly accelerates the loss of CDK and ensures cytokinesis and the exit from mitosis into a new G1 for the mother and daughter cells.Cdc14 is a cell cycle phosphatase essential for the mitosis-to-Gcdc15-2) block cells with high CDK activity right before cytokinesis. However, sister chromatid resolution and segregation appears complete, even for challenging regions (see below)  thermosensitive allele (ts), leads to an anaphase block with incomplete positioning of the SPBs in the mother-to-daughter axis, unhealthy bended and broken spindles, lagging DAPI-stained DNA masses at the bud neck, a gross failure to segregate the nucleolus, and unresolved sister chromatids near the telomeres of several chromosome arms , and the ribosomal DNA array (rDNA) are particularly impeded. The rDNA has been studied in depth , from the centromere to around one-third within the centromere-proximal rDNA, with the other two-thirds of the rDNA and the rest of the chromosome arm up to the telomere unresolved  provided enough Cdc14 activity to repositioning the SPBs, elongating the spindle, and allowing exit from mitosis   once the cells are blocked at the cdc14-1 release, we noticed that this situation could be exploited to address the consequences of this novel cXIIr-AB for the immediate progeny. Thus, we reported in 2012 that the cXIIr-AB is severed as cytokinesis seemed to proceed to completion, giving rise to two daughter cells with different genome contents  pathway. This happens in both daughters despite one of them is to become unviable as it has lost essential genetic information  events, trisomies and monosomies were often found in surviving colonies. Most of these genetic rearrangements fit well within the predictions of models to deal with the broken cXIIr-AB (Fig. The limitations of haploids to assess the putative genomic rearrangements that followed the severing of the cXIIr-AB prompted us to recently study this phenomenon in diploids. Importantly, diploid cells also form the cXIIr-AB, although the failure to timely resolve the bridge after the -AB Fig. . It was -AB Fig. . In addiThe study of Cdc14 has provided important insights into the control of the cell cycle and the counterbalance of CDK activities. Of the many phenotypes observed in cells transiently deficient for Cdc14, the formation of an anaphase bridge that comprises partly resolved sister chromatids has unexpectedly provided a model to study the consequences for the progeny of this aberrant situation (Machín et al.


The symmetry breaking observed in nature is fascinating. This symmetry breaking is observed in both human crowds and ant colonies. In such cases, when escaping from a closed space with two symmetrically located exits, one exit is used more often than the other. Group size and density have been reported as having no significant impact on symmetry breaking, and the alignment rule has been used to model symmetry breaking. Density usually plays important roles in collective behavior. However, density is not well-studied in symmetry breaking, which forms the major basis of this paper. The experiment described in this paper on an ant colony displays an increase then decrease of symmetry breaking versus ant density. This result suggests that a Vicsek-like model with an alignment rule may not be the correct model for escaping ants. Based on biological facts that ants use pheromones to communicate, rather than seeing how other individuals move, we propose a simple yet effective alarm pheromone model. The model results agree well with the experimental outcomes. As a measure, this paper redefines symmetry breaking as the collective asymmetry by deducing the random fluctuations. This research indicates that ants deposit and respond to the alarm pheromone, and the accumulation of this biased information sharing leads to symmetry breaking, which suggests true fundamental rules of collective escape behavior in ants. The collective behavior of large groups of animals is a truly fascinating natural phenomenon. Especially when under pressure, such as encountering predators, certain animals form groups and produce amazing patterns for survival Altshuler et al. (2005) found that symmetry breaking can even occur with escaping ants This conclusion that the asymmetry is independent of the density contradicts the facts reported in the literature that density is an important factor affecting the properties of collective behavior in real biological groups, such as non-living systems Although Vicsek-like models are successful in a wide range of collective motion systems, recent experimental studies uncovered that the collective behavior of specific species may not satisfy all the assumptions and rules. Some examples follow. Rather than incorporating homogeneous agents, a well-defined hierarchy has been found in pigeon flocks In the case of ants, it is well-known that most species have poor eyesight, and a portion of ants are nearly blind. Rather than seeing how other individuals move nearby, ants use their antenna to sense their surroundings and use pheromones to communicate with each other Solenopsis invicta Buren) into a cell with two symmetrically located exits, which were initially blocked so that the ants could not escape. Then a dose of citronella was injected into the center of the cell, and the exits were opened synchronously so that the ants were able to escape. There were 291 repetitions in total. To measure the symmetry breaking, Altshuler et al. (2005) the percentage difference in door use (abbreviated as difference), which is calculated asWe introduced a group of ants ,  and . The difference is 50% and not zero. The focus of this current research is to study how the symmetry breaking is influenced by the total number of ants, which is actually the density when the experiment cell is fixed. Thus, taking two experiments with different total ant numbers, s higher . The numThe Vicsek-like model used by Altshuler et al. inspired by Helbing et al. (2000) suggests a discrete increase of the A number of pheromone-related models, including the best known ant colony algorithms, have been developed The rules are described below, and a schematic diagram is presented in Initially, each ant occupies one randomly chosen locus on the square lattice with a direction vector randomly chosen. (The detection range of an ant is its nearest 8 loci.)At each time step, each ant leaves a constant amount of pheromone When an ant detects one of the two exits, this ant escapes from the chamber immediately. When an ant meets the boundary, it reflects. Otherwise, the position of each ant updates according to the following equations:Here, Moreover, if the detected amount of pheromone is larger than the given threshold In the simulation, the following set of parameters is used: Solenopsis invicta is approximately 100 seconds Experimental research shows that the fade-out time of the recruitment pheromone of the The simulation results compared with experimental results are shown in In addition to the average collective asymmetry, we also investigated how the fluctuation of collective asymmetry is affected by total number of ants  from a single nest in South China Agricultural University located in Guangzhou city were fed in the laboratory for a few weeks, and the experiments were performed in the period of June 4–24, 2011. The temperature in laboratory is consistently approximately Before the experiments, the field-collected red imported fire ants  were used to avoid residual liquid and possible pheromone residue on the equipment.It was impossible to precisely control the total number of ants in each experiment. The reason is twofold. First, to keep the ants in a high-panic situation, the ants have to be introduced into the cell quickly enough at the cost of impreciseness of the ant number. Secondly, some ants died or gradually lost mobility because they contacted the repellent oil before the oil had been blotted by the filtering paper. The average proportion of the remaining ants at the end of each experiment to the total number of ants at the beginning of each experiment is Considering an Just as we calculated the S1 Data(XLSX)Click here for additional data file.


