Publication

Prospective Coding by Spiking Neurons

Publications associées (148)

The Dynamics of Adapting Neurons

How do neurons dynamically encode and treat information? Each neuron communicates with its distinctive language made of long silences intermitted by occasional spikes. The spikes are prompted by the pooled effect of a population of pre-synaptic neurons. To ...

EPFL2011

A synaptic organizing principle for cortical neuronal groups

Henry Markram, Rodrigo de Campos Perin, Thomas Berger

Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and syna ...

2011

Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties

Henry Markram, Felix Schürmann, Sean Lewis Hill, Idan Segev

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling ...

2011

Adaptation reduces variability of the neuronal population code

Eilif Benjamin Muller

Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics ...

2011

Improved Similarity Measures for Small Sets of Spike Trains

Wulfram Gerstner, Richard Naud, Joao Emanuel Felipe Gerhard, Skander Mensi

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in ...

Massachusetts Institute of Technology Press2011

Automated Quantification of Morphodynamics for High-Throughput Live Cell Imaging Datasets

Kevin Smith, Germán González Serrano

We present a fully automatic method to track and quantify the morphodynamics of differentiating neurons in fluorescence time-lapse microscopy datasets. While previous efforts have successfully quantified the dynamics of organelles such as the cell body, nu ...

2011

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Wulfram Gerstner, Eleni Vasilaki, Claudia Clopath

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (S ...

Nature Publishing Group2010

Spike-timing dependent plasticity

Wulfram Gerstner

Spike Timing Dependent Plasticity (STDP) is a temporally asymmetric form of Hebbian learning induced by tight temporal correlations between the spikes of pre- and postsynaptic neurons. As with other forms of synaptic plasticity, it is widely believed that ...

2010

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Wulfram Gerstner, Nicolas Frémaux

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity ...

Society for Neuroscience2010

STDP in adaptive neurons gives close-to-optimal information transmission

Wulfram Gerstner, Jean-Pascal Théodor Pfister, Guillaume Hennequin

Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information tra ...

Frontiers Research Foundation2010

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.

Connectez-vous pour utiliser Chat avec Graph Search