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5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012 -- 11 July 2012 through 14 July 2012 -- Shenyang, This paper presents an intelligent multiple-controller framework for the integrated control of throttle, brake and steering subsystems of realistic validated nonlinear autonomous vehicles. In the developed multiple-controller framework, a fuzzy logic-based switching and tuning supervisor operates at the highest level of the system and makes a switching decision on the basis of the required performance measure, between an arbitrary number of adaptive controllers: in the current case, between a conventional Proportional-Integral- Derivative (PID) controller and a PID structure-based pole-zero placement controller. The fuzzy supervisor is also able to adaptively tune the parameters of the multiple controllers. Sample simulation results using a realistic autonomous vehicle model demonstrate the ability of the intelligent controller to both simultaneously track the desired throttle, braking force, and steering changes, whilst penalising excessive control actions - with significant potential implications for both fuel and emission economy. We conclude by demonstrating how this work has laid the foundation for ongoing neuro-biologically motivated algorithmic development of a more cognitively inspired multiple-controller framework. © 2012 Springer-Verlag., Engineering and Physical Sciences Research Council: EP/I009310/1

Computational neuroscience and Systems Biology are comparatively young, interdisciplinary areas in the life sciences, dealing with, arguably, the most complex systems we know of. All these factors conspire to make the status, and process, of building models in these areas problematic. Oftentimes modellers make tacit assumptions about their general approach, but we would argue that such assumptions should be explicit, and that establishing sound methodological principles is an important foundation stone for making progress.

Huntington's is a genetic, progressive neuro-degenerative disease, causing massive network degradation effecting the medium spiny neurons of the striatum in the basal ganglia (a set of sub-cortical nuclei, believed to be critical for action selection). Despite substantial striatal cell atrophy, some cognitive functions have been shown to improve in manifest Huntington's disease patients over healthy and pre-symptomatic Huntington's disease patients. Using a detailed model of the striatal microcircuit, we show that combining current ideas about the underlying causes of the disease could lead to the counter-intuitive result of improved competitive network dynamics for signal selection.

The study of active and passive neuronal dynamics usually relies on a sophisticated array of electrophysiological, staining and pharmacological techniques. We describe here a simple complementary method that recovers many findings of these more complex methods but relies only on a basic patch-clamp recording approach. Somatic short and long current pulses were applied in vitro to striatal medium spiny (MS) and fast spiking (FS) neurons from juvenile rats. The passive dynamics were quantified by fitting two-compartment models to the short current pulse data. Lumped conductances for the active dynamics were then found by compensating this fitted passive dynamics within the current-voltage relationship from the long current pulse data. These estimated passive and active properties were consistent with previous more complex estimations of the neuron properties, supporting the approach. Relationships within the MS and FS neuron types were also evident, including a graduation of MS neuron properties consistent with recent findings about D1 and D2 dopamine receptor expression. Application of the method to simulated neuron data supported the hypothesis that it gives reasonable estimates of membrane properties and gross morphology. Therefore detailed information about the biophysics can be gained from this simple approach, which is useful for both classification of neuron type and biophysical modelling. Furthermore, because these methods rely upon no manipulations to the cell other than patch clamping, they are ideally suited to in vivo electrophysiology.