Gait is a complex process involving both cognitive and sensory ability and is strongly impacted by the environment. In this paper, we propose to study of the impact of a cognitive task during gait on the cerebral blood flow velocity, the blood flow signal features and the correlation of gait and blood flow features through a dual task methodology. Both cerebral blood flow velocity and gait characteristics of eleven participants with no history of brain or gait conditions were recorded using transcranial Doppler on mid-cerebral artery while on a treadmill. The cognitive task was induced by a backward counting starting from 10,000 with decrement of 7. Central blood flow velocity raw and envelope features were extracted in both time, frequency and time-scale domain; information-theoretic metrics were also extracted and statistical significances were inspected. A similar feature extraction was performed on the stride interval signal. Statistical differences between the cognitive and baseline trials, between the left and right mid-cerebral arteries signals and the impact of the antropometric variables where studied using linear mixed models. No statistical differences were found between the left and right mid-cerebral arteries flows or the baseline and cognitive state gait features, while statistical differences for specific features were measured between cognitive and baseline states. These statistical differences found between the baseline and cognitive states show that cognitive process has an impact on the cerebral activity during walking. The state was found to have an impact on the correlation between the gait and blood flow features. Walking is a complex sensory-cognitive interaction which has various demands depending on the environment –3. InitiIn general, a dual task  causes concurrent demands for attention/cognitive resources. Hence, it generates a cognitive-motor interference that implies tripping, falling, physical instability and/or a decline in performance , 4, 11. Thus, there is a growing interest in clarifying the correlation between motor control and cognition. Brain imaging methods have revealed activation of various cerebral regions associated with higher cognitive functions during walking .However, because of the lack of portability of these devices and the fact that they require the subject to lie down during acquisition, the gait is usually modeled as a series of feet tapping in the fMRI case , 21 and Since it has been proven that CBFV modifications and neural activity can be correlated –28, we uThe current study was focused on studying the associations between the cerebrovascular system and gait and understanding the repercussions of cognitive load during walking on the system. Specially, we examined CBFV signals in MCA. Our major contributions are the statistical differences found in the central blood flow velocity signals between the baseline and cognitive states during walking. We examined both raw signals and the envelope signals, which are maximum peak velocity outcomes. In addition, baseline is actually walking—and on a treadmill which is a dedicated stepping pattern facilitator, walking could be even more automatic than usual—yet the blood flow changed with added cognitive processing; and the changes in CBF consistency mirrored in part the changes in gait consistency—at least for the performance of some—illustrating the potential interference of cognitive functioning and walking.For this preliminary data collection, eleven participants were recruited . None of these subjects had history of concussion, heart murmurs, migraines, strokes or other brain and gait conditions. Participants were asked to signed the University of Pittsburgh Institutional Review Board approved consent form and the procedure was explained prior to the beginning of the experiment and data collection.The participants were briefed to walk at a pace of 2 mph on the Noraxon MR3 treadmill and to remain thought-free during this 6 min baseline testing period. Upon the completion of this trial, the participants were asked to count backwards from 10,000 in decrements of 7 while walking for 6 min for another trial referred as cognitive. Approximately 1 week later, each participant repeated the experiment.To observe stride time, Noraxon SciFit treadmill using capacitive sensor technology to analyze individual foot pressure  was used to measure blood-flow velocity in the mid-cerebral artery. Two 2 MHz transducers were used to gather simultaneous bilateral CBFV acquisitions from the left-MCA and right-MCA. The transducer were planted on transtemporal windows  to reachribution . In thisribution , 45.Furthermore, the end-tidal carbon dioxide The mean, standard deviation, coefficient of variation (which is the ratio of the standard deviation to the mean) and the spectral exponent were estimated for stride interval time series extracted from both feet.The wavelet transform of the centred version of the signal, The mean average with respect to translation coefficient s is plotted. The linear regression of the resulting graph is computed. The slope of this line is The log-log plot of HfBm+1/2 .The spectral exponent The number of scales were chosen with respect of the signal length, using the following expression:To determine the spectral exponents of the stride interval signals, the average wavelet coefficient (AWC) method was used. This method can be summarized as follows , 47:The In this study, four distinct statistical features where evaluated. The standard deviation, the skewness, the kurtosis along with the cross-correlation coefficient of right and left MCA signals were extracted and compared. The first three parameters characterize the shape of the signal’s distribution , while tn is its length.The kurtosis of a distribution can be expressed as :4\documeX and Y represent signals from the right and the left side of the MCA.The expression of the skewness is :5\docume follows :6\documeWith regard to the information-theoretic feature space, the Lempel–Ziv complexity (LZC), which represents the amount of new pattern formation in finite time sequences built upon the original signal   and the In order to obtain a finite time sequence to compute the LZC, the signal is divided into 1,…,α-1} .n. The blocks are a time series of successive data.Then, portions of the quantized signal uch that :7\documeL, an analyse of each block is performed the following way: a counter c(j) is defined to illustrate the amount of new pattern formation. If the sequence represented in a block has not appeared in a previous analysis, this counter is incremented by one.Then, for each length c(n) is the value that the counter takes when the entire analysis is performed and Eventually, the LZC is computed as given herein bellow:L such that To compute the entropy rate  such as :9\documej in The Shannon entropy fined by :10\documL arising only once in L, which is the Shannon entropy of white Gaussian noise process. This term corrects the underestimation of L [L while the second term increases), the function The normalized conditional entropy is then given as :11\documengths L . Conside 0 and 1 .X and Y were normalized and quantized in a similar fashion than the one used to compute the entropy rate, giving as a result the two quantized distribution For comparison between two probability density functions purposes, one can use the cross-entropy rate. This index quantifies the amount of mutual information between two given distributions, and aims to predict the data of a considered signal using the previous and current information found in another signal. The two distributions X, Y and Eventually, the cross-entropy rate ing code :12\documL that were present only once in X for a unit length. The function X and Y are independent) to 1 (when X and Y are synchronized stochastic processes).The normalized cross-entropy was then computed as:Features were also extracted with regard to the frequency domain: the peak frequency, spectral centroid and bandwidth were computed as characterization indexes for the extracted from the TCD signals .X and The peak frequency, given as BW, representing the dispersion of the spectrum, was computed as [The spectral centroid,  follows :15\documputed as :16\documkth level [To extract features related to the time-scale domain, the signal was decomposed into 10 levels th level ) using ath level :17\documk-th level of decomposition to the total energy of the signal.These metrics describe the relative energies repartition within distinct frequency bands based upon the ratio of the The wavelet entropy d signal . It is aSAS version 9.3  was used for all statistical analyses. Our overall strategy was to keep the two trials separate and fit a sufficiently complex model in order to account for between-trial correlation to maximize the use of information and statistical power. For simpler methods such as correlation analyses, we averaged participant measures across the two trials. To examine significance of systematic differences in measures between the two trials we fitted a series of linear mixed models using the SAS MIXED procedure with each gait/blood flow measure as the dependent variable; trial as the factor of interest; and a subject random effect to account for multiple correlated measurements from the same participant. We computed baseline condition vs cognitive task difference for each measure, and fitted a similar mixed model with the said differences as dependent variables but in an intercept-only model to obtain statistical significance for the between-condition difference under two correlated trials. To examine whether participant characteristics , we fit another series of models with each gait/blood flow measure under each task condition as the dependent variable; each participant characteristic as the fixed effect of interest; and a participant random effect to account for multiple trials. To examine correlation coefficients across measures, we first averaged all measures across the two trials. Next, we used Pearson correlation coefficients between gait measures and blood flow measures; as well as between left and right sides for the blood flow measures. Additionally, we obtained partial correlations adjusted for participant characteristics. We are aware of the large number of measures and statistical tests performed in our analysis, and thus use false discovery rate (FDR) methodology for adjusting p-values for multiplicity .The end-tidal carbon dioxide level does not influence the mean diameter of the middle cerebral arteries . ConsequFeature extracted from the subjects’ stride intervals are provided in Table Table Moreover, the inspection of the mean peak blood flow velocity revealed an increase during the cognitive task . The standard deviation and coefficient of variation of the right foot stride interval both are correlated with 10 of the features of the CBFV (with 9 of the correlated being the same for the left and right foot). The same stride related features when the left foot is considered are correlated with 11 and 10 features of the CBFV, respectively. Lastly, the spectral exponent coefficient for the left foot stride interval is correlated to two of the central blood flow velocity signals features: the R-MCA raw signal peak frequency and the R-MCA envelope signal wavelet entropy.Our major finding is that there are statistical differences in cerebral blood flow velocities in MCA between the baseline and cognitive states during walking. These differences were observed both in the raw signals and in the envelope signals  associated with cerebral blood flow velocities.The statistical differences revealed in this study between blood flow during walking and blood flow during walking with an added cognitive processing task illustrate the suggested performance pattern seen in older adults with greater difficulty walking under challenging conditions such as walking and talking or walking and thinking. Under the challenge of the added cognitive task, it was more difficult to maintain usual gait performance. The difficulty manifested in gait as largely greater inconsistency in the pattern of strides than in the mean of the stride interval time series. This difficulty was also mirrored in the cerebral blood flow. Thus, under the cognitive processing task condition, the range of gait performance was expanded for some variables, despite no change in the group mean for the gait characteristics. The expanded range of gait performance may underlie the different and greater number of correlations between gait and blood flow in the walking plus cognitive processing condition. A cognitively challenging task during walking may alter the range of gait performance, which is also associated with greater cerebral blood flow or ‘work’ of the brain.Among the stride measures, the standard deviation of the stride interval exhibited the strongest correlation with blood flow velocity features. This finding may indicate that while the cognitive processing did not disrupt gait described by the mean of the stride interval, the added cognition may have influenced the consistency of gait performance . While all participants accomplished the task of walking and thinking on the treadmill with no change in the mean stride characteristics, the difference in the level of cognitive challenge for participants was apparent in the inconsistency of gait. Greater cognitive challenge for some was associated with greater blood flow. Moreover, the increase in the bandwidth of the raw signals denotes a change in individual behavior of the erythrocytes, as a higher bandwidth value denotes higher spectral spread, which is caused by more erratic flow of red blood cells. The lack of statistical differences between the first and second trials denotes that the extracted features tend to be trial-independent.A potential limitation of this study comes from the fact that the participants were instructed to walk on a treadmill: indeed, changes in the gait characteristics are very likely concealed by the walking rhythm induced by the treadmill.In this paper, the dual task methodology exhibited that the cerebral blood flow velocity signals in the baseline and cognitive states during walking are statistically different. These differences can be found in extracted features for both the raw and envelope of the L-MCA and R-MCA central blood flow velocity signals. While the changes in gait features were subtle, the different and more relations between gait and blood flow with the added cognitive processing during walking suggests cerebral blood flow velocity may represent the work of the brain when thinking and walking.


