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PubMed2002

Evolutionary autonomous agents: a neuroscience perspective.

In this article, I discuss the use of neurally driven evolutionary autonomous agents (EAAs) in neuroscientific investigations. Two fundamental questions are addressed. Can EAA studies shed new light on the structure and function of biological nervous systems? And can these studies lead to the development of new tools for neuroscientific analysis? The value and significant potential of EAA modelling in both respects is demonstrated and discussed. Although the study of EAAs for neuroscience research still faces difficult conceptual and technical challenges, it is a promising and timely endeavour.

Animals↗
PubMed2006

Towards systemic theories in biological psychiatry.

Although still rather controversial, empirical data on the neurobiology of schizophrenia have reached a degree of complexity that makes it hard to obtain a coherent picture of the malfunctions of the brain in schizophrenia. Theoretical neuropsychiatry should therefore use the tools of theoretical sciences like cybernetics, informatics, computational neuroscience or systems science. The methodology of systems science permits the modeling of complex dynamic nonlinear systems. Such procedures might help us to understand brain functions and the disorders and actions of psychiatric drugs better.

Antipsychotic Agents↗
PubMed2006

The importance of different timings of excitatory and inhibitory pathways in neural field models.

In this paper, we consider a neural field model comprised of two distinct populations of neurons, excitatory and inhibitory, for which both the velocities of action potential propagation and the time courses of synaptic processing are different. Using recently-developed techniques, we construct the Evans function characterising the stability of both stationary and travelling wave solutions, under the assumption that the firing rate function is the Heaviside step. We find that these differences in timing for the two populations can cause instabilities of these solutions, leading to, for example, stationary breathers. We also analyse "anti-pulses", a novel type of pattern for which all but a small interval of the domain (in moving coordinates) is active. These results extend previous work on neural fields with space-dependent delays, and demonstrate the importance of considering the effects of the different time-courses of excitatory and inhibitory neural activity.

Animals↗
PubMedDate not supplied

Interhemispheric integration: II. Symmetry and convergence of the corticostriatal projections of the left and the right principal sulcus (PS) and the left and the right supplementary motor area (SMA) of the rhesus monkey.

The relationship between the termination zones of projections from paired homotopic areas of the frontal lobes was examined in the caudate nucleus and the putamen of the macaque monkey. Injections of WGA-HRP and tritiated amino acids were made in topographically matched regions of the principal sulcus (PS) or of the supplementary motor area (SMA) in each hemisphere, such that the projections from the same area on each side were differentially labeled in the same animal. Adjacent sections through the neostriatum were processed for the respective tracers, permitting the relationship between the converging projections to be defined. The topographic distribution and strength of ipsilateral corticostriatal projections observed for separately labeled left and right hemispheres were strikingly similar. Projections from the left and the right PS terminated preferentially in central parts of the left and right neostriata, respectively, while projections of the left and right SMAs terminated preferentially in dorsolateral parts of respective left and right neostriata. Therefore, little evidence for asymmetry of corticostriatal projections was found. The projections of the left and the right PS to the same neostriatum were also compared. Remarkably, whether in the left or right hemisphere, projections from the left and the right PS were in precise register in topographically specific territories of the caudate and putamen. Likewise, projections of the left and right SMAs converged in both the left and right neostriata. Such convergence allows for a remarkable degree of interhemispheric integration in the descending corticostriatal networks.

Animals↗
PubMed1998

Unspecific long-term potentiation can evoke functional segregation in a model of area 17.

It has been shown recently in rat hippocampus that the synapse specificity of Hebbian long-term potentiation breaks down at short distances (< 100 microm). Using a neural network model we show that this unspecific component of long-term potentiation can be responsible for the robust formation and maintainance of cortical organization during activity-driven development. When the model is applied to the formation of orientation and ocular dominance in visual cortex, addition of an unspecific component to standard Hebbian learning, in combination with a tendency of left-eye and right-eye driven synapses to initially group together on the postsynaptic neuron, induces the simultaneous emergence and stabilization of ocular dominance and of segregated, oriented ON/OFF subfields. Since standard Hebbian learning cannot account for the simultaneous stabilization of both structures, unspecific LTP thus induces a qualitatively new behaviour. Since unspecific LTP only acts between synapses which are locally clustered in space, our results imply that details of the local grouping of synapses on the dendritic arbors of postsynaptic cells can considerably influence the formation of the cortical functional organization at the systems level.

