Plasticité fonction du temps d'occurrence des impulsions
Publications associées (256)
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
The neural cell adhesion molecule (NCAM) plays a key role in synaptic plasticity and memory formation. We have recently developed a synthetic peptide, termed C3d, which, through the binding to the first, N-terminal immunoglobulin-like (Ig) module in the ex ...
We have previously shown that labelling intensities for synaptic proteins vary strongly among synaptic boutons. Here we addressed the questions as to whether there are heterogeneous levels of integral membrane synaptic vesicle proteins at distinct active r ...
The impact was examined of exposing rats to two life experiences of a very different nature (stress and learning) on synaptic structures in hippocampal area CA3. Rats were subjected to either (i) chronic restraint stress for 21 days, and/or (ii) spatial tr ...
Several formulations of correlation-based Hebbian learning are reviewed. On the presynaptic side, activity is described either by a firing rate or by presynaptic spike arrival. The state of the postsynaptic neuron can be described by its membrane potential ...
We investigate the propagation of pulses of spike activity in a neuronal network with feed-forward couplings. The neurons are of the spike-response type with a firing probability that depends linearly on the membrane potential. AFter firing neurons enter a ...
Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative res ...
The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is ...
The short-term plasticity of synaptic transmission between excitatory neurons within a barrel of layer 4 rat somatosensory neocortex was investigated. Action potentials in presynaptic neurons at frequencies ranging from 1 to 100 Hz evoked depressing postsy ...
This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double T-Maze navigation tasks, where the robot has to locate and "remember'' the position of ...
Synaptic transmission in the neocortex is dynamic, such that the magnitude of the postsynaptic response changes with the history of the presynaptic activity. Therefore each response carries information about the temporal structure of the preceding presynap ...