Upright stance on a balance board is a skill requiring complex rearrangement of the postural control. Despite the large use of these boards in training the standing posture, a comprehensive analysis of the learning process underlying the control of these devices is lacking. In this paper learning to maintain a stable stance on a multiaxial oscillating board was studied by analyzing performance changes over short and long periods. Healthy participants were asked to keep the board orientation as horizontal as possible for 20 sec, performing two sessions of 8 trials separated by 15-min pause. Memory consolidation was tested one week later. Amplitude and variability of the oscillations around horizontal plane and area and sway path of the board displacement decreased rapidly over the first session. The performance was stable during the second session, and retained after 1 week. A similar behavior was observed in the anterior-posterior and medial-lateral directions for amplitude and variability parameters, with less stable balance in the anterior-posterior direction. Approximate entropy and mean power frequency, assessing temporal dynamics and frequency content of oscillations, changed only in the anterior-posterior direction during the retention test. Overall, the ability to stand on a balance board is rapidly acquired, and retained for long time. The asymmetric stability between anterior-posterior and medial-lateral directions replicates a structure observed in other standing stances, suggesting a possible transfer from previous postural experiences. Conversely, changes in the temporal dynamics and the frequency content could be associated with new postural strategies developed later during memory consolidation. Maintaining upright posture in humans is a complex task which requires a continuous control by the nervous system to overcome the growth rate of the gravitational torque. To guarantee a safe stance in an environment with a variety of perturbations, the strategy of balance control gradually improves during the life span until an adult pattern emerges . AlthougGenerally, learning a motor skill requires many repetitions over a period of time during which the discrepancy between the current performance and a reference target decreases (error-based paradigm). Throughout trial-by-trial training, the error progressively reduces, tracing a curve with an early phase marked by a rapid improvement in performance, followed by a phase in which the performance approximates the reference target much more gradually. Once the training finishes, the memory consolidation begins to develop and the new skill can be retained over a long time –14. DuriThe error-based learning paradigm has been observed across many experimental studies on motor learning, such as reaching in force fields , visuomoIn the field of rehabilitation, a large consensus exists on the beneficial effects of training with balance boards on many postural rehabilitation contexts, such as the recovery of sport skills , the impTo promote a better understanding of the basic adaptation processes and to provide useful suggestions for the dosing and the timing of postural rehabilitation programs, in this work, the issue of learning and memory retention of a novel upright posture was addressed by exploring the ability of healthy young adults to stand and maintain the balance on a board rotating around a multiaxial central pivot.Unlike previous similar studies, mostly using specific sensory perturbations or surface support rotations around single axes, with a limited parameterization, we adopted a multiaxial balance board making the interactions between the postural control and the environment more challenging.To keep a multiaxial balance board horizontal, is necessary to activate all the sensory and motor channels to maximize the upright standing performance. This ecological approach is compromised in simple reactive tasks, such as to respond to single perturbation generated by platform servo- controlled or to master a uniaxial balance board. Instead, the more complex process required to cope with a multiaxial board may stimulate the exploration of new schemas of postural control or the use of memorized models from previous experiences. For example, analyzing the oscillations along the anterior-posterior (AP) and medial-lateral (ML) directions within the same functional context, it will be possible to test if the asymmetric control between the two directions observed during static upright stance  is alsoFor a suitable exploration of these issues, two types of assessment tools were used: stability-related parameters to quantify the reduction of amplitude and variability of the board fluctuations, and a set of measures to evaluate changes in the structure of postural control.The structure of postural control can change regardless of the level of stability improvement and the phase of learning process. For example, given a level of performance, the spatial and temporal structures of the fluctuations in the AP and ML directions, as well as their power spectrum profile, could show different behaviors depending on postural conditions –27, sensIn this view, the current research pursued two main goals: first, to characterize the time course of learning and retention in maintaining upright stance on a multiaxial balance board; second, to highlight structural changes of postural control as they could emerge from stability asymmetry between the AP and ML directions or from modifications of sway oscillations with respect to the frequency domain and/or temporal dynamics.The participants in this study were ten healthy male college students , without any history of neurological diseases or injuries to the lower extremities, and in absence of vestibular or vision disturbances affecting balance. Participants were unfamiliar with the task, and those who had professional experiences in motor performances, such as skiing, skateboarding, martial arts, gymnastics, and ballet were excluded from the study. Standardized instructions and explanations about the procedures were given to the participants just before starting the test.Participants signed an informed consent document according to the Declaration of Helsinki and this study was specifically approved by the ethical committee of the University of Catania.A force platform  was used to analyze static quiet stance, while a multiaxial balance board  was employed for the balance training.The balance board used in this study was a wooden plate 40 cm in diameter, 1.5 cm in thickness and 7 cm high, with a rocker base creating an intrinsic multiaxial instability . OutlinePrior to the balance training, each participant performed a single 50-sec test of quiet stance on the force platform with the feet parallel and 20 cm spaced at the heels. This configuration was chosen to be consistent with the feet positioning over the balance board.After a pause of 2 min, participants started the balance training without any preliminary practice to familiarize with the task. Each participant was helped to stand on the board, and, under the supervision of the investigators, he arranged his feet inside the outline borders. Initially, two investigators provided a support on either side of the participant, allowing him to place the board surface parallel to the floor. From this initial position, the trial started when the participant was ready to leave the external support. For each trial, participants were asked to keep the board as horizontal as possible for 20 sec. No instruction was given as to how to keep balance, and the individuals were free to choose their own strategy. During the tests, participants stood barefoot, with their eyes focusing on a mark placed on the wall at a distance of 2.5 m. To prevent falls two investigators were close to the participant during the entire trial.The experimental protocol included two training sessions (S1 and S2), each of 8 trials, with an inter-session interval of 15 min. Each trial lasted 20 sec with an inter-trial interval of 30 sec. To prevent fatigue, participants were instructed to alert the experimenter if they were feeling fatigue, and the experimenter occasionally asked participants about their fatigue level. Given the large fraction of time for the rest  with respect to the time for training over the balance board (about 6 min), fatigue was never an issue. A week after the completion of the practice, a retention test of 8 trials was performed to assess the level of memory consolidation of the postural performance. The recording sessions took place in a room at the temperature of 22–24°C, without external noises, and with diffused light.Raw signals from the force platform and the markers on the board were captured by the software SmartCapture  and processed offline. All of the data were first interpolated by a cubic spline function to compensate for missing data, and then the signals were low-pass filtered with a zero-lag second-order Butterworth filter with 5-Hz cutoff frequency.The motion of the balance board was analyzed by computing the vector orthogonal to the board plane  was obtained by measuring the angle of oscillations of one marker around its initial position before the trial started.D = −Ax0 − By0 − Cz0, where x0, y0, z0 are the 3D coordinates of the normal vector position.The normal vector computation was performed by using the Cartesian equation of a plane:pparatus ; A, B, CThe angle of the board with respect to the horizontal plane was determined by computing the directional coordinates A, B, C, of the normal vector given three points lying on the plane. To select the three points among the 8 markers applied on the board, the trajectories of each marker was tracked, and the 3 best signals  were chosen.1, y1, z1), , and  are given by the following determinants:The directional coordinates for an equation of the plane passing through the points ; shape In addition to linear