Work with the discrete sequence production (DSP) task has provided a substantial literature on discrete sequencing skill over the last decades. The purpose of the current article is to provide a comprehensive overview of this literature and of the theoretical progress that it has prompted. We start with a description of the DSP task and the phenomena that are typically observed with it. Then we propose a cognitive model, the dual processor model (DPM), which explains performance of (skilled) discrete key-press sequences. Key features of this model are the distinction between a cognitive processor and a motor system , the interplay between these two processing systems, and the possibility to execute familiar sequences in two different execution modes. We further discuss how this model relates to several related sequence skill research paradigms and models, and we outline outstanding questions for future research throughout the paper. We conclude by sketching a tentative neural implementation of the DPM. Many of our daily activities are testimony to the possession of motor skill. One may think of riding a bike, lacing a shoe, or writing one's signature. Accordingly, within the fields of cognitive psychology and cognitive neuroscience ample research has been devoted to understanding how the brain represents and controls motor events. This venture is hindered, among other things, by a lack of direct conscious access to motor processes, and by the considerable time that the acquisition of motor skill typically takes. Nevertheless, various experimental tools have been developed over the last decades from which the workings of motor control—and its constant interaction with higher-order cognition—can be inferred with surprising detail. These experimental tools may be classified within two major experimental paradigms, motor adaptationdiscrete sequence production (DSP) task  explicit instruction, explicit trial-and-error discovery and more elaborated hypothesis testing, or implicit detection of regularity. As most, if not all, of our goal-directed actions involve some kind of sequential structure, the human capacity to acquire sequential motor skill has been a topic of extensive research over the last decades. This research has led to a large variety of laboratory sequence acquisition tasks that typically involve finger-to-thumb opposition movements, finger presses on response boxes or key boards, movements of the whole arm, isometric forces, or oculomotor movements. The purpose of the current article is to provide a comprehensive overview on the contribution of one of these tasks, the  Verwey, , to our The current review, then, is narrow in focus in the sense that it centers on work with the DSP task. Other sequence learning tasks and their major findings will not be discussed in detail  was proposed already by Verwey (In the next section we will (a) provide a description of the DSP task, (b) situate the DSP task within the larger domain of motor sequence learning in order to identify both its strengths and limitations, and (c) provide an overview of the typical phenomena associated with the DSP task. Overall, this section thus constitutes a sort of user's manual of the DSP task. In the third section, we will present the framework. This so-called y Verwey . HoweverSn to denote the n-th stimulus of a sequence, Rn to denote the n-th response in the sequence, and Tn to denote the RT associated with Sn. Sometimes these RTs are referred to as inter-key-intervals (IKIs) but this only holds in the typical case when response-to-stimulus-intervals are 0 ms.The DSP task involves participants resting four to eight fingers on the designated keys of the keyboard Figure 2. A simimotor chunks are assumed to represent a limited number of responses that can be selected and executed as if they are a single response in a control hierarchy  to develop the building blocks; These so-called hy Book, . FollowiSecond, by counterbalancing the fingers of individual participants across the sequential positions of the sequence, finger-specific effects at individual sequential positions are ruled out because each of the fingers contributes equally to the RTs at each sequential position. For example, when participants are using the D, F, G, J, K, and L keys on a keyboard, one participant may practice the 6-key sequence KFGDJL, the next participant the 6-key sequence LGJFKD (each key is shifted rightward relative to the first participant), and so on. This counterbalancing procedure also implies that the same sequences can be used as familiar and as unfamiliar, control, sequences so that RT differences between familiar and unfamiliar sequences are not related to inconspicuous differences in keying order, but rather are clean indicators of the underlying control processes.We consider research with the DSP task as a way to study the building blocks of more complex behavioral patterns that make up everyday behavior , the task differs from the DSP task that focuses on fast and effortless skill acquisition. Still, as will be elaborated on below, the model that Hikosaka et al.  events. The regularity between events is not explicitly conveyed to participants beforehand, and participants are often picking up on the regularity (as shown by performance measures) without being aware of it. Hence, in contrast to the DSP task, the SRT task mainly involves an implicit learning paradigm and does not employ discrete sequences. More importantly even, the SRT task does not typically involve motor chunking , and the alleged use of motor chunks. Earlier research employed much less practice. For example, Restle  S-R learning. This differs from many earlier discrete sequence learning studies, in which participants were asked to explicitly learn the sequences after which their execution was triggered by either a simple go-signal  distinct phases of discrete sequence skill, and the spontaneous segmentation of longer sequences, (b) distinct coding systems that underlie sequence representations, and (c) the development of explicit sequence knowledge.initiation and is reflected in T1. In case of a choice RT paradigm such as the typical DSP task, T1 is assumed to involve the selection and preparation of the sequence. As Figure 1, as the slow first response can be observed even when a short, random series of key-presses is carried out  in the sequence  concatenation: the processes that allow distinct motor chunks within a sequence to be executed in rapid succession as smoothly as possible. The relatively slow response halfway through, then, is assumed to index the transition from one motor chunk to the next, and can be referred to as the concatenation point  show a relatively slow response half way through the sequence  the relatively long RT that is assumed to index the concatenation phase has been found to occur at different sequential locations for different persons. Consequently, across a group of participants a single long RT cannot always be easily observed , participants are typically observed to segment the sequence at the location of the pause when subsequently the pause is removed. This suggests that the position where concatenation occurs is determined by the pause position during practice  against the others  against its directly surrounding neighbors, and look for a significantly longer T that can subsequently be labeled as the concatenation point. However, this method relies on assumptions that during training chunk boundaries are relatively static and that, eventually, short chunks are not combined into larger chunks. This method is relatively insensitive to measuring how the chunking structures develop with practice.Second, Jiménez et al.  proposedThird, Wymbs et al.  modeled Several studies have investigated the type of representation that forms with practice in discrete movement sequences. The general notion is that initial sequence execution relies on effector-unspecific sequence knowledge  and that with practice execution becomes increasingly dependent on effector-specific knowledge  Bapi et al.  showed tIn sum, there is now substantial reason to believe that sequential movement skill involves several types of representation. Some involve a slowly developing motor code , while other representations probably code movement patterns in terms of more rapidly developing spatial reference systems , it may also load, one by one and before execution, a limited number of individual responses into the motor buffer. This motor buffer is assumed to be a part of working memory  little cognitive processor involvement is required when motor chunks are executed by the relatively autonomous motor processor, and that (b) with practice the contribution of the cognitive processor may even be further reduced as entire motor chunks may become triggered by external stimuli as if they involve prepared reflexes  processes from the cognitive processor. Empirical support for this notion was provided by Verwey et al. , 2013, wTwo major issues for the DPM concern the justification for the assumptions of (a) two distinct processors instead of a single graded processing resource, and (b) a race between the two processors. We believe that justification for the two processor assumption comes from several findings. The first relates to the notion that action slips have been found to mainly occur at the decision points in an action sequence, where higher-cognitive involvement is required for adequate action selection  slows ongoing sequence execution, but this slowing is unaffected by the load of the selection process itself  various research paradigms. For example, Sternberg  suggesteSecond, the notion that different processors are racing to trigger a response in a familiar keying sequence fits well with the many indications that the execution of a movement sequence involves the simultaneous use of different codings  in response to just the first stimulus. Subsequent stimuli can be ignored and participants are said to be performing in the chunking mode. This mode can be envisaged as open-loop control in the sense that key-specific stimuli after the first are no longer needed .Verwey  noted thassociative mode develops with DSP practice. In this mode successive reactions are primed by the preceding responses but still require stimulus processing for actual execution—as would occur in SRT learning  and motor level. Its precise features will probably depend on the task requirements, the context, and the amount of practice.The reaction and chunking modes can be easily accounted for by the DPM (see below). The theoretical challenges concern the implementation of the associative mode, especially with respect to the representational level. It is generally accepted that representing sequential information may involve coding across the perceptual, cognitive, and response-based/motor levels  is generated on the basis of stimulus input (Sn) by the concerted action of the cognitive and motor processors. These processors may use a motor buffer that can temporarily hold representations that concern a limited number of responses. In the reaction mode, which is dominant with unfamiliar or random sequences, the cognitive processor processes sensory