Redgrave and Gurney [1] have recently proposed a theory of action discovery in which the occurrence of an unexpected salient sensory event causes short-latency phasic dopamine (DA) neuron activity. Due to convergent projections from DA neurons and cortical neurons onto the striatum, the DA signal reinforces (i.e., biases the animal to repeat) preceding behaviour. In the case that the animal has caused the sensory outcome, continued repetition, along with variations in behaviour, allows the animal to discover the novel action-outcome contingency. A key aspect of this theory is that the DA signal decreases as the action-outcome association becomes predictable. In contrast is the well-known theory that the DA signal specifically represents a reward prediction error [2], and that the tendency to select behaviour is based on its ‘value’ (often represented as the expected subsequent cumulative costs and rewards). The latter theory is attractive in part because it conforms with Reinforcement Learning (RL), a computational formulation of learning from the consequences of actions [3]. RL describes well behaviour observed during many types of goal-directed tasks and allows behaviour to be described as optimal given a particular reward/cost function (e.g., cost per movement). However, is a notion of optimality necessary to produce all behaviour thought to derive from DA-mediated mechanisms? If so, a representation of every movement’s cost and value is required to govern learning. The theory of action discovery [1] suggests that some behaviour is developed through much simpler mechanisms: the occurrence of the salient event rather than a prediction of some quantity (value) associated with each action selected. Here, we implement such a scheme in a learning agent that chooses discrete actions within a discrete state environment (analogous to pushing buttons). Only when the agent has reached a particular state (the salient event) is a reinforcement signal delivered. The tendency to select every action that was selected en route to that state is increased, but with a learning rate that decreases with temporal distance from the reinforcement signal (i.e., an eligibility trace). Importantly, no notion of optimality is represented. Figure ​Figure11 below illustrates the development of behavior due to our scheme (black line) and an RL scheme based on cost (grey). In both, the number of actions chosen en route to the salient event decreases toward a minimum. Such behavior is usually taken as evidence that it approaches optimal given some cost function on which learning depends. However, with our scheme, such behavior emerges ‘for free’ from the statistics of behavior over repeated trials. Actions that lie along the most direct path simply get reinforced at a greater rate than other actions. Figure 1 If, as suggested by [1], learning is truncated after the outcome becomes predictable (via gradual extinction of phasic DA neuron activity), behavior under both schemes will stabilize to the most direct path. However, our scheme also predicts that, without such a truncation, prolonged ‘overlearning’ will result in suboptimal behavior where there is an increasing tendency to select spurious actions (see figure ​figure1).1). Such behavior may be analogous to behavior arising from certain types of DA dysfunction. We discuss the implications of this learning scheme within the context of action discovery and describe additional predicted behaviors.

Previously, it has been shown experimentally that the psychophysical law known as Pieron’s Law holds for color intensity and that the size of the effect is additive with that of Stroop condition (Stafford et al., 2011). According to the additive factors method (Donders, 1868–1869/1969; Sternberg, 1998), additivity is assumed to indicate independent and discrete processing stages. We present computational modeling work, using an existing Parallel Distributed Processing model of the Stroop task (Cohen et al., 1990) and a standard model of decision making (Ratcliff, 1978). This demonstrates that additive factors can be successfully accounted for by existing single stage models of the Stroop effect. Consequently, it is not valid to infer either discrete stages or separate loci of effects from additive factors. Further, our modeling work suggests that information binding may be a more important architectural property for producing additive factors than discrete stages.

A general problem in robotics is how to best utilize sensors to classify the robot's environment. The BIOTACT project (BIOmimetic Technology for vibrissal Active Touch) is a collaboration between biologists and engineers that has led to many distinctive robots with artificial whisker sensing capabilities. One problem is to construct classifiers that can recognize a wide range of whisker sensations rather than constructing different classifiers for specific features. In this article, we demonstrate that a stationary naive Bayes classifier can perform such a general classification by applying it to various robot experiments. This classifier could be a key component of a robot able to learn autonomously about novel environments, where classifier properties are not known in advance.

Navigating the visual world is a challenging problem for autonomous agents, which must be flexible, robust, and preferably easily extensible in order to meet changing task demands. Here, we outline the rationale for an approach to constructing such agents biomimetically, even if they appear ‘over engineered’ at first glance, using the problem of gaze redirection in an attentional task.

Some of the techniques of System Identification (SID) in control theory have been used to train feedforward nets of units which use pulse frequency coding to communicate their output. The algorithm makes use of a vector of signals at each training step but, unlike back error propagation, these are broadcast to the net as a whole. This paper investigates the nature of training in two types of net. One uses a stochastic variant of the semilinear Threshold Logic Unit (TLU), while the other uses digital units defined by populations of values at the vertices of the n-cube. An investigation is made into the effect of training parameters on learning in both cases, leading to a comparison of the two net types on a single problem (the 838 encoder). The greater functionality of the cubic nodes leads to reduced training times and a difference in behavior under change of training parameters.

Novelty detection would be a useful ability for any autonomous robot that seeks to categorize a new environment or notice unexpected changes in its present one. A biomimetic robot (SCRATCHbot) inspired by the rat whisker system was here used to examine the performance of a novelty detection algorithm based on a “naive” implementation of Bayes rule. Naive Bayes algorithms are known to be both efficient and effective, and also have links with proposed neural mechanisms for decision making. To examine novelty detection, the robot first used its whiskers to sense an empty floor, after which it was tested with a textured strip placed in its path. Given only its experience of the familiar situation, the robot was able to distinguish the novel event and localize it in time. Performance increased with the number of whiskers, indicating benefits from integrating over multiple streams of information. Considering the generality of the algorithm, we suggest that such novelty detection could have widespread applicability as a trigger to react to important features in the robot's environment.

