Itamar Daniel Landau

A predictive theory of experimental design for inferring neural population geometry in large-scale recordings

The paper develops a predictive theory for experimental design in neural population geometry inference, showing that more neurons allow accurate inference with fewer trials.

Low-dimensional Neural Codes Suppress Neuronal Noise and Extend the Working Memory Duration

The paper shows that low-dimensional neural coding suppresses noise and extends working memory duration in recurrent networks through theoretical analysis and experimental validation in mice.

Coherent chaos in a recurrent neural network with structured connectivity

This paper introduces a model of recurrent neural networks with structured connectivity that generates spatially correlated chaotic activity, showing how structured connections can produce coherent fluctuations across the network.