measurements of postural stability, we computed the Approximate Entropy (ApEn) and the Fractal Dimension (FD) as non-linear measures to explore the temporal dynamics of the signals along AP and ML directions, and the structure of the planar trajectory tracked by the board normal vector.Approximate Entropy is a probability statistic that calculates the likelihood that a template pattern repeats over time. It is frequently being used to estimate the variation of regularity in time series associated with physiological signals , 38, incd = (2a ⋅ 2b)1/2 where a and b are the major and the minor axes of the 95% confidence ellipse, respectively; Sway path is the total length travelled by the board normal vector. Two-dimensional FD ranges from 0 to 2, with the value increasing when the trajectory is more complex.The Fractal Dimension (FD) was computed using the algorithm described by Prieto et al., , who quaf represents frequencies in the signal and P is the amplitude of the PSD at each frequency.To explore the frequency domain of the postural sway, the Power Spectral Density (PSD) was performed on the unfiltered AP and ML time series using the multitaper estimation method, as suggested by Prieto et al., . This anAll the computations were performed using a customized MatLab R2012a code .The center of pressure (COP) of static stance was computed from forces and torques measured by the force platform and elaborated by the software Sway . To identify similar postural patterns between quiet stance and stance on the oscillating board, we carried out measurements of sway path, RMS, ApEn, and MPF for the COP along AP and ML directions. The determination of these parameters was performed following the processes described in the previous section, replacing the unit normal vector trajectory with the COP trajectory.For each trial the mean and the variability, expressed as Standard Deviation (SD), of each parameter were determined participant by participant, and grand averages over the 10 participants were computed.Statistically significant changes across sessions and trials were estimated using a two way Analysis of Variance (ANOVA) with repeated measures, considering sessions and trials as within subject factors. Statistical differences between the AP and ML directions were evaluated by a three way ANOVA with repeated measures, with sessions, trials and directions as within subject factors. The critical value of F was adjusted applying Greenhouse-Geisser correction, which produces a p-value more conservative. This procedure corrects the repeated-measures ANOVA with respect to a possible violation of the sphericity assumption, that is, the variance of the differences among all combinations of independent variables must be equal. Post hoc tests with Bonferroni correction for multiple comparisons were conducted to identify local significant differences between sessions or trials. The level of statistical significance was set to P < 0.05.a priori based on preliminary data acquired from 5 participants. Power calculations for within-subjects designs were performed using G-power  as input. Power analysis revealed that a sample size of 7 was required to achieve a power of 0.95 for 10 out of 12 parameters, while only for the sway path  a sample size of 10 was required to achieve a power of 0.85. On this basis, we deemed that 10 participants was a sufficient quantity for the results to be meaningful.The sample size, to detect differences in stability and structural parameters over the sessions, was determined 3.1.9.2; ) by taki3.1.9.2;  and givi2p, which describes the percentage of variance of the dependent variable attributed to the independent variables of interest. For the post-hoc paired tests, the effect size was determined by using Hedges’ gav, which represents a correction for bias of Cohen's dav (the standardized mean differences based on the average standard deviation of both repeated measures). The effect size computations was based on the recommendation reported by Lakens [The magnitude of significant differences (effect size) for the multivariate within-subjects ANOVA statistics was estimated by using ηy Lakens .Statistical analysis was performed using SYSTAT, version 11  and Matlab version R2012a .A representative example of data obtained from one participant is illustrated in the Most of the changes reported in this example are replicated in the results of the statistical analyses performed in the entire sample and described in the following sections.The ability to maintain the balance board aligned with the horizontal plane improved progressively across the trials of S1 of training, reaching a stable level during S2. After one week, the level of performance was preserved, with a further improvement in the last trials  and frequency domain (MPF) parameters during the retention session. In fact, while stability measures maintained similar values, passing from the second to the retention session for both the directions, the structure of the temporal sway variability and the frequency content of the signal changed in the AP direction during the retention test.Approximate entropy estimates the level of regularity of the signal in a time series, with high values indicating a more irregular motion and, therefore, a more complex response organization , 38. TheMany authors found that the increase of ApEn or homologues parameters was focused on the AP axis , 55, 62 This line of considerations is also supported by the higher values of FD detected in the retention session with respect to the training sessions. As the FD value increases, the board trajectory gains a more complex geometrical structure. Interestingly, Duarte and Zatsiorsky,  reportedFurther insights on the structure of the postural adaptations associated with the control of the multiaxial balance board may be provided by the modifications observed for the MPF. Some authors reported that changes in frequency band may be indicative of the type of sensory channel involved in the balance task: low frequencies 0–0.3 Hz) are linked to visual control, the medium-low frequency band (0.3–1 Hz) is associated with vestibular regulation, and the medium-high frequency band (>1 Hz) mostly account for proprioceptive and muscular activities , 29, 64. Hz are lA possible rearrangement of the coordination dynamics of the ankle, knee and hip motion to deal with AP instability may enhance the contribution of proprioceptive information , 65, whiChanges in spatio-temporal structure and frequency band showed during the retention session suggest that something more than the only amount of exercise influenced the long-term postural adaptation; that is, processes occurred during the offline periods refined and further improved the postural response. Many studies analyzing this issue found that memory consolidation occurs within a critical time frame following the practice , with anOverall, while the comparison of amplitude and variability changes in AP and ML directions over the course of the learning process suggests that postural control evolves on the basis of previous experienced strategies, later, after the week pause, the changes in ApEn, FD and MPF may represent signals indicating the formation of new stance strategies aimed to further stabilize the learned postural performance.This study provides an extensive description of the timing of adaptation and the nature of the postural processes associated with the balance board training. The rapid performance improvement, within a session of 8 trials, indicates that the effects of this type of exercise on the postural control would be already observed with a short period of training. However, an important message for applicative outcomes provided by our findings is that the possible benefits are not limited to the period of practice, but long time adaptive processes, occurring during offline periods, may be effective later in the process of functional recovery. Thus, to set an optimal paradigm for training postural abilities by using multiaxial balance boards, the rehabilitation programs would take in account that the distribution of the pauses may provide additional advantages with respect to those derived from the amount of practice alone. Future studies will be directed at the refining of the experimental design to quantify the contribution of the distribution of the practice on the upright stance stability.Balance training on an oscillating platform is considered a specific exercise to stimulate the proprioceptive channel . HoweverA more specific suggestions to encourage the use of multiaxial balance board for balance training, regards the anisotropic behavior observed between the AP and ML directions. In fact, the multi directional motion forced by the multiaxial board may optimize specifically this asymmetric schema of postural control with benefit for static and/or more dynamic postures which use similar modality in maintaining upright stance.Overall, the data of the current paper may form the basis for more specific experimental designs aimed to accomplish a direct examination of people that need postural rehabilitation.Learning to maintain a stable upright stance on a multiaxial balance board stimulates a fast improvement of the performance, with long-term retention. The asymmetric stability observed between AP and ML directions suggests that a portion of the processes could have been accomplished by similar strategies experienced in other more common standing postures. In addition, changes in temporal and frequency band profiles during consolidation period indicate that the skill still develops after the practice, producing, specifically for the AP direction, new models of postural control. We deem that these findings may contribute to better understand the basic processes underlying learning and adaptation of upright posture and to help in planning a correct rehabilitation approach to the balance disorders.


