Matthew Chalk

This paper introduces a multi-scale representational geometry framework to analyze how neural populations encode stimulus differences across scales, showing how neurons combine to encode information and how different brain areas differ.

The paper shows that specific ganglion cells respond selectively to contrast changes near moving object edges, suggesting they could serve as primitives for motion segmentation.

The paper introduces two information-theoretic measures, ISSI and Iloc, to quantify neuronal sensitivity in neural systems, showing they outperform Fisher information in realistic conditions.

The paper shows that direction-selective retinal ganglion cells encode motion direction uniformly across all directions, despite being tuned to cardinal axes.

This study shows that neural networks optimized for efficient coding exhibit criticality signatures near optimal noise levels, linking efficient coding and criticality theories.

This study investigates whether retinal ganglion cells encode surprise by analyzing their responses to stochastic flash sequences and finding that their responses align with a normative model of surprise encoding.

This paper develops a scalable Gaussian process model for predicting neural responses to natural images, outperforming deep neural networks on small datasets and enabling efficient model learning and comparison.

This paper optimizes visual prosthetic stimulation using patient feedback and Bayesian optimization, improving perceived image quality in healthy subjects.

This paper introduces a framework using multi-agent reinforcement learning to train and infer neural network functions in systems neuroscience.

This paper introduces a method to separate intrinsic neuron interactions from extrinsic correlations in sensory neural networks using mean-field corrections, validated on retinal recordings.

The paper develops a unified framework to compare and extend theories of efficient, predictive, and sparse coding in sensory neurons, showing how different coding objectives affect neural responses.