Jean-Claude Béïque

The paper investigates how 5-HT neurons in the DRN encode and distinguish two excitatory inputs using spike timing and intrinsic cellular mechanisms.

The paper develops a method to analyze brain-wide electrical fluctuations and finds modular organization in the mouse brain that changes with context.

spikeMAP identifies cortical excitatory and inhibitory neurons in high-density multielectrode recordings using spike sorting and experimental validation.

The paper analyzes serotonergic neuron activity during a dynamic Pavlovian task to estimate reward history and its relation to behavior.

This study quantifies how mice navigate an open maze by analyzing their movement and hole-checking behavior, revealing a cognitive map and learning dynamics.

This study uses deep learning to infer synaptic plasticity rules from single-neuron spiketrains, finding that transformers outperform other models but caution against applying simulation-trained classifiers to real data.

This paper proposes a theory that serotonin neurons encode predictive value signals, explaining various serotonergic responses better than existing theories.

This paper investigates the biophysical properties of hilar mossy cells (hMCs) and develops a model to explain their adaptive spike threshold dynamics and role in pattern separation.

The paper investigates synaptic eligibility traces in mice prefrontal cortex neurons, showing how calcium dynamics and molecular mechanisms enable delayed credit assignment for learning.

The paper uses an augmented integrate-and-fire model to show that the dorsal raphe network encodes both input intensity and its temporal derivative, with the derivative dominating during rapid input increases.

The paper identifies two distinct neural codes, burst fraction and event rate, that convey stimulus information and error signals during learning, with burst fraction showing stronger modulation and temporal alignment with behavioral accuracy.

The paper identifies a slow serotonergic recurrent inhibitory network in the dorsal raphe nucleus that generates non-linear dynamics and winner-take-all computations, affecting reward-conditioned behavior.

The paper develops a synthetic likelihood method to estimate silent synapse fractions, showing it reduces bias and improves statistical power compared to existing methods.

The paper develops a computational method to estimate quantal parameters at single synapses using iGluSnFR imaging and statistical algorithms.