The Experts below are selected from a list of 77526 Experts worldwide ranked by ideXlab platform
Patrick D. Roberts - One of the best experts on this subject based on the ideXlab platform.
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Dynamic regulation of spike-timing dependent Plasticity in electrosensory processing
Neurocomputing, 2006Co-Authors: Patrick D. Roberts, Nathaniel B. Sawtell, Gerardo Lafferriere, Alan Williams, Curtis C BellAbstract:This study investigates the control of spike-timing dependent Plasticity (STDP) by regulation of the dendritic spike threshold of the postsynaptic neuron. The control of synaptic Plasticity may be implemented in the electrosensory system of mormyrid electric fish by feedback control. Dendritic spikes constitute the timing signal of the STDP learning rule that regulates the output of this initial electrosensory processing structure, and the threshold of these spikes appears to be regulated by recurrent inputs from an external nucleus. However, the control dynamics must be shown to be stable, and the conditions for stability would constrain potential models of synaptic regulation. The global stability conditions for the control of STDP are derived using nonlinear control dynamical theory.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity. The neural architecture of the model is based on the electrosensory lateral line lobe of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish's own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical Review E, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by spike-timing dependent Plasticity (STDP). The neural architecture of the model is based on the electrosensory lateral line lobe (ELL) of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish’s own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Stability of negative-image equilibria in Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2003Co-Authors: Alan Williams, Patrick D. Roberts, Todd K LeenAbstract:We investigate the stability of negative image equilibria in mean synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity (STDP). The model architecture closely follows the anatomy and physiology of the electrosensory lateral line lobe (ELL) of mormyrid electric fish. The ELL uses a Spike-Timing-Dependent learning rule to form a negative image of the reafferent signal from the fish's own electric discharge, thus improving detectability of external electric fields. We derive sufficient conditions for existence of the negative image and necessary and sufficient conditions for stability, for arbitrary postsynaptic potential functions and arbitrary learning rules. This significantly generalizes earlier investigations. We then apply the general result to several examples of biological interest, including a class of learning rules consistent with the rule observed experimentally in the mormyrid ELL.
Timothée Masquelier - One of the best experts on this subject based on the ideXlab platform.
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Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: Amirhossein Tavanaei, Timothée Masquelier, Anthony S. MaidaAbstract:The final version of this paper has been published in IEEEXplore available at this http URL. Please cite this paper as: Amirhossein Tavanaei, Timothee Masquelier, and Anthony Maida, Acquisition of visual features through probabilistic Spike-Timing-Dependent Plasticity. IEEE International Joint Conference on Neural Networks. pp. 307-314, IJCNN 2016. This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent Plasticity. PLoS Computational Biology, 3(2), 247-257]. The original model showed that a Spike-Timing-Dependent Plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery. The discovered features where interpretable and could effectively be used to perform rapid binary decisions in a classifier. In order to study the robustness of the previous results, the present research examines the effects of modifying some of the components of the original model. For improved biological realism, we replace the original non-leaky integrate-and-fire neurons with Izhikevich-like neurons. We also replace the original STDP rule with a novel rule that has a probabilistic interpretation. The probabilistic STDP slightly but significantly improves the performance for both types of model neurons. Use of the Izhikevich-like neuron was not found to improve performance although performance was still comparable to the IF neuron. This shows that the model is robust enough to handle more biologically realistic neurons. We also conclude that the underlying reasons for stable performance in the model are preserved despite the overt changes to the explicit components of the model.
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IJCNN - Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Timothée Masquelier, Simon J. ThorpeAbstract:This paper focuses on feedforward spiking neuron models of the visual cortex. Essentially, we show that a combination of a temporal coding scheme where the most strongly activated neurons fire first with Spike Timing-Dependent Plasticity leads to a situation where neurons will gradually become selective to visual patterns that are both salient, and consistently present in the inputs. At the same time, their responses become more and more rapid. These responses can then be used very effectively to perform object recognition in natural images.
