Guillaume Hennequin

Dynamic consensus-building between neocortical areas via long-range connections

This study shows that reciprocal excitatory connections between cortical areas create line attractor dynamics that promote consensus on stimulus features across areas.

When and why does motor preparation arise in recurrent neural network models of motor control?

This study investigates why motor preparation occurs in RNN models of motor control and finds that optimal input-driven control leads to multiple phases of preparation during reach sequences.

A recurrent network model of planning explains hippocampal replay and human behavior

The paper develops a neural network model of planning that explains human behavior and hippocampal replay patterns in rodents.

Beyond the Euclidean brain: inferring non-Euclidean latent trajectories from spike trains

The paper extends the manifold Gaussian process latent variable model to handle non-Euclidean neural data with temporal continuity and count-based noise, improving performance on synthetic and real neural recordings.

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data

The paper introduces iLQR-VAE, a control-based variational inference method for learning dynamics and inputs from neural data, showing effectiveness on synthetic and primate reaching task data.

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models

This paper introduces a scalable Bayesian Gaussian process factor analysis (bGPFA) for neural data, showing it captures preparatory dynamics and long-timescale processes in primate motor cortex.

Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model

The paper proposes a thalamo-cortical circuit model for optimal anticipatory control of motor preparation, showing feedback control enables fast movement preparation and orthogonal activity.

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

This study develops a recurrent neural network model of a visual cortical hypercolumn optimized for sampling-based probabilistic inference, revealing how cortical dynamics arise from efficient computational functions.

Motor primitives in space and time via targeted gain modulation in cortical networks

The paper shows that modulating neuronal input-output gains in cortical networks can generate diverse movements and control their speed and shape.

Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex

The paper shows that a stochastic Stabilized Supralinear Network model explains stimulus-induced changes in cortical variability and correlations.