Algorithms↗
PubMed2001

Input minimization: a model of cerebellar learning without climbing fiber error signals.

The cerebellum is critical for motor learning. Current cerebellar learning models follow the Marr/Albus paradigm, in which climbing fibers provide error signals that shape plastic synapses between parallel fibers and Purkinje cells. However, climbing fibers have slow and largely random discharge, and seem unlikely to provide error signals with resolution sufficient to guide cerebellar learning. Parallel fibers carry error signals and could direct the plasticity of their own synapses, but the error signals are carried along with other signals. This report presents the new input minimization (InMin) model, in which Purkinje cells reduce error by minimizing their overall parallel fiber input. The slowly, randomly firing climbing fiber provides only synchronization pulses. InMin offers an alternative that can unify cerebellar findings.

Action Potentials↗
PubMed2003

New neural circuits for robot phonotaxis.

W. Grey Walter built robotic systems to improve understanding of biological systems. In that tradition, this paper reports ongoing work on a robot model of cricket sound localization. The main advances are the inclusion of a much larger range of neuroethological detail, and the investigation of multimodal influences on the behaviour. The former allows exploration of the functionality of identified neurons in the insect, including the possible roles of multiple sensory fibres, mutually inhibitory connections, and brain neurons with pattern-filtering properties. The latter focuses on the inclusion of an optomotor stabilization response, and how this might improve tracking, particularly under conditions of random disturbance.

Animal Communication↗
PubMed1999

Categorization in fully connected multistate neural network models.

The categorization ability of fully connected neural network models, with either discrete or continuous Q-state units, is studied in this work in replica symmetric mean-field theory. Hierarchically correlated multistate patterns in a two level structure of ancestors and descendents (examples) are embedded in the network and the categorization task consists in recognizing the ancestors when the network is trained exclusively with their descendents. Explicit results for the dependence of the equilibrium properties of a Q=3-state model and a Q=infinity-state model are obtained in the form of phase diagrams and categorization curves. A strong improvement of the categorization ability is found when the network is trained with examples of low activity. The categorization ability is found to be robust to finite threshold and synaptic noise. The Almeida-Thouless lines that limit the validity of the replica-symmetric results, are also obtained.

Learning↗
PubMed1999

Scaling and percolation in the small-world network model.

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Padé approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model.

Disease Outbreaks↗
PubMed2004

Storage capacity diverges with synaptic efficiency in an associative memory model with synaptic delay and pruning.

It is known that storage capacity per synapse increases by synaptic pruning in the case of a correlation-type associative memory model. However, the storage capacity of the entire network then decreases. To overcome this difficulty, we propose decreasing the connectivity while keeping the total number of synapses constant by introducing delayed synapses. In this paper, a discrete synchronous-type model with both delayed synapses and their prunings is discussed as a concrete example of the proposal. First, we explain the Yanai-Kim theory by employing statistical neurodynamics. This theory involves macrodynamical equations for the dynamics of a network with serial delay elements. Next, considering the translational symmetry of the explained equations, we rederive macroscopic steady-state equations of the model by using the discrete Fourier transformation. The storage capacities are analyzed quantitatively. Furthermore, two types of synaptic prunings are treated analytically: random pruning and systematic pruning. As a result, it becomes clear that in both prunings, the storage capacity increases as the length of delay increases and the connectivity of the synapses decreases when the total number of synapses is constant. Moreover, an interesting fact becomes clear: the storage capacity asymptotically approaches 2/pi due to random pruning. In contrast, the storage capacity diverges in proportion to the logarithm of the length of delay by systematic pruning and the proportion constant is 4/pi. These results theoretically support the significance of pruning following an overgrowth of synapses in the brain and may suggest that the brain prefers to store dynamic attractors such as sequences and limit cycles rather than equilibrium states.

Action Potentials↗
PubMed2004

Dynamic proximity of spatio-temporal sequences.

Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be defined through comparison of the synaptic weight matrices that generate them. Following the dynamic neural filter (DNF) formalism we demonstrate this concept by comparing teacher and student recurrent networks of binary neurons. We show that large sequences, providing a training set well exceeding the Cover limit, allow for good determination of the synaptic matrices. Alternatively, assuming the matrices to be known, very fast determination of the biases can be achieved. Thus, a spatio-temporal sequence may be regarded as spatio-temporal encoding of the bias vector. We introduce a linear support vector machine (SVM) variant of the DNF in order to specify an optimal weight matrix. This approach allows us to deal with noise. Spatio-temporal sequences generated by different DNFs with the same number of neurons may be compared by calculating correlations of the synaptic matrices of the reconstructed DNFs. Other types of spatio-temporal sequences need the introduction of hidden neurons, and/or the use of a kernel variant of the SVM approach. The latter is being defined as a recurrent support vector network (RSVN).

Action Potentials↗
PubMed2004

Synchrony detection and amplification by silicon neurons with STDP synapses.

Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

Action Potentials↗
PubMed2001

Generative models, brain function and neuroimaging.

The representational capacity and inherent function of any neuron, neuronal population or cortical area in the brain is dynamic and context-sensitive. Functional integration, or interactions among brain systems, that employ driving (bottom up) and backward (top-down) connections, mediate this adaptive and contextual specialisation. A critical consequence is that neuronal responses, in any given cortical area, can represent different things at different times. This can have fundamental implications for the design of brain imaging experiments and the interpretation of their results. Our arguments are developed under generative models of brain function, where higher-level systems provide a prediction of the inputs to lower-level regions. Conflict between the two is resolved by changes in the higher-level representations, which are driven by the ensuing error in lower regions, until the mismatch is "cancelled". From this perspective the specialisation of any region is determined both by bottom-up driving inputs and by top-down predictions. Specialisation is therefore not an intrinsic property of any region but depends on both forward and backward connections with other areas. Because the latter have access to the context in which the inputs are generated they are in a position to modulate the selectivity or specialisation of lower areas. The implications for classical models (e.g., classical receptive fields in electrophysiology, classical specialisation in neuroimaging and connectionism in cognitive models) are severe and suggest these models may provide incomplete accounts of real brain architectures. Here we focus on the implications for cognitive neuroscience in the context of neuroimaging.

Brain↗
PubMed2004

Cortico-basal ganglia pathways are essential for the recall of well-established visuomotor associations.

Recent human neuroimaging studies, supported by lesion studies with nonhuman primates, have suggested that learning arbitrary associations between sensory cues and behavioural responses requires interactions between the infero-temporal, prefrontal and premotor cortices. We directly tested the hypothesis suggested from our neuroimaging experiments that functional links between the basal ganglia and premotor cortex are involved in the process via which task performance becomes automatic. We made unilateral excitotoxic lesions, centred on the internal pallidum, in four macaques previously given extensive experience on the associations between nonspatial visual cues and movements of a joystick. The basal ganglia lesion was later combined with a premotor cortical lesion in the opposite hemisphere so as to interrupt the connections between them. Three of the animals were subsequently found to be impaired in relearning pre-operatively acquired associations; they eventually succeeded but made three-times as many errors. A fourth animal was unimpaired but its premotor cortex lesion was later found to be incomplete. Response times were only marginally increased and the learning of novel associations appeared relatively unaffected by these lesions. As a control, the effects of a unilateral premotor cortex lesion were assessed with two additional animals but this lesion did not result in a relearning impairment. We therefore suggest that when visuomotor associations have become well established through over-training, performance depends on connections between the basal ganglia and premotor cortex.

Animals↗
PubMed2005

Cortical origin of functional recovery in the somatosensory cortex of the adult mouse after thalamic lesion.

To study the degree and time course of the functional recovery in the somatosensory cortex (SI) after an excitotoxic lesion in the adult mouse thalamus, metabolic activity was determined in SI at various times points post-lesion. Immediately after the lesion, metabolic activity in the thalamically deafferented part of SI was at its lowest value but increased progressively at subsequent time points. This was seen in all cortical layers; however, layers I and Vb recovered more rapidly than layers II, III, IV, Va and VI. Removal of the mystacial whiskers corresponding to the deafferented area, 5 weeks after cortical recovery, produced a subsequent 32% drop in metabolic activity, demonstrating peripheral sensory activation of this part of the cortex. Tracing experiments revealed that the deafferented cortex did not receive a novel thalamic input but that cortico-cortical and contralateral barrel cortex projections to this area were reinforced. We conclude that the cortical functional recovery after a thalamic lesion is, at least partially, due to modified cortico-cortical and callosal projections to the deafferented cortical area.