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin---Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.

Action selection in animals requires rapid decision making that can discriminate the most salient requests for behavioral expression. The basal ganglia (BG) are believed to play a critical role in resolving competition between these requests [1] and, recently it has been proposed that the BG and cortex, taken together, implement a decision algorithm known as the multi-hypothesis sequential probability ratio test (MSPRT) [2]. Here, the cortex first integrates noisy ‘evidence’ indicating salience of action requests. The BG then examine this integrated evidence, and report the channel with maximal mean salience. The MSPRT is optimal in the sense that it guarantees the smallest decision time for a given error. The signals used in [2] assume cortical evidence - as neural firing rates - is supplied by Gaussian distributed signals. However, it is known that such rates are not distributed normally. To remedy this we have deduced three new forms for MSPRT that are based on more biologically realistic input signals. They include the Inverse Gaussian, Gamma and Lognormal distributions. All implementations preserve a form which could be mapped, in principle, onto the anatomy of the BG. Figure ​Figure11 shows an example of the performance of the MSPRT models across all distributions, together with the so called race model. All variants used the same input statistics (mean and variance). Counterintuitively, the relative performance of the models did not change with base level of salience or evidence. Figure 1 Performance of original Gaussian MSPRT[2], new implementations of MSPRT, and Race model. Performance is measured as decision time against number of alternatives for selection. The selection criterion was 1% error rate. The results are averaged across ... A critical parameter in the algorithm is the time step Δt used to sample the cortical input. The Gaussian, Inverse Gaussian, and Gamma cases are generated as stable Levy processes and may be scaled indefinitely with decreasing Δt. However, unlike the MSPRT with Gaussian signals, we show that decision time for the inverse Gaussian, and Gamma decreases with Δt. Figure ​Figure11 use a Δt = 1 ms for all models and clearly shows a performance advantage for the cases with more realistic distributions (the lognormal case is shown with similar scaling even though it has not an associated Levy process). Indefinitely small decision times may appear unrealistic, but we present an interpretation of cortical sampling that relates Δt to the inter-spike intervals of an ensemble of neural afferents impinging on the cortical ‘integrators’. This and, other mechanisms, provide a natural lower bound for Δt. We conclude that neurons may take advantage of the properties of natural spike trains to enhance decision making in the cortex-BG complex.

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved.

Many rodents use their whiskers to distinguish objects by surface texture. To examine possible mechanisms for this discrimination, data from an artificial whisker attached to a moving robot was used to test texture classification algorithms. This data was examined previously using a template-based classifier of the whisker vibration power spectrum [1]. Motivated by a proposal about the neural computations underlying sensory decision making [2], we classified the raw whisker signal using the related ‘naive Bayes’ method. The integration time window is important, with roughly 100ms of data required for good decisions and 500ms for the best decisions. For stereotyped motion, the classifier achieved hit rates of about 80% using a single (horizontal or vertical) stream of vibration data and 90% using both streams. Similar hit rates were achieved on natural data, apart from a single case in which the performance was only about 55%. Therefore this application of naive Bayes represents a biologically motivated algorithm that can perform well in a real-world robot task.

We present a biologically inspired neural network model of visual orienting (using saccadic eye movements) in which targets are preferentially selected according to their reward value. Internal representations of visual features that guide saccades are developed in a self-organised map whose plasticity is modulated under reward. In this way, only those features relevant for acquiring rewarding targets are generated. As well as guiding the formation of feature representations, rewarding stimuli are stored in a working memory and bias future saccade generation. In addition, a reward prediction error is used to initiate retraining of the self-organised map to generate more efficient representations of the features when necessary.

The dynamics and training of recurrent nets which make use of probabilistic nodes based on Boolean functions are explored. These are shown to be equivalent to sigma-pi units when using binary inputs and have the advantage of an immediate implementation in readily available digital hardware. The underlying hypercube structure of the Boolean address space is used to describe the process of generalisation and also sheds light on the role of hidden units. This is made more precise with the aid of the concept of hypercube order statistics. Training algorithms are described which make use of a node time-integration apparatus to promote near optimal cube structures in a natural way.

