Publications by Richard Naud | EPFL Graph Search

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Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons ...

For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show th ...

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 ...

The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. In neurons, stimulated by a time-dependent current or conductance, we want to predict precisely the timing of spikes and the su ...

As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively ac ...

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 ...

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for ...

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we d ...

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 ...

How well are single-cell properties reproduced by the present-day neuronal models? Recently, several labs have approached this question by assessing the quality of the models with respect to spike timing prediction or characteristic features of the voltage ...

Several methods and algorithms have recently been proposed that allow for the systematic evaluation of simple neuron models from intracellular or extracellular recordings. Models built in this way generate good quantitative predictions of the future activi ...

A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrit ...

A complete model for single neurons must reproduce correctly the firing of spikes and bursts. Yet it must remain simple enough to be tractable in large-scale simulations. We present a study of a simplified model of Layer V pyramidal cells of the cortex wit ...

Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the firing threshold and by adaptation currents triggered after each spike. Combini ...

Richard Naud

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Fluctuations and information filtering in coupled populations of spiking neurons with adaptation

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

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

Complexity and performance in simple neuron models

The quantitative single-neuron modeling competition

Improved Similarity Measures for Small Sets of Spike Trains

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

The Dynamics of Adapting Neurons

Quantitative single-neuron modeling: competition 2008

A benchmark test for a quantitative assessment of simple neuron models

Spike-timing prediction in cortical neurons with active dendrites

Spike-timing prediction in a neuron model with active dendrites

Temporal whitening by power-law adaptation in neocortical neurons