Paul Masset

Perception and neural representation of intermittent odor stimuli in mice

This study investigates how mice perceive intermittent odor stimuli by analyzing behavioral responses and neural activity in the anterior piriform cortex, finding that respiratory phase modulates perception but limits information acquisition.

Simultaneous detection and estimation in olfactory sensing

This paper introduces a model for olfactory compressed sensing that simultaneously detects odor presence and estimates concentrations, showing it can be implemented in a biologically plausible circuit.

Multi-timescale reinforcement learning in the brain

This paper shows that dopamine neurons in mice use multiple time scales to encode reward prediction errors, suggesting cell-specific temporal discounting and implications for reinforcement learning algorithms.

Interpretable deep learning for deconvolutional analysis of neural signals

This paper introduces an interpretable deep learning method for deconvolving neural signals, demonstrating its application in analyzing brain activity across different regions and recording modalities.

Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems

This paper presents a spiking neural network model that combines sampling-based inference with attractor dynamics to study head-direction systems, making testable predictions about neural activity patterns.

Bounded optimality of time investments in rats, mice, and humans

This study shows that humans, rats, and mice invest time based on statistical confidence, approaching optimal time allocation strategies under uncertainty.

Neural circuits for fast Poisson compressed sensing in the olfactory bulb

This paper proposes a rate-based Poisson compressed sensing model for the olfactory bulb that accurately detects odors within a single sniff.

Natural gradient enables fast sampling in spiking neural networks

This paper shows how natural gradient methods can enable fast sampling in spiking neural networks by leveraging population geometry for probabilistic inference.