Lower motion thresholds for discriminating opposing motion directions were compared for one dimensional (grating) and two dimensional (plaid) stimuli in central and peripheral vision. The results were consistent with a two-stage model of motion sensitivity in which threshold-limiting noise occurs at both stages, and the speed as well as the direction of the resultant motion is determined by intersection-of-constraints (IOC) from the component motions. The results do not support a purely geometric interpretation of the IOC model, in which thresholds for plaid stimuli are related to thresholds of component gratings by a geometric factor. Neither do the data favour explanations in which local luminance features (i.e. blobs) are detected and their velocity determined. Monte-Carlo simulations of the two-stage process predict thresholds across variations in component direction, contrast, and visual field eccentricity. Lower motion thresholds for gratings and plaids both follow a saturating function of contrast; the fit between grating and plaid data is improved when the plaid contrast is expressed in terms of the contrast of its components. Although less contrast saturation was found in the periphery, in relative terms, plaid and grating motion thresholds were similar in central and peripheral vision, implying cortical magnifications are similar for mechanisms which process grating and plaid motion.

Learning convergence is demonstrated for networks of nodes which are defined by a population of values at the vertices of the n-dimensional hypercube. These are functionally equivalent to higher order nodes or sigma-pi units but have the potential to be implemented in readily available memory components. The cube based structure also offers insight into the process of generalization. Three algorithms are discussed: reward-penalty, back propagation, and a new one based on system identification in control theory. It is shown that reward-penalty style techniques emerge as a special case of system identification.

We recently proposed that short-latency, sensory-evoked dopamine release is critical for learning action-outcome causality [1]. If an action causes an unexpected outcome associated with a phasic visual event, there will be a phasic burst of dopamine in the striatum. Subsequent reinforcement of the striatal response to the cortical representation of the action then makes the selection of the action (and its outcome) more likely; i.e. there is "repetition biasing" of action selection. This, in turn, facilitates associative learning of the action-outcome pairing elsewhere in the brain. Here, we present a model of cortico-striatal plasticity in medium spiny neurons (MSNs) that could form the basis for a quantitative account of action-outcome learning in basal ganglia.

INTRODUCTION In the visual system, attending to important objects in the visual field relies on the transfer of top-down, object-based task information to the spatially organised areas of cortex. How this occurs and the method by which this information can influence the dorsal stream and redirect gaze are not well understood. Current models of the ventral stream mostly focus on the feed-forward mechanisms involved and current feedback models do not seem to address the issue of object-space binding in a comprehensive and plausible manner. METHODS We investigated these questions using the following modeling framework. A bidirectional, ventral stream object recognition hierarchy up to anterior inferior temporal cortex (AIT) from primary visual cortex (V1) and a model of dorsal stream to frontal eye fields (FEF) with our previously developed oculomotor system [1]. Selection is performed in both the object-based mapping of AIT [2] and the spatial mapping of FEF [3] by basal ganglia loops [4]. Modeling of the ventral stream consists of a hierarchy of increasingly spatially invariant cortical areas linked by both feed-forward excitatory and feedback connections. Within each receptive field, there is a competition to represent the strongest and thus most likely representation for that region, which can be biased by the feedback from higher visual areas. Three models of feedback attention mechanism were tested: additive feedback, shunting (multiplicative) feedback and a shunt "gating" of feedback by feed-forward. The model was tested using a simple visual world (colored "flags") that nevertheless challenged all the main competencies being investigated. Performance was measured by (i) eliciting saccadic "behavior" in simulated visual search with different numbers of distractors, and (ii) target segmentation in cluttered scenes within a fixed time window. RESULTS In the target segmentation task, the additive feedback model consistently fails to bind the AIT representation of the object to the correct location on the visual field. The shunting model was able to segment 58% of scenes while the gating model was most successful (83%) (Figure 1). We then took the most successful (gating) model and challenged it with a conjunction visual search task. Here, by simulating models trained and naive to the target stimulus, we showed that subsequent learning of a combined representation of an untrained target stimulus can explain the experimentally observed decrease in the slope of reaction time against number of distractors for that target (Figure 2) [5].

