Guillaume Hennequin

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Related publications (6)

Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements

Wulfram Gerstner, Guillaume Hennequin, Tim Vogels

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a clas ...

Cell Press2014

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Wulfram Gerstner, Guillaume Hennequin, Friedemann Zenke

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep acti ...

Public Library of Science2013

Stability and amplification in plastic cortical circuits

Guillaume Hennequin

EPFL2013

Non-normal amplification in random balanced neuronal networks

Wulfram Gerstner, Guillaume Hennequin, Tim Vogels

In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to the network in two distinct ways. One is induced by the presence of near-critical eigenvalues in the connectivity matrix W, producing large but slow activit ...

2012

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

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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