Publications de Wulfram Gerstner

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.

A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in ...

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Memory consolidation refers to a process of engram reorganization and stabilization that is thought to occur primarily during sleep through a combination of neural replay, homeostatic plasticity, synaptic maturation, and pruning. From a computational perspective, however, this process remains puzzling, as it is unclear how to incorporate the underlying mechanisms into a common mathematical model of learning and memory. Here, we propose a solution by deriving a self-supervised consolidation model that uses r

A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlappin

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 ...

A neuron that is stimulated repeatedly by the same time-dependent stimulus exhibits slightly different spike timing at each trial. We compared the exact solution of the time-dependent firing rate for a stochastically spiking neuron model with refractorines ...

A correlation-based (

Hebbian'') learning rule at the spike level is formulated, mathematically analyzed, and compared with learning in a firing-rate description. As for spike coding, we take advantage of a

learning window'' that describes the effect of timing of pre- and postsynaptic spikes on synaptic weights. A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and spiking dynamics can be separated. Formation of structured synapses is analy

The structure of cross-correlations between subthreshold potentials of neocortical neurons was recently examined. Characteristic features included broad widths and significant peak advances. It was suggested that dynamic synapses shape these cross-correlat ...

An integral equation describing the time evolution of the population activity in a homogeneous pool of spiking neurons of the integrate-and-fire type is discussed. It is analytically shown that transients from a state of incoherent firing can be immediate. The stability of incoherent firing is analyzed in terms of the noise level and transmission delay, and a bifurcation diagram is derived. The response of a population of noisy integrate-and-fire neurons to an input current of small amplitude is calculated

Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from ...

The neuronal mechanisms generating a delayed motor response initiated by a sensory cue remain elusive. Here, we tracked the precise sequence of cortical activity in mice transforming a brief whisker stimulus into delayed licking using wide-field calcium im ...

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 ...

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing att ...

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 (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the volt

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. An

Over the last years evidence has accumulated that shows the possibility to analyze human brain activity on-line and translate brain states into actions such as selecting a letter from a virtual keyboard or moving a robotics device. These initial results have been obtained with either invasive approaches (requiring surgical implantation of electrodes) or synchronous protocols (where brain signals are time-locked to external cues). In this paper we describe a portable noninvasive brain-computer interface that

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic p ...

What is the meaning' of a single spike? Spike-triggered averaging (reverse correlations') yields the typical input just before a spike. Similarly, cross-correlations describe the probability of firing an output spike given (one additional) presynaptic input spike. In this paper we analytically calculate reverse and cross-correlations for a spiking neuron model with escape noise. The influence of neuronal parameters (such as the membrane time constant, the noise level, and the mean firing rate) on the form

Wulfram Gerstner

Chattez avec Graph Search

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity

When shared concept cells support associations: Theory of overlapping memory engrams

Perceptual learning, roving and the unsupervised bias

Two-factor synaptic consolidation reconciles robustness with pruning and homeostatic scaling

Spatial Representation and Navigation in a Bio-inspired Robot

Complexity and performance in simple neuron models

From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness

Hebbian learning and spiking neurons

Subthreshold cross-correlations between cortical neurons: A reference model with static synapses

Population dynamics of spiking neurons: fast transients, asynchronous states and locking

Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models

Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response

The quantitative single-neuron modeling competition

Limits to high-speed simulations of spiking neural networks using general-purpose computers

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

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation

Brain-Actuated Interaction

Hebbian plasticity requires compensatory processes on multiple timescales

Coding properties of spiking neurons: reverse- and cross-correlations