Anabaena PCC 7120. We are able to identify the transcription starting site of each of the promoters as the most populated macrostate in the dynamics. The developed procedure allows also to characterize promoter macrostates in terms of thermo-statistical magnitudes (free energy and entropy), with valuable biological implications. Our results agree with independent previous experimental results. Thus, our methods appear as a powerful complementary tool for identifying protein binding sites in promoter sequences.The identification of protein binding sites in promoter sequences is a key problem to understand and control regulation in biochemistry and biotechnological processes. We use a computational method to analyze promoters from a given genome. Our approach is based on a physical model at the mesoscopic level of protein-DNA interaction based on the influence of DNA local conformation on the dynamics of a general particle along the chain. Following the proposed model, the joined dynamics of the protein particle and the DNA portion of interest, only characterized by its base pair sequence, is simulated. The simulation output is analyzed by generating and analyzing the Free Energy Landscape of the system. In order to prove the capacity of prediction of our computational method we have analyzed nine promoters of  Binding of specific proteins to particular sites in the DNA sequence is a fundamental issue for gene regulation in molecular biology and genetic engineering. A deep understanding of cell physiology requires the analysis of a plethora of genes involving characterization of their promoter architectures that determine their regulation and gene transcription. In order to locate the promoter elements of a given gene, experimental determination of its transcription start site (TSS) is required. This is an expensive, time-consuming task that, depending on our requirements, could be simplified using computational analysis as a first approach. Nevertheless, most computational methods lack a physical basis on the protein-DNA interaction mechanism. We adopt here this strategy, by using a simple model for protein-DNA interaction to find TSS in a bunch of cyanobacteria promoters. We make use of physical tools to characterize these TSS and to relate them with biological properties as the relative strength of the promoter. Our study shows how a model based on a coarse-grained description of a biomolecule can give valuable insight on its biological function. Transcriptional regulation is the main mechanism for gene control in prokaryotes. In order to adapt optimal protein expression to nutritional and environmental conditions, a cascade of transcriptional regulators works as signal transducers determining the accessibility of RNA polymerase to bacterial promoters. In the last years, high throughput approaches have been confirmed as powerful tools for a better understanding of the regulatory networks that govern key aspects of cell physiology, such as the mechanisms leading to pathogenesis or the acclimation to xenobiotics and hostile environments, among others However, successful transcriptome sequencing requires the generation of comprehensive transcriptome profiles that rely on the isolation of a sufficiently large number of reads to detect those biologically relevant transcripts, that represent a relatively small proportion of the cDNA library Anabaena PCC 7120.Therefore, computational methods emerge as valuable complementary approaches for prediction or further validation of high throughput results Cyanobacteria are the only prokaryotes able to perform oxygenic photosynthesis, being key contributors to Anabaena PCC 7120 contains 7,211,789 base pairs (bp) and 6,223 genes organized in a 6,413,771 bp chromosome and 6 plasmids Anabaena PCC 7120 has been used for long time as a model for the study of prokaryotic cell differentiation and nitrogen fixation Anabaena together with the analysis of transcriptome variations resulting from the adaptation to nitrogen stress have provided a holistic picture of this complex process The genome of i.e. local long-lived transient openings in the DNA strands) has also been extensively reported to correlate with several DNA functions, such as the recombination rate, single nucleotide polymorphism, DNA replication or gene transcription The problem of protein-DNA recognition is a widely debated issue, yet far to be fully understood. In this sense, it has been widely reported how the physical properties of the DNA chain result in key functional consequences in this process. DNA local structure highly influences some transcription factors (TFs) binding Despite the lack of consensus on whether PBD model is suitable for predicting protein binding sites Anabaena PCC 7120. We have analyzed the simulations outputs and built systematically the relevant macrostates of the system. In every case, our analysis algorithm finds the TSS as one of these states, yielding in addition thermodynamic parameters  that allow their physical characterization and thus further biological discussion. In this regard, our method arises as a complementary tool that, from physical principles, finds protein binding sites (we focus on TSSs) and characterizes them, allowing to discuss the strength -in terms of RNA production- of such sites, something not achievable by statistical methods. Remarkably, in this case the base pair sequence is the only previous information required. Thus, our numerical outcomes are independent numerical predictions to be confronted with previous or future experimental results.In order to prove the capacity of prediction of the computational methods developed in We base our model on a modification of the PBD model hain see . The onlstacking interactions. The election is the anharmonic potential The potential The original PBD model uses Morse potential for the intra-base pair interaction. Nevertheless, a successful modification includes an entropic barrier which accounts for solvent interactions with open base pairs Inspired on the one-dimensional diffusion stage of DNA-interacting proteins This potential creates a classical field composed by a sum of gaussian wells centered at each base , while four of them exhibit multiple TSSs  We have used the complete model (chain and particle) to analyze nine promoter sequences comprising As it has been pointed out in several studies, the PBD model by itself has been successfully used to analyze promoter sequences, finding protein binding sites where bubbles form with high probability, so allowing the identification of TSSs or the TATA-box It is also remarkable that the opening probability is not strictly related with the A-T content of the local sequence. Although it is clear that long A-T stretches form “softer” regions in the sequence that can open easier, this intuitive argument does not necessarily applies always. The interplay between the sequence and the dynamics is much more complex. The nonlinearity in the Hamiltonian, the long-range cooperativity of the model and the disorder of the sequence revealed in its heterogeneity affects directly the equilibrium and dynamical behavior of the model, being essential to understand the actual breathing dynamics of DNA, as it has been pointed out in previous studies Anabaena PCC 1720.Interestingly, besides the peaks centered on the TSSs, other regions exhibit high probability to form bubbles. Many of these peaks correspond to typical regulation sites of bacteria, such as those located at In order to analyze the sequences in a more systematic way we apply the FEL analysis described in the methods section. This algorithm allows us to define the most relevant states in the dynamics characterizing them from a quantitative point of view. So far, we have shown which regions in the promoter sequences exhibit a higher probability to form bubbles and to be visited by the particle. Nonetheless, these magnitudes give just qualitative information, as the average do not inform about the importance of opening events in the system. The real interest of our model and method is the possibility of giving quantitative measures about the “strength” of the different sites in the sequences, specially interesting in those promoters with several TSSs. Each site can be characterized by the thermodynamical magnitudes calculated from the FEL landscape analysis.We present together the data extracted from the simulation and analysis methods in To illustrate the FEL, Groups of basins separated by barriers lower than In most cases, the most populated macrostate, and thus the most stable one, coincides with an excitation in the TSS region. Other non identified sites also suppose very populated macrostates, suggesting the possibility of additional regulation sites as it is discussed in next section. Our method arises thus as a powerful tool to complement experimental results, providing additional physical information about the relative importance of these sites in regulation processes.Anabaena PCC7120, allowing the detection and characterization of the TSSs.In this work, we propose the use of a coarse-grained model for protein-DNA interaction to analyze promoter sequences, allowing the detection and characterization of protein-binding sites (we focus on the TSS). The proposed model is based on physical principles and inspired on a relatively simple idea: certain DNA-interacting proteins (as RNA polymerase) couple their binding to DNA bubble dynamics. Due to this, we base our model on a PDB representation of the DNA chain -having been proven to reproduce DNA bubble dynamics successfully- and couple it to an additional degree of freedom representing the protein. In the framework of this model and by using a free energy landscape analysis, we have studied promoters of Upon genome analysis and TSSs detection, high-throughput approaches, such as proteomics, are commonly used, resulting in an enormous amount of data in a relatively short period of time. However, analysis of raw data to end up in genome annotation or TSSs mapping is a demanding, time-consuming task, necessary for taking advantage of this information that may delay a more detailed analysis of specific issues. Among the large variety of these methods  populated macrostates as we have defined them . The casfurA, conR or nifB where very populated macrostates appear aside from the discussed TSSs. Although we do not exclude the possibility of false positives, these macrostates may be related with unknown regulatory regions. Thus, our results suggest further experiments to search possible new relevant activity regions. Moreover, additional TSSs might appear if studied under different culture conditions, revealing the complexity of transcriptome profiles even in the case of simple organisms such as bacteria. To finish, we have already mentioned studies discussing the influence of bubble formation on certain DNA-binding proteins aside from RNA-polymerase Anabaena PCC 7120.We have compared our numerical results to the existing experimental ones on TSSs positions and intensities. Nonetheless, it is important to note that our method identifies additional relevant regions of the promoters that have not been experimentally probed yet. We shall mention the cases of promoters Anabaena PCC 7120 has been shown to be an ideal experimental system to probe our numerical method. As it has been displayed, our results agree current experimental knowledge and propose possible new relevant activity regions. However, the model can be applied to the study of promoter sequences in many other organisms. Being the identification of protein binding sites in promoter sequences a key problem to understand and control regulation in biochemical and biotechnological processes, our methods appears as a powerful complementary tool in this scientific endeavor.Text S1This file contains the following information: (1) Explicit Langevin Equations for the model. (2) List of used parameters. (3) Further details on the analysis algorithm  Supplementary figures (dendrograms for the promoters not shown on the manuscript).(PDF)Click here for additional data file.


