Timothée Masquelier

This paper introduces a deep recurrent neural network to model auditory neural responses, showing it outperforms previous methods and reveals longer memory in auditory neurons.

The paper introduces a model called AdapTrans that unifies ON and OFF auditory neural responses and improves prediction accuracy in rat and ferret auditory brain models.

The paper explains sub-optimality in early visual cortex receptive fields through biologically plausible plasticity mechanisms, showing better alignment with biological data than global optimisation models.

This paper shows how Hebbian learning can generate binocular disparity-selective receptive fields in a model of early visual cortex.

Convis is a Python toolbox for simulating filter-based models of early visual processing, offering flexibility and gradient-based optimization over existing methods.