310 citations · 1.9k across the 36 of their papers we have counts for
10 papers · 1 filter
Simplified derivations for high-dimensional convex learning problems
David G. Clark, Haim Sompolinsky
Statistical-physics calculations in machine learning and theoretical neuroscience often involve lengthy derivations that obscure physical interpretation. Here, we give concise, non…
Soft-margin classification of object manifolds
Uri Cohen, Haim Sompolinsky
A neural population responding to multiple appearances of a single object defines a manifold in the neural response space. The ability to classify such manifolds is of interest, as…
A new role for circuit expansion for learning in neural networks
Julia Steinberg, Madhu Advani, Haim Sompolinsky
Many sensory pathways in the brain rely on sparsely active populations of neurons downstream from the input stimuli. The biological reason for the occurrence of expanded structure…
Classification and Geometry of General Perceptual Manifolds
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location…
Linear Readout of Object Manifolds
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Objects are represented in sensory systems by continuous manifolds due to sensitivity of neuronal responses to changes in physical features such as location, orientation, and inten…
Transition to chaos in random neuronal networks
Jonathan Kadmon, Haim Sompolinsky
Firing patterns in the central nervous system often exhibit strong temporal irregularity and heterogeneity in their time averaged response properties. Previous studies suggested th…