669 research outputs found
Hebbian and non-Hebbian plasticity orchestrated to form and retrieve memories in spiking networks
No full textLC
Synaptic efficacy tunes speed of activity propagation through chains of bistable neural assemblies
No full textLC
Functional shape of the spike-triggered adaptation
No full textIn computational neuroscience, it is of crucial importance to dispose of a model that is able to accurately describe the single-neuron activity. This model should be at the same time biologically relevant and computationally fast. Many different phenomenological models have been proposed. In particular, the adaptive exponential integrate-and-fire model (AdEX) introduced by R. Brette and W. Gerstner accounts for adaptation via spike-triggered currents and the dynamical threshold introduced by Badel et al. Includes refractoriness via a dynamical threshold. In real neurons, adaptation occurs on multiple timescales. Furthermore, it has also been shown that the dynamics of the adaptation depends on the timescale on which the input statistics varies. Here, a new model is proposed that combines both adaptation and refractoriness. In practice, a slightly modified version of the AdEX model was extended using different dynamics of the voltage threshold. To account for multiple-timescale adaptation, power law dynamics was considered. It was also investigated whether the ability to predict spike timing could be improved by driving the modified AdEX model with the fractional derivative of the injected current. All the proposed models were fitted on experimental data from rat cortical pyramidal neurons. The models proposed here can reproduce the activity of the real neuron with high accuracy and about 60% of the observed spikes were correctly predicted with a precision of ±3ms. The introduction of a moving threshold did not not improve in a drastic way the ability to predict spikes, but in the case of cumulative power law dynamics the model was able to capture scale-invariant adaptation. It turns out that the fractional derivative of the injected current can partially account for adaptation. However, the best model takes as input signal the injected current and has both cumulative power law threshold and spike-triggered currentSSVLaboratory of Computational Neuroscience (LCN), EPF
Escape rate models for noisy integrate-and-free neurons
No full textThe noisy integrate-and-fire neuron is difficult to treat analytically. In particular, interspike-interval densities have to be computed numerically. We compare here the noisy integrate-and-fire neuron with three escape noise models for neuronal spiking which can be solved analytically. We show that an escape model with an instantaneous rate depending on the momentary membrane potential and its derivative provides an excellent approximation to the dynamics of the noisy integrate-and-fire model. We also demonstrate that the method of images does not yield reliable interspike-interval densities. (C) 2000 Elsevier Science B.V. All rights reserved
Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis
No full textElectrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas
Network dynamics of spiking neurons with adaptation
No full textHow to describe circuit level cortical dynamics?LC
Spatial learning and navigation in the rat : a biomimetic model
No full textAnimals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed with a representation of space. Such a representation (i.e. cognitive map) allows the animal to locate itself within a known environment and perform complex target-directed behaviour. In order to efficiently perform, the animal not only should be able to exhibit these types of behaviour, but it should be able to select which behaviour is the most appropriate at any given task conditions. Neurophysiological and behavioural experiments provide important information on how such processes may take place in the rodent's brain. Specifically, place- and orientation sensitive cells in the rat Hippocampus have been interpreted as a neural substrate for spatial abilities related to the theory of the cognitive map proposed in the late 1940s by Tolman. Moreover, recent dissociation experiments using selectively located lesions, as well as pharmacological studies have shown that different brain regions may be involved in different types of behaviour. Accordingly, one memory system involving the hippocampus and the ventral striatum would be responsible for cognitive navigation, while navigation based on stimulus-response associations would be mediated by the dorsolateral striatum. Based on these studies, the aim of this work is to develop a neural network model of the spatial abilities of the rat. The model, based on functional properties and anatomical inter-connections of the brain areas involved in spatial learning should be able to establish a distributed representation of space composed of place-sensitive units. Such a representation takes into account both internal and external sensory information, and the model reproduces physiological properties of place cells such as changes in their directional dependence. Moreover, the spatial representation may be used to perform cognitive navigation. Modelled place cells drive an extra-hippocampal population of action-coding cells, allowing the establishment of place-response associations. These associations encoded in synaptic connections between place- and action-cells are modified by means of reinforcement learning. In a similar way, simple sensory input can be used to establish stimulus-response associations. These associations are encoded in a different set of action cells which corresponds to a different neural substrate encoding for non-cognitive navigation strategies (i.e. taxon or praxic). Both cognitive and non-cognitive navigation strategies compete for action control to determine the actual behaviour of the agent. Tests of the performance of the model show that it is able to establish a representation of space, and modelled place cells reproduce some physiological properties of their biological counterparts. Furthermore, the model reproduces goal-based behaviour based on both cognitive and non-cognitive strategies as well as behaviour in conflicting situations reported in experimental studies in animals
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