Sophie Denève

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

The paper develops a predictive coding framework for recurrent spiking networks, showing that heterogeneous networks are more efficient and robust than homogeneous ones.

This study investigates how predictive learning in neural networks generates low-dimensional latent representations of sensory inputs by analyzing network dynamics and intrinsic dimensionality.

The paper investigates bistable perception using a Bayesian framework and finds that a circular inference model better explains the data than optimal Bayesian integration.

The paper shows that population-level optimization of efficiency maintains stable neural representations through spike-frequency adaptation and balanced connectivity, reconciling neural and behavioral findings in orientation coding.

The paper proposes a framework for neural circuits to learn dynamic tasks using local synaptic plasticity rules and two key mechanisms: top-down feedback and balanced excitation-inhibition.