input and selects the appropriate response separately for each particular stimulus. Next, it puts the motor processor to work for the actual execution of the response. With repeated execution of the same sequence of events, associations develop between successive events. The resulting representation allows for response selection processes to be primed when they are executed in a familiar order on the basis of preceding events (associative mode). Moreover, when the representation becomes sufficiently strong at the motor level, it allows for the temporary activation of a short series of movements  as if they are loaded in a single step into a motor buffer. Next, the motor buffer content is read and executed by the motor processor. Because the motor buffer capacity is limited, the number of simultaneously prepared and executed responses is limited. Finally, the independence of the cognitive processor and motor processor allows a race between them in that the cognitive processor selects responses at the cognitive (“response selection”) level, and the motor processor triggers responses from the motor buffer.Figure The DPM forwards a number of testable predictions on the dynamic interplay between the different modes and the types of sequence knowledge acquired. For example, the model predicts that for participants without explicit sequence knowledge, the effect of a secondary task on executing a DSP sequence will vanish if key-specific stimuli after the first are no longer presented . The reason is that without explicit knowledge and external stimuli, the cognitive processor is no longer able to race with the motor processor, and thus never enhances skilled  performance. Additionally, if after extensive practice the chunking mode is prevented through, for example, introducing (auditory) stop-signals during a specific proportion of sequences within a block (requiring to terminate sequence execution), it can be expected that executing a familiar sequence in a single-stimulus condition is only better than executing an unfamiliar sequence for aware (and not for unaware) participants because their explicit knowledge still allows the cognitive processor to enhance performance beyond pure S-R translation. Furthermore, artificially slowing execution rate by using more complex responses will increase the presence, and contribution, of explicit sequence knowledge and/or the associative mode because there is more time to contribute. These and other (types of) predictions need to be addressed in future research.In our efforts above to situate the DSP task within the larger domain of sequence learning, we already anticipated a discussion about how the DPM relates to other work on sequence skill. Here we outline such a link, first, with respect to discrete sequence skill, and second, with respect to the models that stem from related sequence learning paradigms. This results in various issues for future research.not account for overlearned sequences  it distinguishes the associative and chunking modes of sequence execution (and thereby their respective literatures), (b) it is able to explicitly account for automaticity of skill by the relative autonomous execution processes of a motor system (motor processor and motor buffer), and (c) it allows for explaining the overall dynamic interplay between cognitive and automatic processes in daily life.In this section we discuss on the basis of cognitive-neuroscientific findings . With practice, sensorimotor cortico-striatal loops (SLs) gradually take over and enable both more automatic S-R translation and sequence based performance in close interaction with premotor and primary motor corticesPMC), while for the chunking mode the SMA is involved instead (SLSMA). In the chunking mode, an ALPRE−SMA loop may remain involved for the actual loading of motor chunks. Hence, besides building from the accepted distinction between the AL and the SL, we also propose functional divisions of both the AL and the SL.We explicitly distinguish the three modes in which sequences can be executed, and thus focus mostly on implementation and less on representation of sequence skill. Though this endeavor probably results in an oversimplification and a somewhat artificial separation of massively interacting networks  probably involves areas that are consistently related to spatial response selection, such as the premotor cortex (PMC), the parietal cortex and the prefrontal cortex (PFC)  and motor cortices. Various findings support this notion of activity shifts. First, Miyachi et al.  found thThe crucial role of the BG for motor chunking has become evident over the last decades. Studies on stroke . This notion also fits well with two studies by Verwey and colleagues who showed that the ability to segment long sequences into chunks is impaired in elderly  function for the BG in sequence skill. Specifically, as discrete sequence skill involves the activation by PFC/pre-SMA of particular sequence (motor chunk) representations laid out somewhere else in the brain , the effectiveness of this advance preparation can be assumed to require the temporary inhibition of execution processes. The BG are well-suited to moderate this process as they are involved in go- (cf. direct pathway) and no-go-signals , or when the chunking mode has been disengaged through experimental manipulations. This fits well with studies that relate both the SL and the PMC to implicit sequence learning in the SRT task  that striatum and PMC (possibly in concert with more posterior areas) define a functional loop that underlies the reaction mode from the moment that S-R translation becomes relatively automatic (cf. habit formation). In the case of the DSP task this would develop quite rapidly because of the high spatial compatibility of stimuli and responses. We further suggest (b) that a sensorimotor-PMC loop underlies the associative mode, and (c) that a sensorimotor-SMA loop underlies the chunking mode. The main distinction between the associative and the chunking modes may lie in the efforts of the BG to inhibit execution during the activation of  areas that contain relevant sequence representations. Besides generating predictions for future research, we believe that this tentative mapping of the DPM's execution modes on specific cortico-striatal loops will contribute to explorations on the biological plausibility of DPM.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cerebellar climbing fiber activity encodes performance errors during many motor learning tasks, but the role of these error signals in learning has been controversial. We compared two motor learning paradigms that elicited equally robust putative error signals in the same climbing fibers: learned increases and decreases in the gain of the vestibulo-ocular reflex (VOR). During VOR-increase training, climbing fiber activity on one trial predicted changes in cerebellar output on the next trial, and optogenetic activation of climbing fibers to mimic their encoding of performance errors was sufficient to implant a motor memory. In contrast, during VOR-decrease training, there was no trial-by-trial correlation between climbing fiber activity and changes in cerebellar output, and climbing fiber activation did not induce VOR-decrease learning. Our data suggest that the ability of climbing fibers to induce plasticity can be dynamically gated in vivo, even under conditions where climbing fibers are robustly activated by performance errors.DOI:http://dx.doi.org/10.7554/eLife.02076.001 The cerebellum (or ‘little brain’) is located underneath the cerebral hemispheres. Despite comprising around 10% of the brain’s volume, the cerebellum contains roughly half of the brain’s neurons. Many of the functions of the cerebellum are related to the control and fine-tuning of movement, and people whose cerebellum has been damaged have problems with balance and coordination, and with learning new motor skills.One of the roles of the cerebellum is to control a reflex known as the vestibulo-ocular reflex, which enables us to keep our gaze fixed on an object as we turn our heads. The cerebellum relays information about head movements to the muscles that control the eyes, instructing the eyes to move in the opposite direction to the head. This keeps the image of the object we are looking at stable on the retina.The vestibulo-ocular reflex is controlled by a circuit that includes Purkinje cells (which are the main output cells of the cerebellum) and climbing fibres (which originate in the brainstem). Any failure of the vestibulo-ocular reflex to fully compensate for head movements generates an error signal that activates the climbing fibres. These in turn modify the output of Purkinje cells, leading ultimately to adjustments in eye movements.However, Kimpo et al. have now obtained evidence that Purkinje cells can modulate their response to the instructions they receive from climbing fibres. Monkeys sat in a rotating chair while a visual object they were trained to track with their eyes was moved to induce errors in the vestibulo-ocular reflex. When the object was moved so that a bigger reflexive eye movement was required to stabilize the image, the activation of the climbing fibres in response to the error led to a change in the response of the Purkinje cells, as expected. However, when a smaller reflexive eye movement was needed, the error-driven responses of the climbing fibres did not alter the responses of Purkinje cells. Similar results were obtained using pulses of light to artificially activate climbing fibres and thus simulate error signals.The work of Kimpo et al. indicates that the cerebellum does not blindly follow the instructions it receives from the brainstem, but can instead modulate its responses to incoming information about performance errors. Further work is now required to identify factors that influence the responsiveness of the cerebellum: such information could ultimately be used to improve learning of motor skills and recovery from injury.DOI:http://dx.doi.org/10.7554/eLife.02076.002 The cerebellum is thought to implement a supervised learning algorithm, with the climbing fiber input to the cerebellum providing error signals that induce learning. According to this model, performance errors activate neurons in the inferior olive and their climbing fiber axons, which in turn trigger the induction of plasticity in the cerebellar cortex to produce adaptive changes in behavior . PreviouThe question of whether error signals carried by climbing fibers are the trigger for learning has been controversial, since the evidence from different studies has been inconsistent. Climbing fibers are activated by performance errors during a wide range of motor learning tasks  needs to be well calibrated. This calibration occurs through a form of motor learning that depends on the cerebellar floccular complex  . ClimbinPrevious efforts to determine whether these error signals in the climbing fibers are what drive VOR learning have been inconclusive. Over the course of both VOR-increase and VOR-decrease learning, the Purkinje cell simple spike output during the VOR is altered in a manner consistent with the induction of LTD in parallel fiber