Estimation of the maximal ion channel conductances in Hodgkin-Huxley models from patch clamp data is a difficult optimization problem. Automating this process promises high-throughput computational modeling of use to both experimenters (for rapid feedback on their experimental preparations) and modelers (for investigating the details of neuronal function). Hitherto, attempts to do this have focused on stochastic searches such as genetic algorithms and simulated annealing [1-3]. Such methods give robust estimates of model parameters but converge slowly or need to sample a large population of test cases in parallel, and therefore require substantial computing resources. Meanwhile, deterministic searches (e.g. the simplex search and conjugate gradient descent) are far more computationally efficient but are hampered by the complex fitting landscape of the optimization problem. As such, there is no general neuronal parameter-fitting algorithm that is both computationally efficient and robust.

Loss of dopamine cells in Parkinson's Disease and its ani-mal models leads to profound motor deficits. An intactdopamine system also seems to be critical for many formsof learning [1]. Much work on understanding these rolesof dopamine has focused on the striatum, the main inputnucleus of the basal ganglia, as the striatum is the mainlocus of dopamine's action in the verterbrate brain [2].Damage to the striatum itself also impairs both motoraction and learning [3]. Thus, the twin problems of under-standing the computational roles of dopamine and thestriatum are inseparably intertwined.Understanding dopamine's effects on the complex striatalmicrocircuit ideally requires large-scale models that repli-cate the neuron types, numbers, and connectivity at one-to-one scale. To build at such scales, we require individualneuron models that are simple enough to be computa-tionally tractable, but sufficiently complex to capture keymembrane properties that contribute to the characteristicbehavior of a neuron species. Our neuron model of choiceis the recent canonical spiking model of Izhikevich [4].However, it has not yet been extended to account for theaction of neuromodulators.We extend the striatal medium spiny (MSN) and fast-spik-ing (FS) interneuron models of [5] to account fordopaminergic modulation of intrinsic ion channels andsynaptic inputs. We use data from a recent 189 compart-ment model of the MSN [3] to tune our simple model ofthat neuron under both current injection and spikinginput regimes with varying activation of dopamine D1-and D2-type receptors. The reduced models capture theinput-output relationships for both current injection andspiking input with remarkable accuracy. We derive a fullset of stability properties for the original and dopaminemodulated forms of the MSN model. We use these toestablish that the dopamine models do not change thestability properties and hence the models predict that theMSN is not bistable in either baseline or dopamine-satu-rated conditions. Our extensions to the simple model ofthe FS interneuron are consistent with the existing data,but tuning the new parameters is made difficult by thelack of quantitative results from experimental work. Ourwork thus establishes reduced yet accurate dopamine-modulated forms of MSN and FS interneuron models,suitable for use in large-scale models of the striatum.Moreover, these also provide a tractable framework forfurther study of dopamine's effects on individual neuroncomputation.

The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit's connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells. (C) 2009 Elsevier Ltd. All rights reserved.

How are we to go about understanding the computations that underpin cognition? Here we set out a methodological framework that helps understand different approaches to solving this problem. We argue that a very powerful stratagem is to attempt to ‘reverse engineer’ the brain and that computational neuroscience plays a pivotal role in this programme. En passant, we also tackle the oft-asked and prior question of why we should build computational models of any kind. Our framework uses four levels of conceptual analysis: computation, algorithm, mechanism and biological substrate. As such it enables us to understand how (algorithmic) AI and connectionism may be recruited to help propel the reverse-engineering programme forward. The framework also incorporates the notion of different levels of structural description of the brain, and analysis of this issue gives rise to a novel proposal for capturing computations at multiple levels of description in a single model.

Loss of dopamine from the striatum can cause both profound motor deficits, as in Parkinsons's disease, and disrupt learning. Yet the effect of dopamine on striatal neurons remains a complex and controversial topic, and is in need of a comprehensive framework. We extend a reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tune our D1 and D2 receptor MSN models using data from a recent large-scale compartmental model. The new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. We analytically derive a full set of equilibrium points and their stability for the original and dopamine modulated forms of the MSN model. We find that the stability types are not changed by dopamine activation, and our models predict that the MSN is never bistable. Nonetheless, the MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors. Our models also predict a more pronounced membrane potential bimodality following D1 receptor activation. This work thus establishes reduced yet accurate dopamine-modulated models of MSNs, suitable for use in large-scale models of the striatum. More importantly, these provide a tractable framework for further study of dopamine's effects on computation by individual neurons.