The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with  Translation of our increasing knowledge of brain signals into treatment of patients with brain damage or with disconnections between brain and body requires the ability to read and transmit information bi-directionally between brain electrodes (or other probes) and a neuroprosthetic processor. This is the realm of brain-machine interface (BMI) and brain-computer interface (BCI), primarily distinguished by whether connecting to a relatively limited processor maintained on the body for managing a prosthetic or connecting to an external computer for the purpose of direct communication between the brain and a computer. It has been suggested that in the upcoming years, it will be important for neurosurgeons to understand and integrate BCI technology and its clinical applications into their field .Currently, most of the algorithms utilized in BMI and BCI are based on low-dimensional interpretations of brain signals, typically based on rate determinations integrated over hundreds of milliseconds (ms) or seconds. By contrast, we are developing biomimetic brain models (BMMs) that attempt to replicate some of the attributes of brain signaling-specifically the spiking activation of the individual neurons. This increases the bandwidth requirements for communication since we now have the possibility of utilizing all the information from as many neurons as can be recorded (currently order 100) with firing rates of over 100 Hz. Learning can be incorporated into the BMM. This allows the hybrid BMM brain system to produce a coadapting symbiotic relation where both the brain and the BMM learn at the same time and adapt to each other .in vivo [Using data from a non-human primate, we are developing a test bed for the development of BMI/BCI neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of neuronal systems. We have implemented BMMs capable of replicating many experimental paradigms, such as sensorimotor learning experiments –5 or celin vivo ,8, incluin vivo . We havein vivo . Here wein vitro with a computer or analog device to simulate dynamic processes such as membrane or synaptic currents. However, scaling up the system to the next level, where a brain neuronal network is connected to biomimetic neuronal network model, posed a more challenging task.The proposed system is somewhat analogous to dynamic clamp , which iLarge-scale spiking neuron models of brain function are developed using neural network simulators such as NEURON, NEST, and GENESIS . Existinin vivo neurophysiological data source and a BMM that is simulating a network of spiking neurons. We specify the requirements of the system, detail the proposed design, and offer a prototype implementation following the design. The prototype links the PLEXON Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from mi-croelectrodes implanted in a live subject, to a NEURON-based BMM of sensori-motor cortex that controls a virtual arm. Our implementation achieves low-latency interconnection between the data source and the spiking neuronal model.In this paper, we address these issues by proposing a design and a prototype implementation for a network-based interface between an Since BMI systems also communicate via a processor, we will use the shorthand BCI to cover both BCI and BMI systems. In this section, we start by specifying the requirements of an interface system between neurophysiological data sources and BMMs. As discussed in the ‘Background’ section, this type of interface, which is not currently available, would be required to leverage the benefits of realistic large-scale spiking network models in BCI systems. We subsequently propose a generic or abstract design for a network-based system that meets the specified requirements. In the last subsection, we provide an example prototype implementation of the proposed generic design, which includes a more technical description of the different elements involved.Generally, BCI systems must support three functions: data acquisition, data processing, and prosthetic control. In this way, system requirements of this study are similar to previous work in this area. In our case, the design must support the following generic functionality: (1) collection of empirical neurophysiological data from a live subject-person, monkey, or mouse-facilitated by data acquisition hardware; (2) delivery of collected data from the sources to the appropriate processing modules via network environments such as a local area network (LAN); (3) extraction of relevant information from the raw empirical data; (4) feeding of data into a BMM spiking neural network simulator, here NEURON ; and (5)In the offline processing mode, all data delivered should be fed to the BMM without real-time constraints. Hence, all the data delivered should be kept in a queue so that the BMM can use the data as required. On the other hand, in the online processing mode, data delivery and simulation execution must meet real-time requirements. The BMM can run slower or faster than data acquisition, which occurs in real time. If the BMM runs faster than the generation of empirical data, it should wait for new input data to be received. If slower, the data delivered to the BMM must be partially discarded. in vivo data sources with simulator-based neuronal network models, and more specifically, of satisfying the system requirements outlined in the previous section. We propose an abstract modular design capable of interfacing in silico interface modules, interconnection modules, and in vivo interface modules. The in vivo interface modules allow the system to interact with the data acquisition systems. The in silico interface modules allow the system to interact with the BMM, set the execution configuration parameters, and provide tools for model optimization and data analysis. The interconnection modules manage the information exchange between the in vivo and in silico interface modules. The design has three data flows. First, data collected from the subject via data acquisition hardware flows into the system through the in vivo interface modules. The interconnection modules then process and send this data to the in silico interface modules, where the data is fed as input to the BMM simulator. In the second data flow, simulation results are fed back into the system via the in silico interface and interconnection modules and are used to control a prosthetic device through the in vivo interface modules. The third data flow propagates the user configuration data to other modules in the design. The user configuration may include some important parameters for the interconnection of in vivo experiments and BMMs, such as the data transmission protocol and data processing options. Though the proposed design deals with interfacing to physical prosthetic devices [The generic design is composed of  devices , its intThere are many ways to record brain activity from a live subject. Here we focus on electrophysiology techniques such as single-unit recordings, which measure the changes in voltage of a single neuron; and local field potentials (LFPs), which provides a measure of the synaptic currents in a volume of tissue and have been previously used as input to a BMM . Since dThe raw data  from the DIM may be fed directly into a BMM via the BMM simulator interface module (SIM) or may require additional operations to extract meaningful information, where the definition of ‘meaningful’ will vary depending on the destination of the BMM. The DPM is responsible for these additional operations. For example, the DPM may conduct filtering operations to the input data or perform operations to the results returned from the BMM simulation in order to convert them into commands that the prosthetic device understands.incremental control  and point-to-point (specification of absolute endpoints).This module delivers the commands converted from the simulation results as inputs to the prosthetic device. The type and format of the commands as well as the communication protocol required to transmit it will depend on the specific prosthetic device employed. Two examples of control command types for a robotic arm, a common type of prosthetic device, are The SIM provides an interface to a BMM simulator to interact with the rest of the system. The SIM is responsible for providing bidirectional communication between the simulator and the data communication module (DCM), sending data processed by the DPM as input to the simulator, and delivering simulation results to PIM through the interconnection modules.in vivo interface modules (DIM and PIM) and the module responsible for interfacing with the simulator (SIM) in real time. To minimize transfer latency, dedicated connections or LAN is recommended. The goal of DCM is to provide real-time data transmission between the in vivo and in silico interface modules. Prior to transmission by the DCM, data is processed by the DPM.In the design, it is critical to enable the transfer of data between the The MOM provides optimization functions that interact with the simulators during execution time, such as cell distribution methods for efficient balance and parallelism of large network models .Some simulators have their own model analysis and visualization tools that users are already familiar with. However, the MAM allows the inclusion of additional ones, including simulation performance measurements such as CPU and memory usage. Users may access such results by calling functions provided by the MAM from their simulation code.The MCM provides an easy-to-use configuration interface for setting parameters in all the modules in the design. For example, the user can select the most