Learning temporal structure of the input with a network of integrate-and-fire neurons

1 code implementation • 21 Dec 2019 • Lyudmila Kushnir, Sophie Denève

The network achieves the efficiency by adjusting its synaptic weights in such a way, that for any neuron in the network, the recurrent input cancels the feedforward for most of the time.

Neurons and Cognition

Enforcing balance allows local supervised learning in spiking recurrent networks

no code implementations • NeurIPS 2015 • Ralph Bourdoukan, Sophie Denève

The fast connections learn to balance excitation and inhibition using a voltage-based plasticity rule.

Spatio-temporal Representations of Uncertainty in Spiking Neural Networks

no code implementations • NeurIPS 2014 • Cristina Savin, Sophie Denève

It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions.

Firing rate predictions in optimal balanced networks

no code implementations • NeurIPS 2013 • David G. Barrett, Sophie Denève, Christian K. Machens

This is an important problem because firing rates are one of the most important measures of network activity, in both the study of neural computation and neural network dynamics.