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Pattern learning using Spike-Timing-Dependent Plasticity: a theoretical approach
BMC Neuroscience, 2009Co-Authors: Matthieu Gilson, Timothée Masquelier, Etienne Hugues, Anthony N BurkitttAbstract:Recognition tasks performed by humans and animalsrequire the learning and storage of representations byneuronal networks of external sensory stimuli. Recentstudies have established the importance of timing withinspike trains for synaptic Plasticity, which is hypothesisedto lead to learning at the behavioral level [1,2]. This Spike-Timing-Dependent Plasticity (STDP) was shown to bothstabilize the weights of the synapses on a neuron andinduce competition between them in order to generatenetwork structure [3]. For example, STDP can capturetemporal correlation or interaural time differences at thescale of milliseconds within spike trains [1,3].
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Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity.
PLoS Computational Biology, 2007Co-Authors: Timothée Masquelier, Simon J. ThorpeAbstract:Spike timing dependent Plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.
Alan Williams - One of the best experts on this subject based on the ideXlab platform.
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Dynamic regulation of spike-timing dependent Plasticity in electrosensory processing
Neurocomputing, 2006Co-Authors: Patrick D. Roberts, Nathaniel B. Sawtell, Gerardo Lafferriere, Alan Williams, Curtis C BellAbstract:This study investigates the control of spike-timing dependent Plasticity (STDP) by regulation of the dendritic spike threshold of the postsynaptic neuron. The control of synaptic Plasticity may be implemented in the electrosensory system of mormyrid electric fish by feedback control. Dendritic spikes constitute the timing signal of the STDP learning rule that regulates the output of this initial electrosensory processing structure, and the threshold of these spikes appears to be regulated by recurrent inputs from an external nucleus. However, the control dynamics must be shown to be stable, and the conditions for stability would constrain potential models of synaptic regulation. The global stability conditions for the control of STDP are derived using nonlinear control dynamical theory.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity. The neural architecture of the model is based on the electrosensory lateral line lobe of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish's own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical Review E, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by spike-timing dependent Plasticity (STDP). The neural architecture of the model is based on the electrosensory lateral line lobe (ELL) of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish’s own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Stability of negative-image equilibria in Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2003Co-Authors: Alan Williams, Patrick D. Roberts, Todd K LeenAbstract:We investigate the stability of negative image equilibria in mean synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity (STDP). The model architecture closely follows the anatomy and physiology of the electrosensory lateral line lobe (ELL) of mormyrid electric fish. The ELL uses a Spike-Timing-Dependent learning rule to form a negative image of the reafferent signal from the fish's own electric discharge, thus improving detectability of external electric fields. We derive sufficient conditions for existence of the negative image and necessary and sufficient conditions for stability, for arbitrary postsynaptic potential functions and arbitrary learning rules. This significantly generalizes earlier investigations. We then apply the general result to several examples of biological interest, including a class of learning rules consistent with the rule observed experimentally in the mormyrid ELL.
Matthieu Gilson - One of the best experts on this subject based on the ideXlab platform.
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Representation of input structure in synaptic weights by Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2010Co-Authors: Matthieu Gilson, David B. Grayden, Doreen A. Thomas, Anthony N. Burkitt, J. Leo Van HemmenAbstract:Spike-Timing-Dependent Plasticity (STDP) has been shown to generate a synaptic weight structure that is determined by the timing of the pre- and postsynaptic spikes at the synapse. In this paper it is shown under what conditions a neuron stimulated by several pools of delta-correlated inputs encodes this input structure in its resulting weight structure. The analysis is carried out using Poisson neurons with weight-dependent STDP. The learning dynamics induced by STDP leads to both stabilization of the input weights and competition between the weights for a broad range of learning parameters. The results demonstrate how weight-dependent STDP can generate multimodal stable asymptotic distributions of the synaptic weights.