appropriate data communication protocol or data processing algorithm for a given experiment.In this section, we introduce an example prototype implementation  of the pWe tested the prototype implementation using a spiking neuronal network model of sensorimotor cortex ,4. The mThe model is able to provide two types of proprioceptive stimulus to the ES cells: through the P cells for continuous stimuli or through NetStim spike generator units with location (NSLOC) for discrete stimuli. P cells are implemented as integrate and fire neurons, and therefore include cell dynamics and internal variables, such as membrane voltage. When a spike is received from the external data source, an action potential is artificially triggered in the corresponding P cell. P cells also potentially allow for continuous stimuli inputs by modulating its membrane voltage over time. NSLOC units are simple spike generators, known as NetStim in the NEURON environment, with no internal dynamics. The user can determine its firing rate or, as in our case, the specific times when spikes should be generated. In our model, we have extended NetStim to have an x, y, z location (thus the name NSLOC) in order to facilitate distance-based random connectivity.For the evaluation, two prerecorded monkey spiking data files from macaque primary motor cortex (M1) and dorsal premotor cortex (PMd) are fed to the BMM. The prototype implementation supports BMMs with continuous P-based and discrete NSLOC-based inputs. Thus, depending on the input cell type chosen, the discrete multiunit activity (MUA) values from the prerecorded spiking data are either processed by the binning operation as explained below for P cells or delivered directly as discrete input through the NSLOC units. P cell is a simple point process cell, so the binned input increases the voltage in the P cells, which makes the P cells fire once a threshold is reached, whereas NSLOC units generate spikes to corresponding ES cells at the times specified in the inputs.www.plexon.com). In the prototype implementation, DIM retrieves spikes from the Plexon MAP server by means of the synchronous API such that the Plexon MAP server notifies the DIM that spikes are available. The DIM then retrieves spikes as a chunk.Plexon is a commercial tool for system neuroscience research. It collects signals from microelectrodes implanted in the brain of a live subject and provides MATLAB APIs for user software to connect with the Plexon system and to retrieve signals from it. The signals consist of channel number, unit number, signal type , timestamp, and waveforms. The Plexon MAP server pulls signals from a data acquisition device and makes the signals available periodically . A user software can get the signals synchronously or asynchronously depending on APIs used . The binning operation sums the number of spikes per microelectrode or channel for a given period of time, which leads to a spike frequency value per channel per bin. The PLEXON system acquires 96-channel MUA data recorded by the microelectrodes implanted in the monkey’s brain. Spikes from each channel are sorted according to their waveform shape into up to 4 units , with unsorted spikes in unit 0. Additionally, the system sometimes reported simultaneously occurring signals (sync spikes) on multiple electrodes within a very short interval ). Given that there are many noise sources, such as small displacements in the electrodes, we hypothesized that such unsorted or sync spikes can be regarded as noise that needs to be filtered out. Since the binning and filtering operations require a certain amount of computation, it is important to be aware of where they are executed as this may disturb real-time processing. Depending on whether the operations take place in the DPM of the client or the server, our implementation provides two options: the Heavyweight Client mode (HWC) and the Lightweight Client mode (LWC). In the HWC mode, the operations are conducted in the client’s DPM while the server’s DPM just receives processed data from the client, and simply forwards them to the SIM. In the LWC mode, the client’s DPM just sends the raw spiking activity to the server’s DPM, where the binning and filtering operations are conducted.For discrete input simulations, the server’s DPM performs the itemizing operation, instead of the binning operation, in both of the HWC and LWC modes, before pushing a chunk of spikes to the queue. The itemizing operation divides the chunk of spikes into groups, each with a fixed number of spikes  for efficient queue management. For example, when the fixed number of spikes for a group is 20 and the DPM receives a chunk which has 30 spikes, the first 20 spikes in the chunk are pushed in the queue as a group. Then, another group composed of the remaining 10 spikes and another 10 spikes being assigned an arbitrary value  is queued. A counter is used to track the number of real spikes so that the arbitrary valued spikes are never used. The time required for the itemizing operation is proportional to the size of chunk.in vivo recording system and may be reusable for different acquisition systems. Spike information from the Plexon MAP server is converted into the designed data format and delivered from the client to the server. The data format consists of a header and a payload field. The header field indicates the type of data packet . The payload field contains the binned data and the binning window number for the P-based model; and the timestamp, channel ID, and unit ID of each spike, for the NSLOC-based model.The DCM supports two data transmission protocols: the User Datagram Protocol (UDP) and the Transmission Control Protocol (TCP). UDP is a connectionless transmission protocol that does not guarantee that packets sent reach the destination or that they are delivered in order . On the In order to feed the BMM running in NEURON with continuous or discrete input, inter-process communication between the SIM and the BMM was implemented with an existing first-in, first-out (FIFO) queue facility in Python. This queue provides easy-to-use operations such as putting/getting items into/from the queue and locking. For online mode simulations, the queue in SIM supports two cases: either the simulation is slower or faster than the rate of input of spiking data. The SIM assumes that input data is coming in real time. Therefore, in online mode, to recognize if the simulation is faster or slower than real-time, the SIM compares the timestamp in NEURON with the most recent input. If the difference is greater than a constant value, latency requirement (LR), which is fixed by user, the simulation is slow, so the SIM discards an item in the queue and executes the comparison again. Otherwise, the simulation is not slow and the item is processed. Discarding spikes allows the simulation to catch up with the data rate as less spikes are processed. We evaluate the consequences of this in the ‘Results’ section. For offline mode simulations, LR is set to a value bigger than simulation time .To deliver input from the queue to the BMM, we utilized a callback function that is added to the NEURON event queue and invoked at a specific simulation time by NEURON. When the function is invoked, it fetches data in the queue and then feeds the data as input to the BMM. Since the SIM queue contains only valid input data , the callback function just pulls an input from the queue (if one exists), feeds it to the BMM, and moves the simulation forward. When the queue is empty, the function waits for data to arrive.The MCM provides an initial handshaking protocol to establish connections between the client and the server and between the client and Plexon MAP server. Through the initial handshaking protocol, the client and server share configuration information such as the transmission protocol and where the binning operation takes place. Therefore, users only need to change parameters or flags in the configuration file located on the server side. Further details on the parameters found in the configuration file and how they can be modified by the user are described in .in vivo and in silico labs, and connected via LAN. The Plexon server emulated the in vivo data acquisition on the same machine where the client (DIM) ran. It retrieved spiking activity from prerecorded Plexon files. For demonstration of the prototype implementation, the server and client ran in different machines connected to each other via a LAN environment. We constructed two system environments: ENV1 and ENV2. ENV1 is composed of two identical machines to conduct fair comparison of performance between the HWC and LWC modes. ENV2 consists of machines with better performance than those in ENV1 and arranged in a realistic research environment, with the machines located in two distinct research laboratories, the Round-trip time (RTT) was measured between two physical machines running the client and server in ENV1 and ENV2 varying the size of data transmitted  according to the processing modes and datasets used in later experiments. The RTT was measured with the Linux Ping command using S option for specific data sizes. The RTT serves as a baseline to analyze whether or not the prototype implementation achieved expected transmission time. As the size of transmitted data increased , RTT also increased in both ENV1 (from 0.71 to 0.87 us) and ENV2 (from 0.98 to 1.55 us). RTT in ENV2 was greater than in ENV1 for all data sizes, with the largest difference observed for a data size of 1,032 bytes (1.55 vs 0.87 us).For the experiments, we used two datasets recorded by the Plexon system, which were extracted from a live macaque monkey’s M1 and PMd regions for 100 s of real time. The