There are two modes of control recognised in the cognitive psychological literature. Controlled processing is slow, requires serial attention to sub-tasks, and requires effortful memory retrieval and decision making. In contrast automatic control is less effortful, less prone to interference from simultaneous tasks, and is driven largely by the current stimulus. Neurobiological analogues of these are goal-directed and habit-based behaviour respectively. Here, we suggest how these control modes might be deployed in an engineering solution to Automatic Vehicle Control. We present pilot data on a first step towards instantiating automatised control in the architecture, and suggest a synergy between the engineering and biological investigation of this dual-process approach.

This paper describes emergent neurobiological characteristics of an intelligent multiple-controller that has been developed for controlling the throttle, brake and steering subsystems of a validated vehicle model. Simulation results demonstrate the effectiveness of the proposed approach. Importantly, the controller exhibits discrete behaviours, governed by its two component controllers. These controllers are selected according to task demands by a fuzzy-logic based supervisor. The system therefore displays `action selection' under central switched control, as has been proposed to take place in the vertebrate brain. In addition, the supervisor and modular controllers have analogues with the higher and lower levels of functionality associated with the strata in layered brain architectures. Several further similarities are identified between the biology and the vehicle controller. We conclude that advances in neuroscience and control theory have reached a critical mass which make it timely for a new rapprochement of these disciplines.

BackgroundMany technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model--the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified.Methodology/principal findingsWe defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S>1--an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process.Conclusions/significanceWe have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.

We present an application of a massively parallel processor array VLSI circuit to the implementation of neural networks in complex architectural arrangements. The work was motivated by existing biologically plausible models of a set of sub-cortical nuclei -the basal ganglia. The model includes 5 layers, each consisting of 16384 leaky integrator neurons, with inter-layer synaptic weights forming various one-to-one and diffuse connectivity patterns. The architecture of the SIMD processor array allows all the neurons per layer to be updated simultaneously. The performance of the processor array chip in simulating the model is compared with the original model being executed on a computer workstation. It is demonstrated that in this application the chip outperforms the workstation by five orders of magnitude in terms of computational performance and seven orders of magnitude in terms of energy efficiency, providing a high-speed, low-power, compact hardware platform for possible embedded robotic applications.

The search for the neural substrate of vertebrate action selection has focused on structures in the forebrain and midbrain, and particularly on the group of sub-cortical nuclei known as the basal ganglia. Yet, the behavioural repertoire of decerebrate and neonatal animals suggests the existence of a relatively self-contained neural substrate for action selection in the brainstem. We propose that the medial reticular formation (mRF) is the substrate's main component and review evidence showing that the mRF's inputs, outputs and intrinsic organization are consistent with the requirements of an action-selection system. The internal architecture of the mRF is composed of interconnected neuron clusters. We present an anatomical model which suggests that the mRF's intrinsic circuitry constitutes a small-world network and extend this result to show that it may have evolved to reduce axonal wiring. Potential configurations of action representation within the internal circuitry of the mRF are then assessed by computational modelling. We present new results demonstrating that each cluster's output is most likely to represent activation of a component action; thus, coactivation of a set of these clusters would lead to the coordinated behavioural response observed in the animal. Finally, we consider the potential integration of the basal ganglia and mRF substrates for selection and suggest that they may collectively form a layered/hierarchical control system.

In insects the optomotor response produces a motor action to compensate for unintended body rotation. The response is generally modeled as a Reichardt-Hassenstein (HSD) or Barlow-Levick (BL) correlation detector, as anatomical and physiological studies in Drosophila melanogaster have demonstrated consistent neural pathways and responses in the insect brain [1]. Recordings from the descending neurons carrying the optomotor response signal in honeybees indicate an ordering effect for different stimulus spatial frequencies, with a greater response with decreasing frequency [2] (see Figure ​Figure1A),1A), which is not accounted for by HSD or BL correlation detectors. Figure 1 A. Cartoon of ordering effect indicated by honeybee descending neuron responses. Spatial frequency decreases from blue to green. B. Model diagram showing annealed synapses (coloured, same colours indicate same synaptic conductance). C. Slice of annealing ... We present a model in the SpineML format of the optomotor system, using Izhikevich point neurons tuned to match the respective physiological responses, which is shown in Figure ​Figure1B.1B. To examine if the model reproduces the ordering effect found in the honeybee we performed simulated annealing on four conductance values in the model, as shown in Figure ​Figure1.1. The objective function is designed to maximize: correct ordering; a 10Hz maximum response; and contrast between responses to forward and reverse motion. A. The data was imported into a commercial software package (MATLAB 7.14, The MathWorks Inc., Natick, MA, 2012) for analysis and interpolated onto a 414 grid. A 3D slice of this 4D grid can be seen in Figure ​Figure1C.1C. Spatial frequencies of 32.7, 18.9 and 9.5 Hz are used. A stable region in which a high value of the objective function, and thus correct spatial frequency ordering, could be obtained was found. In the stable region the onset pathway activity is low, leading to offset activity dominating. A RHD using the model up to the Medulla does not show correct ordering.

