R
Romain D. Cazé
Researcher at Centre national de la recherche scientifique
Publications - 26
Citations - 424
Romain D. Cazé is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Biological neuron model & Stimulus (physiology). The author has an hindex of 7, co-authored 24 publications receiving 290 citations. Previous affiliations of Romain D. Cazé include University of Paris & Imperial College London.
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Journal ArticleDOI
Contribution of sublinear and supralinear dendritic integration to neuronal computations.
Alexandra Tran-Van-Minh,Romain D. Cazé,Romain D. Cazé,Therese Abrahamsson,Therese Abrahamsson,Laurence Cathala,Boris Gutkin,Boris Gutkin,David A. DiGregorio +8 more
TL;DR: This article reviews the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear, and describes how global and local integration strategies permit the implementation of similar classes of computations.
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Passive dendrites enable single neurons to compute linearly non-separable functions.
TL;DR: In this paper, it was shown that a single non-linear dendritic sub-unit, in addition to the somatic nonlinearity, is sufficient to compute linearly non-separable functions.
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Adaptive properties of differential learning rates for positive and negative outcomes
TL;DR: It is shown analytically how the optimal learning rate asymmetry depends on the reward distribution and how a biologically plausible algorithm that adapts the balance of positive and negative learning rates from experience is implemented.
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Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour.
TL;DR: The results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.
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Hippocampal replays under the scrutiny of reinforcement learning models.
TL;DR: This work reviews the main findings concerning the different hippocampal replay types and the possible associated RL models (either model-based, model-free, or hybrid model types) and illustrates the link between data and RL through a series of model simulations.