Joshua L. Pughe-Sanford — accepted AI-conference papers

2026

A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation

Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin, Pembe Gizem Ozdil, Philip Greengard, Anirvan M. Sengupta +1

AAAI 2026technical

We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window of its input history onto a canonical direction obtained via canonical correlation analysis (CCA) of previously observed past–future input pairs, and

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2025

Neurons as Detectors of Coherent Sets in Sensory Dynamics

Joshua L. Pughe-Sanford, Xuehao Ding, Jason J Moore, Anirvan M. Sengupta, Charles Epstein, Philip Greengard +1

NeurIPS 2025poster

We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact representations of such dynamics. From prior experience, neurons learn {\it coherent sets}—regions of stimulus state space whose trajec…

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