Animals↗
PubMed2005

Learning in realistic networks of spiking neurons and spike-driven plastic synapses.

We have used simulations to study the learning dynamics of an autonomous, biologically realistic recurrent network of spiking neurons connected via plastic synapses, subjected to a stream of stimulus-delay trials, in which one of a set of stimuli is presented followed by a delay. Long-term plasticity, produced by the neural activity experienced during training, structures the network and endows it with active (working) memory, i.e. enhanced, selective delay activity for every stimulus in the training set. Short-term plasticity produces transient synaptic depression. Each stimulus used in training excites a selective subset of neurons in the network, and stimuli can share neurons (overlapping stimuli). Long-term plasticity dynamics are driven by presynaptic spikes and coincident postsynaptic depolarization; stability is ensured by a refresh mechanism. In the absence of stimulation, the acquired synaptic structure persists for a very long time. The dependence of long-term plasticity dynamics on the characteristics of the stimulus response (average emission rates, time course and synchronization), and on the single-cell emission statistics (coefficient of variation) is studied. The study clarifies the specific roles of short-term synaptic depression, NMDA receptors, stimulus representation overlaps, selective stimulation of inhibition, and spike asynchrony during stimulation. Patterns of network spiking activity before, during and after training reproduce most of the in vivo physiological observations in the literature.

Action Potentials↗
PubMed2005

A population-based model of the nonlinear dynamics of the thalamocortical feedback network displays intrinsic oscillations in the spindling (7-14 Hz) range.

The thalamocortical network is modelled using the Wilson-Cowan equations for neuronal population activity. We show that this population model with biologically derived parameters possesses intrinsic nonlinear oscillatory dynamics, and that the frequency of oscillation lies within the spindle range. Spindle oscillations are an early sleep oscillation characterized by high-frequency bursts of action potentials followed by a period of quiescence, at a frequency of 7-14 Hz. Spindles are generally regarded as being generated by intrathalamic circuitry, as decorticated thalamic slices and the isolated thalamic reticular nucleus exhibit spindles. However, the role of cortical feedback has been shown to regulate and synchronize the oscillation. Previous modelling studies have mainly used conductance-based models and hence the mechanism relied upon the inclusion of ionic currents, particularly the T-type calcium current. Here we demonstrate that spindle-frequency oscillatory activity can also arise from the nonlinear dynamics of the thalamocortical circuit, and we use bifurcation analysis to examine the robustness of this oscillation in terms of the functional range of the parameters used in the model. The results suggest that the thalamocortical circuit has intrinsic nonlinear population dynamics which are capable of providing robust support for oscillatory activity within the frequency range of spindle oscillations.

Animals↗
PubMed2006

An identified central pattern-generating neuron co-ordinates sensory-motor components of respiratory behavior in Lymnaea.

Defining the attributes of individual central pattern-generating (CPG) neurons underlying various rhythmic behaviors are fundamental to our understanding of how the brain controls motor programs, such as respiration and locomotion. To this end, we have explored a simple invertebrate preparation in which the neuronal basis of respiratory rhythmogenesis can be investigated from the whole animal to a single cell level. An identified dopaminergic neuron, termed right pedal dorsal 1 (RPeD1), is a component of the CPG network which controls hypoxia-driven, aerial respiration in the fresh water snail Lymnaea stagnalis. Using intact, semi-intact and isolated brain preparations, we have discovered that in addition to its role as a respiratory CPG neuron, RPeD1 co-ordinates sensory-motor input from the pneumostome (the respiratory orifice) at the water/air interface to initiate respiratory rhythm generation. An additional, novel role of RPeD1 was also found. Specifically, direct intracellular stimulation of RPeD1 induced pneumostome openings, in the absence of motor neuronal activity. To determine further the role of RPeD1 in the respiratory behavior of intact animals, either its axon was severed or the soma selectively killed. Many components of the respiratory behavior in the intact animals were found to be perturbed following RPeD1 axotomy or 'somatomy' (soma removed). Taken together, the data presented provide a direct demonstration that RPeD1 is a multifunctional CPG neuron, which also serves many additional roles in the control of breathing behavior, ranging from co-ordination of mechanosensory input to the motor control of the respiratory orifice.

Action Potentials↗