inputs that were coactive with the climbing fibers during training , 1985. AWe recorded from Purkinje cells in the floccular complex of two adult rhesus monkeys during VOR-increase and VOR-decrease training. An extracellular recording from a Purkinje cell provides simultaneous access to two distinct physiological signals: complex spikes, which provide a one-to-one readout of spikes in the single climbing fiber innervating the Purkinje cell ; and simipsiversive vestibular stimuli  at the parallel fiber-Purkinje cell synapses  or throufferents , leadingfferents . In contlearning , 1985. Hlearning .One approach we used to address this question was a trial-by-trial analysis of the correlation between activity in the climbing fibers and plasticity of the Purkinje cell responses during training. The activity of an individual climbing fiber encodes retinal slip in a probabilistic manner . ClimbinWe identified pairs of consecutive trials in which there was a spike in the climbing fiber on the first trial of the pair, but not the second trial (CF–No CF pairs), and calculated the trial-to-trial change in the simple spike response . LikewisThe tight, trial-by-trial correlation between the occurrence of a climbing fiber spike on one trial, and the change in Purkinje cell simple spike output on the next trial suggests that the activation of the climbing fibers is what is triggering the changes in Purkinje cell output during VOR-increase training. In striking contrast, in the very same set of cells, a trial-by-trial analysis during VOR-decrease learning revealed no correlation between climbing fiber activity and plasticity of Purkinje cell responses , 2013, tThe Purkinje cells did undergo gradual, adaptive changes in their responses over the course of the full VOR-decrease training session, which could be detected during the same ∼90-s VOR-decrease training sessions used for the trial-by-trial analysis . Across What could be preventing the error signals in the climbing fibers from inducing adaptive changes during VOR-decrease training? One trivial possibility would be that during contraversive vestibular stimuli there are simply not enough synapses active simultaneously with the climbing fibers to serve as a substrate for associative plasticity. This seems unlikely because there is robust activation of mossy fiber inputs to the cerebellar flocculus during both contraversive and ipsiversive vestibular stimuli , and a sRecent studies have highlighted the duration of the complex spike as a factor that may regulate the induction of plasticity by the climbing fibers. The complex spike consists of a large initial spike followed by a variable number of spikelets. When the number of spikelets, and hence the complex spike duration, is manipulated in vitro or in decerebrate animals , the proWe also considered the possibility that the length of the climbing fiber-triggered pause in simple spike firing may affect the potency of climbing fiber activity to induce learning . HoweverIt is conceivable that climbing fiber-triggered plasticity would be less effective at reducing Purkinje cell simple spike firing if the firing rate was already very low. Therefore, we compared the average simple spike responses during vestibular stimuli in the direction associated with elevated climbing fiber activity: ipsiversive and contraversive vestibular stimuli during VOR-increase and VOR-decrease training, respectively. For both, firing rate decreased relative to baseline during the training stimulus , but thiThe results of our trial-by-trial analysis suggest that error signals carried by climbing fibers contribute to the induction of plasticity during VOR-increase but not during VOR-decrease training. However, such correlational evidence cannot, by itself, establish causality, and the relationship between the short-term effects observed in the trial-by-trial analysis and long-term learning has not been established . Thus, tTo optogenetically stimulate climbing fibers, virus carrying ChR2 was injected into the inferior olive, and several weeks later, the climbing fiber terminals in the cerebellar flocculus were illuminated with blue light . ChR2 ex. If the climbing fibers were stimulated during the ipsiversive phase of the same vestibular stimulus, the VOR gain was higher than the vestibular-only control at the end of training , either unilaterally or bilaterally. These stronger climbing fiber stimulation paradigms induced bigger increases in VOR gain when delivered during the ipsiversive phase of the vestibular stimulus  are selectively impaired on VOR-increase but not VOR-decrease learning . That stThe results for VOR-increase learning provide a positive control indicating that our experimental approaches were able to detect climbing fiber-triggered plasticity, which is critical for interpreting the negative results from VOR-decrease learning. Previous, seemingly inconsistent findings about the role of the climbing fibers in cerebellum-dependent learning have been difficult to reconcile because different labs use different behavioral paradigms and different experimental approaches. In contrast, direct comparison of the VOR-increase and VOR-decrease paradigms provides a demonstration that the very same animals and even the very same cells could toggle between either a tight coupling or no apparent coupling between climbing fiber activity and the induction of plasticity, depending on the behavioral context of the specific oculomotor training paradigm. Thus, direct comparison of the positive and negative results from VOR-increase and VOR-decrease learning in the same animals and the same cells, using the same techniques, suggests that climbing fiber efficacy is actively regulated.Recently, there has been considerable interest in the possibility that the duration of the complex spike may regulate the induction of plasticity by the climbing fibers. In vitro, ethanol and neuromodulators such as norepinephrine can affect the duration of a complex spike and the induction of climbing fiber-dependent plasticity . HoweverAnother factor that may gate the efficacy of climbing fibers to induce synaptic plasticity is the level of inhibition: coactive inhibitory inputs can impair the induction of LTD at the parallel fiber-to-Purkinje cell synapses , but alsThe relationship between short-term, trial-by-trial plasticity and longer-term learning is not understood . Our resIn the trial-by-trial analysis for VOR-increase training, the change in Purkinje cell activity on trials following a climbing fiber spike was remarkably large, approximately 10% of the average firing rate . If the Nevertheless, the parallels between the changes observed on a single trial and the changes observed over longer time scales suggest that they could be related. In particular, the direction of the trial-by-trial changes in Purkinje cell responses during VOR-increase learning are consistent with those observed over the course of a brief, 90 s VOR-increase training period , red andOur results provide evidence for a novel component of the neural algorithm for cerebellar learning in vivo—namely, a gating of the ability of error signals in the climbing fibers to induce learning. Since previous work has shown that regulation can also occur at the level of olivary spiking, climbing fiber-triggered plasticity seems to be gated at two different stages—at the level of the olivary neurons responding to a performance error by either spiking or not spiking, and at the level of climbing fiber spikes either inducing or not inducing plasticity.Thus, the cerebellum appears to conditionally heed the instructive signals provided by the climbing fibers. Dynamic gating of the neural error signals controlling the induction of learning may be a general feature of neural learning algorithms, and merits systematic investigation in the cerebellum and other circuits. Defining the factors that influence the receptivity of the cerebellum to neural error signals will be critical for developing more sophisticated theories of learning, and for developing strategies to optimize the recruitment of plasticity to aid learning or recovery from injury.During experiments, monkeys were restrained in a primate chair with their head fixed to the chair via a surgically implanted head post. Horizontal eye position (relative to the head) was measured using the eye coil method  and diffThe vestibular stimuli were paired with a moving visual stimulus. The visual stimulus was provided by a high-contrast black-and-white pattern projected onto a tangent screen. Animals received a liquid reward for keeping their eyes within 2° of a small target in the middle of the visual stimulus. During VOR-increase training, the visual stimulus moved in the opposite direction from the vestibular stimulus at 15°/s . During Single-unit extracellular recordings were made from 10 Purkinje cells in the flocculus and ventral paraflocculus of two awake, behaving, male rhesus monkeys using platinum–iridium electrodes. Voltages related to neural activity were sampled at 50 kHz. The cells included in the analysis were all identified as horizontal gaze velocity Purkinje cells, based on their responses during smooth pursuit eye movements, VOR performance in the dark, and cancellation of the VOR as the monkey tracked a visual target moving exactly with the head . Simple Voltages related to the eye velocity, vestibular stimulus, and visual stimulus were recorded at 500 Hz per channel. Eye velocity was obtained from the eye position signal using a hardware differentiator and filter with a 300 Hz corner frequency. For Data were analyzed using Matlab to compute the changes in Purkinje cell firing on the trial following a trial with a climbing fiber spike. Trial-by-trial analysis was performed on consecutive vestibular stimuli in the ‘on’ direction for climbing fibers: ipsilateral vestibular stimuli (head rotations towards the side of recording) for VOR-increase training and contraversive vestibular stimuli (away from the side of recording) for VOR-decrease training . The intFor each training condition and each cell, we identified all pairs of consecutive trials in which a climbing fiber spike (CF) occurred in the analysis window on the first trial but not the second trial (No CF), and all pairs of consecutive trials in which there was no climbing fiber spike in the analysis window on either trial (No CF–No CF pairs). The simple spike response in the first trial was then subtracted from that in the second trial of the pair to obtain the trial-to-trial difference in the simple spike responses . The triTo determine whether changes in Purkinje cell activity were significant, we created a bootstrap distribution of each data set and calculated the 95% confidence intervals (mean ± 2 standard deviation) based on this distribution. Mean values above or below the confidence intervals were considered significant. The bootstrap distribution was created as follows: for each cell, consecutive pairs of trials were selected randomly from the pool of all available trial pairs  to match the number of CF–No CF or No CF–No CF pairs that were actually observed. An average trial-to-trial change in firing rate was calculated for each cell, and