936 citations · 1.7k across the 53 of their papers we have counts for
27 papers · 1 filter
A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation
Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin +4
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 ca…
Neurons as Detectors of Coherent Sets in Sensory Dynamics
Joshua L. Pughe-Sanford, Xuehao Ding, Jason J. Moore +4
We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact representations…
Neuronal Temporal Filters as Normal Mode Extractors
Siavash Golkar, Jules Berman, David Lipshutz +3
To generate actions in the face of physiological delays, the brain must predict the future. Here we explore how prediction may lie at the core of brain function by considering a ne…
The Neuron as a Direct Data-Driven Controller
Jason Moore, Alexander Genkin, Magnus Tournoy +3
In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a…
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii +1
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between the…
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate…