Marcus Ghosh

This paper introduces partially recurrent neural networks (pRNNs) and shows they can match or exceed fully recurrent networks in performance while using fewer parameters.

The paper describes a massively collaborative project using spiking neural networks to study sound localization, involving 31 researchers across multiple countries.

The paper introduces a novel multisensory task to evaluate how well algorithms can integrate signals across sensory channels and time, showing that simple temporal integration can match complex recurrent models.

The paper demonstrates that nonlinear fusion outperforms linear fusion in multisensory integration tasks when sensory signals are statistically dependent, and shows that spiking neural networks can approximate this nonlinear algorithm.

This paper introduces a CRISPR-Cas9 method for rapid generation of F0 biallelic knockouts in zebrafish, enabling quick screening of complex phenotypes.

This study examines the phenotypic effects of SNX14 loss-of-function mutations in mice and zebrafish, revealing species-specific consequences despite conserved biochemical roles.

This study develops computational tools to analyze larval zebrafish behavior across multiple timescales, revealing structured patterns and identifying novel phenotypes.