The basal ganglia (BG) have long been implicated in both motor function and dysfunction. It has been proposed that the BG form a centralized action selection circuit, resolving conflict between multiple neural systems competing for access to the final common motor pathway. We present a new spiking neuron model of the BG circuitry to test this proposal, incorporating all major features and many physiologically plausible details. We include the following: effects of dopamine in the subthalamic nucleus (STN) and globus pallidus (GP), transmission delays between neurons, and specific distributions of synaptic inputs over dendrites. All main parameters were derived from experimental studies. We find that the BG circuitry supports motor program selection and switching, which deteriorates under dopamine-depleted and dopamine-excessive conditions in a manner consistent with some pathologies associated with those dopamine states. We also validated the model against data describing oscillatory properties of BG. We find that the same model displayed detailed features of both γ-band (30–80 Hz) and slow (∼1 Hz) oscillatory phenomena reported by Brown et al. (2002) and Magill et al. (2001), respectively. Only the parameters required to mimic experimental conditions (e.g., anesthetic) or manipulations (e.g., lesions) were changed. From the results, we derive the following novel predictions about the STN–GP feedback loop: (1) the loop is functionally decoupled by tonic dopamine under normal conditions and recoupled by dopamine depletion; (2) the loop does not show pacemaking activity under normal conditionsin vivo(but does after combined dopamine depletion and cortical lesion); (3) the loop has a resonant frequency in the γ-band.

The implementation of a tactile sensory system for neuromorphic signal processing applications is presented. It has been developed by modelling the structure and behaviour of real rodent facial vibrissae. The primary afferents have been modelled using empirical data taken from electrophysiological measurements, and implemented in real time using hardware processors (DSP/FPGA). Pipelining techniques were employed to maximise the utility of the FPGA hardware. The system is to be integrated into a more complete whisker sensory model, including neural structures within the central nervous system, which can be used to orientate a mobile robot.

Both a FPGA implementation of a rodent?s tactile sensory system and a neural FPGA based hardware processor, mimicking the brainstem behaviour are presented. They have principally been designed using biological considerations. The two systems are being ported on a single FPGA platform and will ultimately be embedded on a mobile robot, which will operate in real world environments for object recognition and surface textural discrimination purposes.

Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called ‘small-world’ and ‘scale-free’ networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans . Here, we extend this work to a specific neural circuit of the vertebrate brain—the medial reticular formation (RF) of the brainstem—and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement.

The implementation of a large scale, leaky-integrate-and-fire neural network processor using the Xilinx Virtex-II family of field programmable gate array (FPGA) is presented. The processor has been designed to model biologically plausible networks of spiking neurons in real-time to assist with the control of a mobile robot. The real-time constraint has led to a re-evaluation of some of the established architectural and algorithmic features of previous spiking neural network based hardware. The design was coded and simulated using Handel-C hardware description language (HDL) and the DK3 design suite from Celoxica. The processor has been physically implemented and tested on a RC200 development board, also from Celoxica.

A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented. This has been designed, as part of a collaborative project, to investigate and implement biologically plausible models of the rodent vibrissae based somatosensory system to control a robot. An emphasis has been made on hard real-time performance of the processor, as it is to be used as part of a feedback control system. This has led to a revision of some of the established modelling protocols used in other hardware spiking neural network processors. The underlying neuron model has the ability to model synaptic noise and inter-neural propagation delays to provide a greater degree of biological plausibility. The processor has been demonstrated modelling real neural circuitry in real-time, independent of the underlying neural network activity.