60 citations · 100 across the 6 of their papers we have counts for
7 papers
A multi-scale information geometry reveals the structure of mutual information in neural populations
Simone Azeglio, Steeve Laquitaine, Ulisse Ferrari +1
Understanding how neural population responses represent sensory information is a central problem in systems neuroscience. One approach is to define a representational geometry on s…
Decomposing stimulus-specific sensory neural information via diffusion models
Steeve Laquitaine, Simone Azeglio, Carlo Paris +2
To understand sensory coding, we must ask not only how much information neurons encode, but also what that information is about. This requires decomposing mutual information into c…
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
Jacob Granley, Tristan Fauvel, Matthew Chalk +1
Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted…
Contextual Bayesian optimization with binary outputs
Tristan Fauvel, Matthew Chalk
Bayesian optimization (BO) is an efficient method to optimize expensive black-box functions. It has been generalized to scenarios where objective function evaluations return stocha…
Efficient Exploration in Binary and Preferential Bayesian Optimization
Tristan Fauvel, Matthew Chalk
Bayesian optimization (BO) is an effective approach to optimize expensive black-box functions, that seeks to trade-off between exploitation (selecting parameters where the maximum…
Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Ulisse Ferrari, Stephane Deny, Matthew Chalk +3
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend stron…