Publication

Firing patterns in the adaptive exponential integrate-and-fire model

Publications associées (32)

Computational principles of single neuron adaptation

Cortical neurons continuously transform sets of incoming spike trains into output spike trains. This input-output transformation is referred to as single-neuron computation and constitutes one of the most fundamental process in the brain. A deep understand ...

EPFL2014

A new Mathematical Framework to Understand Single Neuron Computations

Skander Mensi

An important feature of the nervous system is its ability to adapt to new stimuli. This adaptation allows for optimal encoding of the incoming information by dynamically changing the coding strategy based upon the incoming inputs to the neuron. At the leve ...

EPFL2014

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

Wulfram Gerstner, Carl Petersen, Richard Naud, Skander Mensi, Christian Antonio Pozzorini, Michael Avermann

Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107: 1756-1775, 2012. First published December 7, 2011; do ...

American Physiological Society2012

NineML: the network interchange for neuroscience modeling language

Sean Lewis Hill, Eilif Benjamin Muller, Michael Lee Hines

The growing number of large-scale neuronal network models has created a need for standards and guidelines to ease model sharing and facilitate the replication of results across different simulators. To foster community efforts towards such standards, the I ...

2011

The Dynamics of Adapting Neurons

Richard Naud

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

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

Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation

Michele Giugliano

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically det ...

2011

Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem

Joao Emanuel Felipe Gerhard

Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli ...

2010

Statistical tests for neural population models - The multivariate time rescaling theorem

Joao Emanuel Felipe Gerhard

The coordinated, collective spiking activity of neuronal populations encodes and processes information. One approach towards understanding such population based computation is to fit statistical models to simultaneously recorded spike trains and use these ...

2010

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

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