macaque monkeys were performing a center-out reaching task , with her right arm attached to exo-skeletal robotic manipulandum (KINARM), corresponding to the initial manual training described in . The aniin vivo data source and the simulator-based sensorimotor cortex BMM under the principles of the proposed design. Using the two datasets on the two different computational environments, we measured the overhead caused by modules between the NEURON simulator and the Plexon SoftSer-ver. In these experiments, the overheads included the latencies for the following operations: (a) reordering, (b) binning (for continuous input) or itemizing (for discrete input), (c) filtering, (d) transmitting binned or chunk of spikes, (e) pushing binned (or chunk of) spikes to the queue, and (f) pulling from the queue to feed the BMM.We conducted a series of experiments to verify that the prototype implementation succeeded in enabling the interaction between the n chunks of spiking data, the latency in the binning operation is calculated as the time interval between the reception of the nth chunk of data by the DPM and the completion of binning computation. The transmission latency is the time required to deliver a chunk of spiking data or a binned spikes set through the network. For the latency measurement, we used a simplified version of the BMM; while accepting continuous input through P cells or discrete input through NSLOC units, P cells and NSLOC units were disconnected from the ES cells to prevent model computations from taking place. For both the offline and online processing mode simulations, we ran the full version of the NEURON model for 100 s of simulated time. We also measured the elapsed time, which corresponds to the execution time of the run phase in NEURON. Note that experimental results on ENV1 were about the same as the ones on ENV2 regardless of which transmission protocol between UDP and TCP was used. Hence, for the latency measurements, only the TCP results on ENV2 are presented in this paper. In terms of execution time and spikes generation, we only report the results from the HWC mode simulations, given that the LWC mode simulations results were almost identical, both in ENV1 and ENV2.Assuming the binning operation requires Due to the difficulty of synchronizing the time in two distinct machines, we measured overhead by asking the client and the server to send very small-sized messages before and after each operation to a time collection server running in another machine in the same LAN environment. Therefore, considering the overhead caused by the measurement process, the prototype implementation achieved reasonable transmission times (<1 ms). As shown in The size of a single queue item for the NSLOC-based simulation is 496 bytes, composed of 20 spikes × 3 elements per spike × 8 bytes, 8 bytes for the number of spikes in the item, and 8 bytes for the serial number of the item. For the P-based simulation, the size is 776 bytes (96 channel × 8 bytes + 8 bytes for binning time). However, the pushing and pulling operations in the NSLOC-based simulations take significantly more time than in the P-based ones. In the prototype implementation, there are two ways for the queue to recognize whether the simulation is faster or slower than the real time. First, the queue checks how many items it contains. If it has less than two items, it assumes that the simulation is running in real time. Otherwise, the queue carries out an additional test to check the simulation: it retrieves the current NEURON time through a shared variable, Python LOCK-based, and calculates the difference between the latest time in the queue items and the current NEURON time. If the difference is greater than LR, the simulation is considered to be running slower than the real time. Otherwise, the queue assumes that the simulation is running in real time. For the latency measurement, we used a simplified model; hence, the queue in the P-based model is likely to have a single item within a binning interval, which prevents the queue from fetching the current NEURON time. However, in the NSLOC-based model, it is rare that the queue has less than two items, even for the simplified model, which increases its overhead for pulling/pushing from/to the queue.In The prototype implementation achieved transmission between the data source and the BMM with low overhead (<3.5 ms). This was achieved through the use of basic Java-based MATLAB and Python-based libraries. Python threading is limited by its global interpreter lock . Hence, The prototype implementation of the design currently enables the data flow from the Plexon MAP server (DIM) to NEURON (SIM). Depending on the data acquisition devices and BMM the spiking network simulators used, it might be necessary to modify the implementation of modules to deal with different types of input and output, and different libraries. For example, if LFPs are to be used as input, then DPM and DCM could be extended to deliver this type of signal. Nonetheless, all operations were modularized to maximize module reusability in other contexts.There are several inter-process communication methods available that support data streams, including sockets, message queueing, and pipes. The rationale for choosing the client–server model using TCP/IP socket communication for our prototype is its suitability for research in neuroprosthetics, where the different systems are typically interconnected in a LAN environment. The TCP/IP socket provides easy-to-use interfaces between different platforms (Windows and Linux) as well as different programming environments (MATLAB and Python), which are used in the prototype implementation. It is yet unclear what communication methods will be required for working neuroprosthesis of this kind in the future, but similar setups are a feasible option, for example, if the working neuroprosthesis is being used in a clinical environment .With respect to TCP and UDP transmission comparison, it is generally known that the TCP transmission is slower than UDP . Thus, pIn the prototype implementation, the online mode simulations generated significantly less spikes than the offline mode ones since a large number of input spikes were discarded by the queue in the SIM. The queue is aimed at achieving real-time simulations while minimizing the discarding of input spikes but does not consider the type of spikes being discarded or the effect this has on the network. In addition to the queue, we devised an input spike filtering operation, based on the assumption that unsorted spikes or spikes firing simultaneously within a short sync time window  were the result of noise in the system. As shown in in vivo data into a parallelized BMM.Our design does not manage the computational resources required by the BMM simulators. This contrasts with previous methods, such as the Cyber-Workstation, which includes the parallel processing capabilities required for the concurrent execution of multiple models demanding large amounts of computing resources . The desIn the present example, we assumed that the recorded MUA data represented pro-prioceptive information fed directly from the monkey’s brain to a corresponding neural population (P) for continuous input, or to spike generators (NSLOC) for discrete input to the BMM. These proprioceptive signals, which could potentially be recorded from M1, PMd or posterior parietal cortex (PPC), would represent the external state of the prosthetic device  ,37. HoweRecently, communication between a NEURON-based BMM and a physical robot arm was demonstrated in a LAN environment . The outin vivo data source, a simulator-based BMM, and a prosthetic device. The design specifies a set of modules and data flows among them. We implemented a prototype of the design, enabling interaction between the Plexon MAP server and a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation showed that it supports real-time simulations with a low communication overhead. Interconnection between in vivo experiments and simulator-based BMMs opens the door to a new range of experimental paradigms in brain research. For example, the design could underlie hybrid experimental test beds combining brain activity and a BMM to develop new forms of brain-machine interfaces where the real and simulated brain coadapt and work together [We proposed a design that enables communication between an together ,36 in ortogether ,39.


Learning and memorizing past information are critical features of neural networks. Several models have been proposed to explain the storage and retrieval of different kinds of information ,2 . ThesWM is most probably realized in the prefrontal cortex (PFC). To capture the diverse dynamics observed in PFC during WM experiments, we choose to model WM by a reservoir computing network . BasicalThe hippocampus, especially its CA3 area, is believed to play a major role in LTM formation on time scales of hours to days. We model its auto-associative capabilities by the well-known idea of cell assemblies . AccordiAnalyzing the capabilities and characteristics of the proposed system, we train the reservoir to perform certain arithmetic operations within different contexts. During operation, the cell assembly network learns to detect and remember associations between context signals and computed results. This enables the system to remember results of earlier calculations beyond the WM capacity and to reuse them for later tasks. Thus, the system is able to perform computations based on short-term memories of the operands as well as to overcome the limits of the short-term memory by storing computed results into the long-term memory and transferring them back for further usage.