Publications by Guillaume Hennequin

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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 ...

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 ...

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 ...

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 ...

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 ...

Guillaume Hennequin

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Code-specific policy gradient rules for spiking neurons

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

STDP in adaptive neurons gives close-to-optimal information transmission

Non-normal amplification in random balanced neuronal networks

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

Stability and amplification in plastic cortical circuits