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Pattern learning using Spike-Timing-Dependent Plasticity: a theoretical approach
BMC Neuroscience, 2009Co-Authors: Matthieu Gilson, Timothée Masquelier, Etienne Hugues, Anthony N BurkitttAbstract:Recognition tasks performed by humans and animalsrequire the learning and storage of representations byneuronal networks of external sensory stimuli. Recentstudies have established the importance of timing withinspike trains for synaptic Plasticity, which is hypothesisedto lead to learning at the behavioral level [1,2]. This Spike-Timing-Dependent Plasticity (STDP) was shown to bothstabilize the weights of the synapses on a neuron andinduce competition between them in order to generatenetwork structure [3]. For example, STDP can capturetemporal correlation or interaural time differences at thescale of milliseconds within spike trains [1,3].
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Specialisation in recurrent neural networks with Spike-Timing-Dependent Plasticity
2008Co-Authors: Matthieu Gilson, David B. Grayden, Doreen A. Thomas, Anthony N. Burkitt, J. Leo Van HemmenAbstract:We examine in this paper how Spike-Timing-Dependent Plasticity (STDP) can implement the strengthening of recurrent excitatory connections in addition to their homeostatic equilibrium in a network of Poisson neurons stimulated by correlated pools of external inputs. This phenomenon is determined by the interplay between STDP, the neuronal mechanisms related to the post-synaptic response and the input correlation structure. Our results can be related to the emergence of functional areas in recurrently connected networks, i.e. self-organising maps.
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The learning dynamics of Spike-Timing-Dependent Plasticity in recurrently connected networks
BMC Neuroscience, 2007Co-Authors: Matthieu Gilson, Anthony N. Burkitt, J. Leo Van HemmenAbstract:Background Functional organization in neural networks is believed to arise from synaptic Plasticity. Spike-Timing-Dependent Plasticity (STDP) is a candidate for such Plasticity which has received considerable experimental support and been the subject of considerable theoretical investigation. Our work extends the framework developed in [1] for analyzing the learning dynamics of STDP in feed-forward network architecture to the case of recurrently connected networks.
Todd K Leen - One of the best experts on this subject based on the ideXlab platform.
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Stochastic perturbation methods for Spike-Timing-Dependent Plasticity
Neural computation, 2012Co-Authors: Todd K Leen, Robert FrielAbstract:Online machine learning rules and many biological Spike-Timing-Dependent Plasticity (STDP) learning rules generate jump process Markov chains for the synaptic weights. We give a perturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), is well justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Monte Carlo simulations. The methods are also applicable to the dynamics of stochastic neural activity. Like previous ensemble analyses of STDP, we focus on equilibrium solutions, although the methods can in principle be applied to transients as well.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity. The neural architecture of the model is based on the electrosensory lateral line lobe of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish's own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Random walks for Spike-Timing-Dependent Plasticity.
Physical Review E, 2004Co-Authors: Alan Williams, Todd K Leen, Patrick D. RobertsAbstract:Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by spike-timing dependent Plasticity (STDP). The neural architecture of the model is based on the electrosensory lateral line lobe (ELL) of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish’s own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.
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Stability of negative-image equilibria in Spike-Timing-Dependent Plasticity.
Physical review. E Statistical nonlinear and soft matter physics, 2003Co-Authors: Alan Williams, Patrick D. Roberts, Todd K LeenAbstract:We investigate the stability of negative image equilibria in mean synaptic weight dynamics governed by Spike-Timing-Dependent Plasticity (STDP). The model architecture closely follows the anatomy and physiology of the electrosensory lateral line lobe (ELL) of mormyrid electric fish. The ELL uses a Spike-Timing-Dependent learning rule to form a negative image of the reafferent signal from the fish's own electric discharge, thus improving detectability of external electric fields. We derive sufficient conditions for existence of the negative image and necessary and sufficient conditions for stability, for arbitrary postsynaptic potential functions and arbitrary learning rules. This significantly generalizes earlier investigations. We then apply the general result to several examples of biological interest, including a class of learning rules consistent with the rule observed experimentally in the mormyrid ELL.