Jeffrey Seely



Topological data analysis

Publications / Presentations

Propagting targets through noninvertible layers of deep networks
Jeffrey Seely, Raoul-Martin Memmesheimer, Larry Abbott
Poster at Cognitive Computational Neuroscience, 2017

Behaviorally selective engagement of short-latency effector pathways by motor cortex
Andrew Miri, Claire Warriner, Jeffrey Seely, Gamaleldin Elsayed, John Cunningham, Mark Churchland, Thomas Jessell
Neuron, 2017

Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1
Jeffrey Seely, Matthew Kaufman, Stephen Ryu, Krishna Shenoy, John Cunningham, Mark Churchland
PLoS Computational Biology, 2016

The largest response component in motor cortex reflects movement timing but not type
Matthew Kaufman, Jeffrey Seely, David Sussillo, Stephen Ryu, Krishna Shenoy, Mark Churchland
eneuro, 2016

State-space models for cortical-muscle transformations
Jeffrey Seely, Matthew Kaufman, Chris Cueva, Liam Paninski, Krishna Shenoy, Mark Churchland
Talk at COSYNE, 2014

Quantifying representational and dynamical structure in large neural datasets
Jeffrey Seely, Matthew Kaufman, Adam Kohn, Matthew Smith, Anthony Movshon, Nicholas Priebe, Stephen Lisberger, Stephen Ryu, Krishna Shenoy, Larry Abbott, John Cunningham, Mark Churchland
Poster at COSYNE, 2013

Response normalization in theoretical firing rate models
Jeffrey Seely, Carson Chow
Poster at COSYNE, 2011

The role of mutual inhibition in binocular rivalry
Jeffrey Seely, Carson Chow
Journal of Neurophysiology, 2011

Optimization of the leak conductance in the squid giant axon
Jeffrey Seely, Patrick Crotty
Physical Review E, 2010


Topological analysis of motor cortex
New York Applied Topology Meeting
June 2016

Neural computation: visual cortex versus motor cortex
The Applied Topology Seminar, University of Pennsylvania
March 2016

Dynamical systems and system identification
NeuroTheory Workshop, Janelia Research Campus
November 2014

Neural computation: representation and dynamics
CodeNeuro, San Francisco
November 2014

Denoising neural signals with tensor decompositions
Modeling variability in neuronal populations, workshop at NYU
June 2014