Concept

Hebbian theory

Related publications (104)

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer netw ...

Springer Verlag2009

Networks of Mixed Canonic-Dissipative Systems and Dynamic Hebbian Learning

Max-Olivier Hongler, Julio Rodriguez

We consider a collection {O_k}_{k=1}^N of interacting parametric mixed canonical-dissipative systems, (MCD). Each individual Ok, exhibits, in absence of interaction, a limit cycle L_k on which the orbit circulation is parameterized by w_k(t). The underlyin ...

Shaker Verlag2008

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Wulfram Gerstner, Eleni Vasilaki, Claudia Clopath, Lorric Ziegler

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of syna ...

Public Library of Science2008

Phenomenological models of synaptic plasticity based on spike timing

Wulfram Gerstner

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of ...

Springer Verlag2008

Slow oscillations in neural networks with facilitating synapses

Henry Markram, Gilad Silberberg

The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillatio ...

Springer US2008

Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution

Wulfram Gerstner, Jean-Pascal Théodor Pfister, Taro Toyoizumi

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic ne ...

Massachusetts Institute of Technology Press2007

Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...

IEEE Press2007

Slowness: An Objective for Spike-Timing-Dependent Plasticity?

Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, ...

Public Library of Science2007

A supervised learning approach based on STDP and polychronization in spiking neuron networks

Samy Bengio

We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algo ...

2007

Theory of non-linear spike-time-dependent plasticity

Jean-Pascal Théodor Pfister

A fascinating property of the brain is its ability to continuously evolve and adapt to a constantly changing environment. This ability to change over time, called plasticity, is mainly implemented at the level of the connections between neurons (i.e. the s ...

EPFL2006

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