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Slow fluctuations in recurrent networks of spiking neurons

Publications associées (51)

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Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

Joao Emanuel Felipe Gerhard

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired re ...

Public Library of Science2013

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

Parallel retrieval of correlated patterns: From Hopfield networks to Boltzmann machines

Andrea De Antoni

In this work, we first revise some extensions of the standard Hopfield model in the low storage limit, namely the correlated attractor case and the multitasking case recently introduced by the authors. The former case is based on a modification of the Hebb ...

Elsevier2013

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Joao Emanuel Felipe Gerhard

Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on ne ...

Massachusetts Institute of Technology Press2011

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We consider the problem of neural association, which deals with the retrieval of a previously memorized pattern from its noisy version. The performance of various neural networks developed for this task may be judged in terms of their pattern retrieval cap ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

Mohammad Amin Shokrollahi, Krishna Kumar, Amir Hesam Salavati

We consider the problem of neural association for a network of non-binary neurons. Here, the task is to recall a previously memorized pattern from its noisy version using a network of neurons whose states assume values from a finite number of non-negative ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

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

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

Interferences in the Transformation of Reference Frames during a Posture Imitation Task

Aude Billard, Eric Sauser

We present a biologically-inspired neural model addressing the problem of transformations across frames of reference in a posture imitation task. Our modeling is based on the hypothesis that imitation is mediated by two concurrent transformations selective ...

2007

How the visual brain integrates temporally dispersed information is an open question. Often, it is assumed that the visual system simply sums light over a certain period of time (e.g. Bloch's law). However, in feature fusion, information presented later do ...

Elsevier2007