then all cells were averaged to complete one run of the bootstrap. The bootstrap was repeated 1000 times, and 95% confidence intervals were established based on the mean ± 2 standard deviation of the entire distribution.To determine which statistical test is appropriate for each dataset in To confirm that the Purkinje cells exhibited learning-related changes in their responses over the course of a VOR-decrease training session, even though there was no trial-by-trial effect of climbing fiber input during VOR-decrease training, we analyzed the progressive change in Purkinje cell firing over the course of each ∼90-s training period. Previous studies have reported learning-related changes in the Purkinje cells’ response during the VOR after at least an hour of VOR-increase or -decrease training . To deteFor all the complex spikes used in the trial-by-trial analysis , we measured features of the complex spike and the complex-spike-triggered pause in simple spiking. The number of spikelets following the large initial transient in each complex spike was counted manually, with the experimenter blind to training condition. The complex spike duration was defined as the latency from the peak of the initial large transient to the peak of the last spikelet. The length of the post-complex spike pause was calculated as the latency from each complex spike to the next simple spike. Results were averaged within each cell, and a paired t-test between VOR-increase and VOR-decrease training sessions was performed to assess significance.For the analysis shown in An optogenetic approach was used to stimulate the climbing fibers . The neu> 1012) into the dorsal cap of Kooy of the inferior olive, which provides the climbing fiber input to the cerebellar flocculi, of mice >9 weeks old. Stereotaxic coordinates were used to target the dorsal cap  coupled to a 200-μm optical fiber . The optrode was introduced via the implanted cannula. For some physiological recordings, the mouse was initially anesthetized using isoflurane or ketamine and then allowed to recover for the recordings. Purkinje cell activity was sampled at 50 KHz using Spike 2 (CED), and sorted off-line (Spike 2). Climbing fibers were activated using the same optical stimulation parameters that were used in the behavioral, optogenetic training experiments (see below). An electrophysiological waveform artifact due to the light pulse was observed in some recordings. To isolate the waveform artifact, the optrode was moved until the activity of the cell that was being recorded disappeared, and the same light stimulus trains were then delivered. This isolated waveform of the artifact was subtracted from the electrophysiological traces to identify the complex spike response to the light pulse.2) was repeated every second and centered on peak ipsiversive or contraversive vestibular stimulus speed, to roughly mimic the climbing fiber responses during VOR-increase or VOR-decrease training, respectively. Stimulation was delivered bilaterally, with stimulation of climbing fibers in the two cerebellar hemispheres 180° out of phase. In a separate cohort of mice, a 250-ms train of three light pulses with a 125-ms inter-pulse-interval  was delivered unilaterally. As a control, the same experiments were performed without climbing fiber stimulation during the training blocks (‘vestibular only’ condition). In a ‘climbing fiber only’ training condition, climbing fibers were stimulated bilaterally or unilaterally with the train of three light pulses used in optogenetic training. The effects of unilateral or bilateral climbing fiber only stimulation were not significantly different, and were therefore pooled . Vestibular stimuli were delivered by rotating the whole body about an earth–vertical axis using a 1 Hz sinusoidal velocity profile (±10°/s peak velocity). To stimulate the climbing fibers in the flocculus, a 200-μm optical fiber (Thor Labs) was introduced through the cannula implanted above the cerebellar flocculus. To optimize the position of the optical fiber for the most effective activation of the climbing fibers, a 20-Hz train of 2 ms light pulses was delivered for 3 s at different depths. When positioned near climbing fibers expressing ChR2, the high frequency stimulation induced a nystagmus similar to what was previously described when the inferior olive was electrically stimulated . The optTraining periods consisted of 3 × 10-min blocks of vestibular stimuli paired with climbing fiber stimulation. Before and after each block, the vestibular stimulus was delivered in the absence of climbing fiber stimulation for 60–80 s to measure the gain of the VOR. The VOR was measured again 2 hr after optogenetic training; during the intervening two-hour retention period, the animal was kept in the dark in its home cage. All training and testing were conducted in total darkness; the optical fiber used to optically stimulate the flocculus was optically insulated to prevent light leaks.For each mouse, the order of training with the optogenetic VOR-increase, optogenetic VOR-decrease, vestibular only and climbing fiber only conditions was pseudo-randomized. For the mice that underwent bilateral climbing fiber stimulation, half were tested first with the single light pulse simulation protocol, and the other half with the train of three light pulses. There were at least 48 hr between tests of the different training conditions. After the optogenetic training experiments were conducted, we tested the ability of the mice to undergo a learned decrease and increase in VOR gain in response to a more typical VOR training paradigm, which paired the vestibular stimulus with a visual stimulus. For visually-induced VOR-decrease or VOR-increase training, the vestibular stimulus  was paired with a visual stimulus that moved exactly with or exactly opposite the head, respectively. The visual stimulus was provided by a back-lit optokinetic drum with black and white stripes, each subtending approximately 7.5° of visual angle.For bilateral stimulation with single light pulses, 7 mice were tested on all training conditions, except one mouse was not tested in the vestibular only condition. For bilateral stimulation with three light pulses, 7 mice were tested on all training conditions, and an additional two mice were tested on a subset of training conditions. Data from the 30 min time point of the bilateral experiments with three light pulses, along with the corresponding vestibular only controls from the same mice, were reported in a previous study . For uniEye position, vestibular and visual stimulus position and velocity were sampled at 1000 Hz (Spike 2). Custom software was used for data analysis . To calculate eye velocity, the eye position was first low-pass filtered using a third-order Butterworth filter with a cutoff frequency of 9 Hz, and then differentiated. Eye velocity data were edited to remove saccades and any body movement artifacts using a velocity threshold. Eye and head velocity data were fit with sine waves. VOR gain was calculated by dividing the amplitude of the eye velocity fit by the amplitude of the head velocity fit. Learning was calculated as the percent change in VOR gain relative to the pre-training baseline, that is, a 0% change would indicate no learning. The normality of the distribution of VOR gain changes for each training condition at each time point of interest was tested using the Shapiro–Wilk normality test (Graphpad Prism). For statistical analyses that included at least one set of data with a non-normal distribution, non-parametric tests were used. For the experiments that used single pulses of climbing fiber stimulation , a repeaAll procedures were approved by Stanford University’s Administrative Panel on Laboratory Animal Care. review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  The paper deals with multiple topics that are not well integrated to form a cohesive story. In many ways the study of trial-by-trial of 2) Previous studies showed that VOR-increase and VOR-decrease learning are mediated by different mechanisms . It was 3) The ChR2 experiments are interesting but they are very closely related to the authors' recent Nature Neuroscience paper describing similar results. It is not clear whether the data in the current study provide a significant advance over 4) The term gating might be somewhat confusing in the context of this paper because it has previously been used to refer to gating in the inferior olive , for instance because of the nucleo-olivary pathway. When using the term “gating”  one would normally suggest that it is a design principle of a circuit that a signal with a particular function is sometimes allowed, sometimes not, to fulfil that function. When A causes B only when it occurs together with C, we do not usually say that C is a gating mechanism. In the reviewers' view, the authors seem to suggest something stronger than what they have demonstrated. When it comes to the speculations about what the missing factor might be, I think that a word or two about how the mossy fiber input might differ between the two experimental situations might be justified.5) The conclusion that the ability of climbing fibers to induce cerebellar plasticity is regulated downstream, rather than in a different area, should be better justified.6) It would also be nice to discuss why increases and decreases in VOR gain might be implemented by different mechanisms. The reviewers appreciate that such discussion might be hypothetical at present, yet it is important to be included in the paper. This could be a potentially very important manuscript, but reviewers felt that the important message does not come across. Because the paper deals with a number of different topics, it is sometimes hard to follow and the innovation/conceptual advances often remain unclear. Several observations are interesting, but as written it is not obvious that the paper provides a cohesive story and a major conceptual advance. Thus, the authors can and should boost up both Introduction and Discussion to highlight what is unique and novel in the present experiments. Also the Results section needs to provide better logical justification about the numerous experiments performed. The potential importance of the paper was better articulated during the consultation process and discussion among the reviewers and editors than by the manuscript itself. The case for the conceptual value of the manuscript must be made by the paper itself and the new data it presents.The biggest concern with the manuscript is that the progress is several small loosely linked steps forward and there was concern that the major advances are far from clear.The manuscript has been extensively rewritten to clarify the rationale for all of the experiments and to more effectively highlight the novel findings and conclusions.The major conceptual advance is the demonstration of a new component of the learning algorithm in the cerebellum of awake behaving animals, namely regulation of the ability of climbing fiber spiking to trigger cerebellar plasticity. The Marr-Albus-Ito model of cerebellar learning suggests that a performance error elicits a response in the climbing fibers, which triggers plasticity. Previous studies have provided evidence that the ability of error-related cues to drive spiking in the climbing fibers can be regulated by the state of the cerebellar circuit during training . Our papThere was overall concern about the fact that stimulation experiments are in mice and recording experiments in monkey.The VOR is a phylogenetically old behavior, and we know of no evidence to suggest mechanistic differences across species. On the contrary, decades of results reported from fish, rodents, rabbits, cats, monkeys and humans have been remarkably consistent, as is the case for the present experiments.eLife paper from the Lisberger lab and two previous papers from that lab. Those papers reported that a single complex spike in a Purkinje cell, reflecting a spike in its climbing fiber input, reliably triggers plasticity that can be detected on the next trial during smooth pursuit learning. In striking contrast to those papers, we found a clear dissociation between the complex spikes and the induction of plasticity during VOR-decrease learning. Because the experiments were all done in rhesus monkeys, this difference, which is central to our conclusions, cannot be attributed to a species difference.The recordings and associated trial-by-trial analysis of VOR learning were conducted in rhesus monkeys so that they could be directly compared with the in-depth, trial-by-trial analysis of monkey smooth pursuit learning described in a recent The optogenetic stimulation experiments were conducted in mice for practical reasons; the technical difficulty of doing those experiments in monkeys was prohibitive.The relationship between trial-by-trial learning and longer-term learning is not established and although reviewers thought this one of the most interesting aspects of the paper, the connection to the rest of the paper was not clear.The relationship between trial-by-trial learning and longer term learning is not understood, but our results provide some of the best evidence to date that they may be related, by showing parallels between the contribution, or lack of a contribution, of climbing fiber activity to plasticity on several different time scales. The contribution of the climbing fibers to plasticity differed across learning paradigms, however, within a paradigm, the contribution of the climbing fibers was consistent over a single trial, over the course of a 30-minute training session, and 2 hours after the end of training.One of the reviewers expressed serious reservations about these shortcomings. It is important that more focus on the novel aspects of the paper is needed. In addition, the authors should either eliminate some of the less compelling aspects of the paper or fill in the dots as to why they are important and related to the big story.We have extensively rewritten the manuscript to focus on the main, novel conclusions and to weave them more tightly together.1) The paper deals with multiple topics that are not well integrated to form a cohesive story. In many ways the study of trial-by-trial ofis reminiscent of the recent Lisberger eLife paper that provided an in-depth characterization of trial-by-trial learning in smooth pursuit. In contrast to that paper, the present manuscript does not provide an in depth study of trial-by-trial learning. Although there is speculation about the relationship between trial-by-trial learning and long-term learning, it is not known if these phenomena are related in any way. A clear distinction and comparison is necessary. The differences between pursuit versus VOR should also be discussed.The trial-by-trial analysis technique used in our manuscript was modeled on that used by the Lisberger lab, moreover, we are recording from Purkinje cells in the same region of the cerebellum, the floccular complex. Despite the similarities in our experimental approaches, our results and conclusions differ in a major way from the Lisberger lab’s results on smooth pursuit learning. The previous analyses of smooth pursuit learning  all found a consistent, trial-by-trial correlation between spikes in a climbing fiber and the induction of plasticity, consistent with the classic, Marr-Albus-Ito model of cerebellar learning. In striking contrast, our results from VOR-decrease learning show that there are conditions where there are robust error signals and spiking in the climbing fibers, but there is no induction of plasticity that can be detected on the next trial. This latter observation supports our novel conclusion that the ability of the climbing fibers to induce plasticity can be gated downstream of spikes in the climbing fibers. The revised manuscript lays out this comparison of the different oculomotor learning tasks. In addition, we include a discussion of the different time scales of plasticity.2) Previous studies showed that VOR-increase and VOR-decrease learning are mediated by different mechanisms . It was already known that there could be climbing fiber activity that would drive VOR-increase learning but not VOR decrease learning. As a result, the findings ofdo not appear particularly surprising, so the authors have to motivate the novelty better.The revised manuscript better delineates the novelty of the current findings relative to previous work from our laboratory. Boyden & Raymond 2004 hypothesized that different mechanisms contribute to VOR-increase and VOR-decrease learning, based purely on behavioral observations of the different properties of these two forms of VOR learning. 3) The ChR2 experiments are interesting but they are very closely related to the authors' recent Nature Neuroscience paper describing similar results. It is not clear whether the data in the current study provide a significant advance over: They use some of the same control data in, and in that paper they showed that optogenetic activation could induce VOR-increase learning but not VOR-decrease learning.natural climbing fiber responses present during visual-vestibular VOR- decrease training are not correlated with the induction of plasticity either. Each approach has its strengths and limitations – the stimulation experiments can establish causality but are inherently “unnatural”, whereas the recording experiments document what happens under more natural conditions, but can only show correlations, which does not necessarily mean there is a causal relationship. Together the convergent evidence from the stimulation and recording experiments are considerably more powerful than either alone, and thus the combination of the two is an important contribution of the current manuscript. In addition, the current manuscript improves upon the stimulation approach by comparing multiple climbing fiber stimulation protocols and by testing whether climbing fiber stimulation might be initiating changes in the VOR circuit that would support the delayed expression of VOR-decrease learning, which does not appear to be the case.The main conclusion of 4) The term gating might be somewhat confusing in the context of this paper because it has previously been used to refer to gating in the inferior olive , for instance because of the nucleo-olivary pathway. When using the term “gating”  one would normally suggest that it is a design principle of a circuit that a signal with a particular function is sometimes allowed, sometimes not, to fulfil that function. When A causes B only when it occurs together with C, we do not usually say that C is a gating mechanism. In the reviewers' view, the authors seem to suggest something stronger than what they have demonstrated. When it comes to the speculations about what the missing factor might be, I think that a word or two about how the mossy fiber input might differ between the two experimental situations might be justified.We use the term “gating” to describe our observation that in the very same animals and the very same cells, there can either be a tight coupling between climbing fiber spikes and the induction of plasticity, or no such coupling, depending on the behavioral context. The revised Discussion clarifies that climbing fiber triggered plasticity seems to be regulated, or gated, at two different stages – at the level of the olivary neurons either spiking or not spiking, for instance because of the Purkinje cell-nucleo-olivary pathway; and at the level of spikes in the olivary neurons and their climbing fiber axons either inducing or not inducing plasticity.We now include a discussion of what is known about the mossy fiber input in the two relevant contexts, which suggests that some other factor provides the gate.5) The conclusion that the ability of climbing fibers to induce cerebellar plasticity is regulated downstream, rather than in a different area, should be better justified.Two lines of evidence suggest that we are recording from and stimulating the cerebellar area (the floccular complex) relevant for both learning paradigms. First, lesion studies have shown deficits in both VOR-increase and VOR-decrease learning after lesions of the floccular complex . Second,6) It would also be nice to discuss why increases and decreases in VOR gain might be implemented by different mechanisms. The reviewers appreciate that such discussion might be hypothetical at present, yet it is important to be included in the paper.We now include a brief treatment of this issue in the Discussion.


We integrate an olfactory epithelium and bulb (OB) model with an olfactory (piriform) cortex (OC) model, all comprising abstract graded units representing local populations of neurons, to demonstrate the overall system's applicability to odor recognition tasks. Large-scale simulations on a cluster computer allow us to evaluate an instance of the model with the size matching that of the olfactory system of a small mammal. The input data is synthesized with the intention to resemble the distribution of various features of early olfactory response patterns to naturalistic odor stimuli, e.g. ,2.Self-organization of the feedforward connectivity from the OB to the piriform cortex based on statistical properties of synthetic olfactory stimuli with the support of synaptic plasticity provide the capacity for generating sparse and distributed cortical representations . At the The model is evaluated in real-world scenarios involving odor classification and segmentation in noisy environments. Firstly, the network is trained to perform concentration invariant odor recognition. Secondly, the model identifies single-ligand components of odor mixtures it is subjected to in a testing phase. Finally, the effect of context dependence is evaluated by examining the system's capability to recognize specific low intensity stimuli when it is forced into an arousal state. Preliminary results show the dependence of performance on the network size, the number of odors and the complexity of their early olfactory activation patterns.In a broader context, the proposed large-scale abstract models of holistic olfactory processing implements a multiscale approach to modeling olfaction. After the network has been trained, its connection matrices are utilized in simulating a more biophysically realistic model of the mammalian olfactory system .


Since Georg von Bekesy laid out the place theory of the hearing, researchers have been working to understand the remarkable properties of mammalian hearing. Because access to the cochlea is restricted in live animals, and important aspects of hearing are destroyed in dead ones, models play a key role in interpreting local measurements. Wentzel-Kramers-Brillouin (WKB) models are attractive because they are analytically tractable, appropriate to the oblong geometry of the cochlea, and can predict wave behavior over a large span of the cochlea. Interest in the role the tectorial membrane (TM) plays in cochlear tuning led us to develop models that directly interface the TM with the cochlear fluid. In this work we add an angled shear between the TM and reticular lamina (RL), which serves as an input to a nonlinear active force. This feature plus a novel combination of previous work gives us a model with TM-fluid interaction, TM-RL shear, a nonlinear active force and a second wave mode. The behavior we get leads to the conclusion the phase between the shear and basilar membrane (BM) vibration is critical for amplification. We show there is a transition in this phase that occurs at a frequency below the cutoff, which is strongly influenced by TM stiffness. We describe this mechanism of sharpened BM velocity profile, which demonstrates the importance of the TM in overall cochlear tuning and offers an explanation for the response characteristics of the Tectb mutant mouse. Mammals transduce sound waves to neuronal signals within the fluid-filled cochlea via traveling waves in the organ of Corti (OC). This mechanical process is responsible for much of the extraordinary range and sensitivity that is characteristic of mammalian hearing, yet underlying mechanisms of nonlinear perception that enable the most interesting aspects of hearing, are still being determined This model builds on results in W(z), where z is the long dimension. This and other dimensions are based on observations for the mouse The cochlea is modeled as a long, fluid-filled duct partitioned into two compartments, the scala vestibule (SV) and scala tympani (ST), roughly corresponding to the physiology. The lumped masses, Some simplifying approximations specific to this model design are discussed as follows: by introducing the shear between the TM and RL we also introduce the possibility of two-dimensional motion the TM. Thin-film viscous adhesion will keep the gap between the TM and reticular lamina (RL) constant. The direction of the viscoelastic force due to the TM  to the fluid pressure. These displacements are expressed using vector notation  represents the passive problem, which we solve as in Once the passive model is solved, we can use the resulting The LHS of Eq. (10) requires choosing an eigenmode to set We wished to consider the effect of longitudinal coupling in the TM, both to test the effect of a longitudinal coupling source besides the fluid on our model. We developed a term to be added to a two degree of freedom model, without any shear motion. in vivo, making the second wave an important consideration.Also of note is the extreme similarity of wavenumbers between the system with and without shear. Adding this component lets us extract more information about the system, but the waves are essentially the same. Thus conclusions about the two degree of freedom system The most significant result we found was that by increasing the stiffness of TM, the selectivity  of the BM increased, but the sensitivity (as defined by the maximum velocity) decreased, as shown in scala vestibuli, which is related to the active force, and the fluid pressure near the BM, which indicates its motion The wavelength correction defined in Eq. (13) is responsible for most of the active force gain. Examining the equation analytically and looking at predictions for the frequency dependent behavior of the quantities on which We can think of this transition as restricting the frequency range of the active region, somewhat akin to a high-pass filter. The abrupt loss of passive amplitude at the cutoff frequency restricts on the high-frequency end, together making a narrow band region of gain. Shifting the frequency of the “high-pass filter”, by changing the TM stiffness, but maintaining the frequency cutoff alters the Q of the frequency response. For stiffness values that allow the filtering effects to overlap, the frequency of maximum vibration is impacted by the gain-reducing qualities and is diminished, lowering sensitivity, trading sensitivity for selectivity. The high-frequency end of the band can be altered by varying BM stiffness, giving the same sensitivity-selectivity trade off see  but the This result is particularly interesting given that velocity threshold measurements on the BM of the high-frequency  region of the Tectb mutant mouse displays increased selectivity and somewhat decreased sensitivity compared to the wildtype As part of this interpretation, we must consider how the changes observed in the TM of the Tectb mutant may manifest themselves as the increase in stiffness required by our model. Our lumped stiffness parameter simultaneously represents TM attachment, elastic modulus, and any bending effects which are left out of our model. For modeling purposes we assumed a fairly homogeneous cross section, but the actual cochlea is quite complex, and any inhomogeneity due to fluid pressure, geometry or differential force from the cells versus the numerous fluid spaces in the OC may make the bending characteristics quite important. In this case, a loosely coupled system might in effect be stiffer by requiring a greater fluid pressure at some local point to produce the same amount of force transmitted at another local point (such as directly over the hair cells). Thus the observed disorganization of the TM structure that led other researchers to think about a decrease in longitudinal coupling might increase the lumped parameter spring constant.i was needed for correct timing in his model. Examining To amplify BM vibrations, OHC electromotility must be properly timed in the vibration cycle in vivo factors including local damping from surrounding tissues and slow electrical response times may introduce delay, we investigated altering the phase of the input-output relation. The maximum velocity in these scenarios, shown in In general, we assumed the phase of action of the cochlear amplifier was zero. That is, a positive shear of OHC stereocilia causes a contraction at the same point in the phase cycle without any sort of delay By introducing the shear angle into the model, we gained a means to examine the role of this quantity which is known to grow from base to apex Our model offers an intriguing possibility to explain the simultaneous increase in selectivity with loss of sensitivity of the Tectb mutant - a band pass filtering mechanism, with the low corner being determined by the TM-BM interaction. This idea lends further support to previous studies that suggest the properties of the TM are important to overall cochlear tuning There is a the growing body of evidence that the TM is a critical structure in the precise nature of mammalian hearing File S1Figure S1, Frequency responses due to change in TM damping. More TM damping leads to a smoother curve, but does not significantly shift the peak or the frequencies of the phase transition. Sensitivity increases with selectivity. Figure S2, Frequency responses due to change in OC stiffness. In this case, increased coupling between the TM and BM leads to a sharper, bumpier curve, and sensitivity increases with selectivity. Figure S3, Frequency responses due to change in BM stiffness. Changing the BM stiffness leads to a trade in sensitivity for selectivity. In this case, we can observe that the peak frequency increases with BM stiffness, as expected, while the phase difference remains the same. This is compatible with our band pass filter description of the tuning mechanism, but in this case we are moving the high frequency corner. Figure S4, Diagram of the three masses of the cochlear partition and the forces on them per unit length. Arrows are intended to depict direction of force due to positive displacements. The direction of Table S1, Viscoelastic Parameters. Table S2, Structural Dimensions.(PDF)Click here for additional data file.


However, the preferred search programs and parameters that will optimise the sensitivity and specificity of the alignments between the sequence reads and the framework genome(s) are not necessarily obvious. Here we demonstrate a process that uses paired-end sequence reads to choose an optimal program and alignment parameters.The advent of cheap high through-put sequencing methods has facilitated low coverage skims of a large number of organisms. To maximise the utility of the sequences, assembly into contigs and then ordering of those contigs is required. Whilst sequences can be assembled into contigs Unlike two single fragment reads, in paired-end sequence reads, such as BAC-end sequences, the two sequences in the pair have a known positional relationship in the original genome. This provides an additional level of confidence over match scores and e-values in the accuracy of the positional assignment of the reads in the comparative genome. Three commonly used sequence alignment programs: MegaBLAST, Blastz and PatternHunter were used to align a set of ovine BAC-end sequences against the equine genome assembly. A range of different search parameters, with a particular focus on contiguous and discontiguous seeds, were used for each program. The number of reads with a hit and the number of read pairs with hits for the two end sequences in the tail-to-tail paired-end configuration were plotted relative to the theoretical maximum expected curve. Of the programs tested, MegaBLAST with short contiguous seed lengths (word size 8-11) performed best in this particular task. In addition the data also provides estimates of the false positive and false negative rates, which can be used to determine the appropriate values of additional parameters, such as score cut-off, to balance sensitivity and specificity. To determine whether the approach also worked for the alignment of shorter reads, the first 240 bases of each BAC end sequence were also aligned to the equine genome. Again, contiguous MegaBLAST performed the best in optimising the sensitivity and specificity with which sheep BAC end reads map to the equine and bovine genomes.Paired-end reads, such as BAC-end sequences, provide an efficient mechanism to optimise sequence alignment parameters, for example for comparative genome assemblies, by providing an objective standard to evaluate performance. With the availability of the so-called Next Generation Sequencing (NGS), relatively cheap high-throughput short molecule sequencing technologies such as Illumina GA and ABI SOLiD, and medium length sequencing technologies such as Roche 454 is giving non-specialist laboratories the ability to sequence large genomes. However, the large number of reads produced by these NGS technologies creates problems for the utilisation of the sequence data. In the last few years a number of new programs for the alignment of short reads, for example in the range 30-150 bases, have been described, these include Maq  and BowtHowever, many projects are likely to be low coverage skims of previously unsequenced genomes  possiblyIn recent years a wide range of different programs have emerged to complement BLAST, itself a compromise between specificity and sensitivity relative to the Smith-Waterman algorithm. True Smith-Waterman is too slow for large scale projects, but in an effort to approach its speed, sensitivity and specificity, MegaBLAST  and PattHere we use the example of the analysis of the effectiveness of three widely used DNA sequence alignment programs to position ovine BES reads against the equine and bovine genome assemblies to demonstrate the utility of the approach. We use the information about the positional relationship of the end sequences of each BAC in the ovine genome to estimate the sensitivity and specificity of the methods of determining the positions in the related, but not identical genomes.The full-length BESs from the ovine CHORI-243 BAC library, constructed from a single individual animal , were maPatternHunter  Table 11 performBlastz  has beenMegaBLAST has been designed for very quickly searching large databases and also permits the use of contiguous and discontiguous seeds to initiate matches. The performance of discontiguous MegaBLAST at all word sizes is clearly more specific than contiguous MegaBLAST when the latter is run with no score filtering at all. With long word sizes, such as 16, discontiguous MegaBLAST is also more sensitive than contiguous MegaBLAST, but less sensitive than contiguous MegaBLAST with a word size of 12 or less. As expected, using both a seed designed for protein coding DNA and a seed designed for non-coding DNA [Overall, for this particular comparison, the best parameters were short contiguous word size settings for MegaBLAST. A small improvement in sensitivity over searches was observed when using a word size of 8 instead of 9 Figure . AlthougIn comparison, the virtual ovine genome approach , which aMegaBLAST search results can be filtered post completion of the searches using the alignment scores. Increasing the alignment score cut-off, substantially reduced the false positive rate with limited impact on the total number of BES for which correct positions were obtained Table . IndividTo test the applicability of the approach described here with sequences of shorter length the ovine BESs were trimmed to the first 240 bases, whilst retaining the pairing information. A smaller number of searches of program and parameter values were undertaken for the alignment of trimmed ovine BESs against the equine genome assembly Figure . As withThe ovine and bovine genome sequences are more closely related to each other than either is to the equine genome. We explored the choice of parameters for aligning the trimmed ovine BESs sequence reads to the bovine genome assembly. Although contiguous MegaBLAST was also better than discontiguous MegaBLAST, the difference was not as large as that observed for the searches against the equine genome (data not shown). Thus the relative performance of the different sets of parameters is also dependent on the characteristics of the sequence datasets being used for a particular comparison. This reinforces the need for the empirical comparison of programs and parameters with the datasets to which they will be applied. The frequency distribution of the alignment scores for the 240 base trimmed BESs against the equine and bovine genomes show marked differences  reduced the sensitivity and specificity scores for all search programs and parameter sets tested relative to Btau4.0, but did not alter the relative ranking of the programs and parameters (data not shown). Thus where the objective is to optimise search parameters, as long as the order of contigs and scaffolds in the comparison genome is approximately correct, this approach will be effective.Paired-end sequences provide a very useful dataset for optimising program and parameters for positioning sequence reads (or contigs) with a range of different lengths from one genome against another genome. Parameter estimation can be undertaken with genomes in various stages of assembly, although a substantial number of scaffolds much longer than the average length of the inserts in the paired-end reads are required. Surprisingly, MegaBLAST with contiguous seeds performs better than discontiguous seed MegaBLAST and Blastz for alignment of ovine reads to the equine and bovine genomes. PatternHunter would perform much better if it were able to effectively utilise soft masking. A range of programs and parameter settings can be quickly surveyed with datasets appropriate to the particular objective. The optimal balance between yield and specificity of positioning chosen will depend on the subsequent use of the results.The objective of the general methodology we present here is to compare a number of alignment programs to maximise the number of sequences generated as part of a sequencing project of species A, that are correctly positioned on the already assembled genome of a related species B. A set of paired-end reads must be obtained for species A as part of the genome sequencing project. Then the paired-end reads for species A can be used to optimise the choice of DNA sequence alignment program and parameters to align all unpaired end reads to the framework genome B, thus enabling rapid and accurate construction of sequence contigs for species A. The general approach is as follows; with the selected DNA sequence alignment programs and a range of parameters position the paired-end reads from species A on to the genome of species B. Then count the total number of sequences positioned by each program and set of parameters and calculate the number of sequences that retain the original positional relationship of paired end reads within species A within the genome of species B. Plot the results as in Figure The set of ovine BESs downloaded from GenBank were filtered as described previously  and "sofBLAST version 2.2.16 was obtained from the NCBI website . Blastz x) ranging between 0 and 100%For a given number of positioned BESs, the theoretical maximum curve estimates the expected percentage of total BESs that are correctly positioned in a tail-to-tail pair. Assuming all BESs are correctly positioned on the genome, the theoretical maximum curve was calculated using formula (1) below for values of the number of BESs with positions(X = number of BESs with positionst = Total number of BESsy = Expected number of total BESs that are correctly positioned in a pairBuilt into this approach is the assumption that positioning one BES correctly is independent of positioning the other end of the BAC, or of positioning it correctly. Where this is not true the number of BACs with both ends correctly positioned may exceed the theoretical maximum curve by a small amount.The percentage of BACs in tail-to-tail configuration is always expressed as a percentage of the total BACs in the dataset, not as a percentage of BACs with at least one BES positioned.The false positive and false negative rates as shown in Table o = number of BESs actually in tail-to-tail BACse = number of BESs predicted to be in tail-to-tail BACsBAC: Bacterial Artificial Chromosome; BES: BAC end sequence.AR carried out the majority of the analyses. WB and SMcW carried out some of the analyses, BPD conceived, designed and coordinated the study and drafted the manuscript. All authors drafted, read and approved the final manuscript.


An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness. To reach an object, the brain has to select among a set of possible arm trajectories that displace the hand from an initial to a final desired position. Because of the intrinsic redundancy characterizing the human arm, the number of admissible joint trajectories toward the goal is generally infinite. However, many studies have demonstrated that the range of actual trajectories can be limited to those that result from the fulfillment of some optimal rules. Various cost functions were shown to be relevant in the literature. A peculiar aspect of most of these costs is that each one of them aims at optimizing one specific feature of the movement. The necessary motor flexibility of everyday life, however, might rely on the combination of such cost functions rather than on a single one. Testing this cost combination hypothesis has never been attempted. To this aim we propose a reaching task involving target redundancy to facilitate the comparisons of different candidate costs and to identify the best-fitting one (possibly composite). Using a numerical inverse optimal control method, we show that most participants produced movements corresponding to a strict combination of two subjective costs linked to the mechanical energy consumption and the joint-level smoothness. Numerous experimental studies have demonstrated that biological motion exhibits invariant features, i.e. parameters that do not significantly change with movement size, speed, load and direction The first one is methodological: in many cases, models based on divergent assumptions and minimizing different costs can yield similar arm trajectories In order to test this cost combination hypothesis, our approach was two-fold. First, we wanted to stress the differences between the predictions of different classical models already existing in literature. To this aim, we designed a pointing task with target redundancy. Precisely, we reduced the external constraints of the task by asking subjects to reach to a vertical bar. Thus no accuracy requirement was present in the vertical axis, which had the interesting advantage of discriminating better between different cost functions than during classical point-to-point experiments  with the maximization of joint smoothness (here the integrated squared acceleration). Further analyses demonstrate that this mix-of-cost model replicated the most important features of arm movements and performed better than any other single cost function on which our method was based. Results provided therefore support the cost combination hypothesis and, in particular for this task, emphasize two complementary and subjective costs.meanstd: age Twenty naive subjects  indicating the requested fingertip initial position were attached. Differently colored pieces of scotch-tapes were stuck on the leads to easily identify the references. This color-code was then used to verbally specify the initial posture that the subject had to select at the beginning of each movement. By imposing the initial finger position, a unique starting posture of the arm was thus defined in the para-sagittal plane. The positions of the leads were adjusted before the experiment, based on the subject's upper arm and forearm lengths and the vertical distance shoulder-ground.The experimenter then gave the following instruction to the participants: look at the bar in front of you, close the eyes and quickly show the location of the bar by touching it with the fingertip, performing a one-shot movement. No instruction was given to the subjects with respect to where and how to reach the bar. Because of the features of the task itself, participants had to implicitly control the finger position along the antero-posterior and lateral directions whereas full freedom was left along the vertical one. Note that the challenge for the subjects (i.e. the objective reward of the task) was to be precise enough to actually touch the bar, since no on-line vision was allowed. Since subjects were free to moved in 3-D, touching the bar was not so easy because of the presence lateral and antero-posterior errors and the absence of on-line visual feedback. Nevertheless, it is worth noting that reaching any point on the vertical bar allowed the subject to perform the task successfully. During the protocol, the five initial postures were tested in a random order. For each initial posture, twenty trials were recorded, so that a total of 100 movements per subject were monitored. A few trials were repeated during the experiment (less than 5%), when the subjects clearly missed the bar or did not perform a one-shot movement. Every set of 25 movements, subjects were allowed to rest. Data from a total of 2000 pointing movements were collected for this reaching-to-a-bar task.Arm and head motion were recorded by means of a motion capture system . Ten cameras were used to capture the movement of six retro reflective markers (15 mm in diameter), placed at well-defined anatomical locations on the right arm and head .filtfilt function).All the analyses were performed with custom software written in Matlab  from the recorded three-dimensional position of the six markers . Recorded signals were low-pass filtered using a digital fifth-order Butterworth filter at a cutoff frequency of 10 Hz (Matlab spline function). Standard kinematic parameters described in previous experimental arm pointing studies were calculated movement duration (MD), peak velocity (PV), mean velocity (MV), relative time to peak velocity (TPV) defined as the ratio between the acceleration duration and MD, index of velocity shape (Vpeak/Vmean) defined as the ratio between the peak of velocity and its mean value, and curvilinear distance of the finger (CD) defined by the integral over time from 0 to MD of the norm of the fingertip velocity vector. The constant error was computed as the orthogonal distance between the terminal finger position and the bar. The variable error was defined as the standard deviation computed on the distances between the measured endpoints across trials.The temporal finger movement onset was defined as the instant at which the linear tangential velocity of the fingertip exceeded 5% of its peak and the end of movement as the point at which the same velocity dropped below the 5% threshold. All time series were normalized to 200 points by using Matlab routines of interpolation  were then evaluated using the inverse kinematic function, relating the The shoulder joint was defined as the origin of the frame of reference see .endpoint position consistency index (CI) was defined as the ratio between the standard deviation of the fingertip position on the movement vector angle (denoted by MV) defined as the counterclockwise-oriented angle between a horizontal line and the line connecting the initial and terminal fingertip positions.Finally, additional task-relevant parameters were computed. The area between paths. Given the complexity of the polygon to be integrated .Moreover, to assess whether the finger path had a convex or concave curvature, we computed the signed Index of Path Curvature (sIPC). This was defined as the averaged ratio between the maximum path deviation from a segment connecting the initial-final finger positions and the length of this segment, attributing a positive sign when the finger position was above the straight line (for concavity). Thus, this parameter evaluates the average or global convexity or concavity of a hand path. In addition, joint coupling was calculated as the determination coefficients between the shoulder and elbow angular displacements. In order to compare models predictions and measured data, we computed the qqplot Matlab function). Shapiro-Wilk's test was used to quantify these observations for some relevant parameters. One-way ANOVAs were also performed to analyze the effects of the initial posture on certain parameters. Post-hoc tests were conducted with Scheffé's test when necessary and appropriate  suggested that the results presented in this study do not critically depend on this choice , the optimal solution is known and the objective is to recover the performance criterion which has been optimized. Addressing the motor planning problem in this way is generally more difficult than using the more standard direct optimal control approach, which consists of guessing a plausible cost and comparing its predictions with the experimental data. However, inverse OCP is better suited to provide, with less a priori, the cost or mix of costs that must be optimized to replicate the measured arm trajectories. In this paragraph, we present a numerical method for solving an inverse OCP, which was initially described by kinematic models: the minimum hand jerk smoothness in the broad sense of “having small high-order derivatives” dynamic models were proposed to avoid these inverse dynamics computations, such as the minimum torque geodesic model suggests that the brain may select the shortest path in configuration space with respect to the kinetic energy metric Energetic models were also considered in several studies, in particular those involving the minimization of work of torques , joint smoothness (angular acceleration and angle jerk models), torque change, torque, geodesic, mechanical energy, and neural effort  consists in solving an optimization problem for the unknown How to define a good “metric” in the space of trajectories is still an open question in motor control Solving the bi-level problem is not straightforward for several reasons. First, the objective function To improve the algorithm efficiency, we found useful to appropriately scale the step size along each dimension of the search space. We used a re-scaling vector, per se, especially when dealing with complex costs and dynamics. However, in the deterministic context that we consider here, there exist efficient numerical techniques to find approximate solutions. A classical method is to transform the OCP into a nonlinear programming (NLP) problem with constraints. Here we used an orthogonal collocation method, precisely the Gauss pseudospectral method. This method is efficiently implemented in the open-source Matlab software As explained above, the inner loop of the bi-level problem requires solving direct OCPs for a given Apart from the inverse approach, a verification was also conducted by directly analyzing the predictions of each single cost model  showed that the subjects used only Subjects could reach wherever they desired on the bar . Therefore, it appeared important to verify whether their behavior was consistent or not. An analysis of the consistency index  clearly adopted a behavior optimizing a well-characterized hybrid cost, essentially mixing the absolute work and the angular acceleration . Consequently, for the further investigations using direct optimal control, we included this identified composite cost to compare it with the basis costs. Since the ratio between the weighting coefficients of energy and angle acceleration was roughly 10∶1, the hybrid cost was chosen to be A preliminary inspection of To quantify the matching between models and real data an analysis of the finger path was conducted, including all subjects and all initial postures. The difference between simulated and measured paths was first measured through the area  see . It is aA specific analysis of task-relevant parameters was also performed see . The mosConcerning the shape of the path .In this study we investigated the cost combination hypothesis for the optimal control of arm movements. To this aim we adopted an inverse optimal control methodology to identify the cost function that best replicates the participants' behavior during a task with target redundancy. Inverse optimal control revealed that the observed hand paths were close to the solutions of an optimal control problem relying on a composite cost function mixing mechanical energy expenditure and joint smoothness. This hybrid cost was found to fit well the experimental data, not only much better than any single other cost under comparison, but also better than any other linear combination of the candidate costs.Reaching to objects involving target redundancy is a very common task in everyday life. For instance, grasping a small ball can be achieved through many task-equivalent solutions, depending on how one chooses to put his fingers on it. In such a case, like for the bar, target point discriminability is greatly reduced and, therefore, decision confidence in the brain decreases Certain limitations however remain such as the uniqueness of the solution and the problem of local minima, which are hardly avoidable in the context of complex non-linear optimal control. Uniqueness of the solution has been addressed recently in static inverse optimization Inverse optimal control results showed that most subjects (15/20) adopted a behavior which essentially corresponded to a strict mixture of two subjective costs (absolute work of torques and angular acceleration energy). More precisely, mixing these two costs was found to fit better the observed hand paths than other linear combinations of the eight candidate costs we considered. Each subject could use a different weighting of those two costs but on average their contribution to the total movement cost was roughly the same . These findings were quite robust as confirmed by the results when using an alternative metric  torques needed to drive the movement has to reach a minimum. This model was mainly influenced by the maximum exploitation of gravity to reach the bar. The minimum torque change model, which maximizes smoothness in the dynamic space, also predicted non-biological paths since even the movement direction was poorly predicted in most cases. Similarly, the minimum effort model, optimizing the amount of neural input to control the movement, was unable to predict some basic features of the recorded arm trajectories. Other simulations showed that neither modeling agonist/antagonist muscles as low-pass filters nor separating the control of static  and dynamic forces (speed-related) could improve drastically the model predictions for this task  may result in a “not so large” torque change cost. Due to nonlinearities, it is nevertheless difficult to establish general rules. In the same vein, the similarity of joint acceleration and jerk costs is the reason why, in this study, we only conclude about the optimization of a quite generic “joint smoothness” term. In general, objective costs are also optimized for the task achievement per se and are thus complementary to subjective costs. Consider for instance the task of drawing a straight line on a sheet on paper. In this case, optimizing the jerk at hand would be the best solution to produce such a path. Energy and joint smoothness costs could however be integrated in the motor plan to determine the remaining degrees of freedom . Conversely, when trying to jump at a maximal height, it is likely that the weight given to the energy cost is decreased. Joint smoothness should instead remain still present to avoid injuries and fulfill goal achievement. We propose therefore that planning is a dynamic process weighting flexible objective costs  with more deeply anchored subjective costs. This combination of cost would crucially yield the necessary flexibility for the sensorimotor system to achieve a variety of tasks, which agrees with other recent results obtained in the stochastic optimal control context We still remain ignorant about the detailed neural mechanisms underlying such flexible combinations of cost functions. We may suggest however that subjective cost functions are encoded at a low level of the CNS, while objective cost functions are determined at a higher level. Autonomic motor system that control basic involuntary function through the sympathic system dealing with body's resources might regulate the selection and combination of costs. In other words, we speculate that hypothalamus, reticular formation and spinal cord, which ensure the regulation of internal body states contributing to overall physiological balance, would control the optimization process, however remaining under the influence of descending pathways. Such a hierarchical view of motor planning and control is reminiscent of the theory proposed in Text S1General settings of the optimal control problems and details about their solutions.(PDF)Click here for additional data file.Text S2Materials to verify and support the results described in the main text.(PDF)Click here for additional data file.