Publications Google Scholar
Sean Bittner, Agostina Palmigiano, Alex Piet, Chunyu Duan, Carlos Brody, Kenneth Miller, and John Cunningham.
"Interrogating theoretical models of neural computation with deep inference." bioRxiv (2019): 837567.
Abigail Russo, Ramin Khajeh, Sean Bittner, Sean Perkins, John Cunningham, LF Abbott, and Mark Churchland "Neural trajectories in the supplementary motor area and primary motor cortex exhibit
distinct geometries, compatible with different classes of computation." bioRxiv (2019): 650002.
Sean Bittner and John Cunningham. "Approximating exponential family models
(not single distributions) with a two-network architecture."arXiv preprint arXiv:1903.07515 (2019).
Abigail Russo, Sean Bittner, Sean Perkins, Jeffrey Seely, Brian London, Antonio Lara, Andrew Miri, Najja Marshall,
Adam Kohn, Thomas Jessell, LF Abbott, John Cunningham, and Mark Churchland.
Motor cortex embeds muscle-like commands in an untangled population response.
Neuron 97.4 (2018):
Sean Bittner, Ryan Wiliamson, Adam Snyder, Ashok Litwin-Kumar, Brent Doiron, Matt Smith, Steven Chase, and Byron Yu.
Population activity structure of excitatory and inhibitory neurons. PloS one 12.8 (2017): e0181773.
Sean Bittner, Siheng Chen, and Jelena Kovacevic. Fast algorithm for neural network reconstruction. In
Proc. IEEE Int. Sympo. Biomed. Imag., Brooklyn, NY, Apr. 2015.
"Interrogating theoretical models of neural computation with deep inference." Computational Neuroscience Journal Club, Princeton University, Princeton, NJ, Nov. 2019.
"Degenerate solution networks (DSNs) for theoretical neuroscience." Group for Neural Theory, École normale supérieure, Paris, France, Feb. 2019.
"Controlling for known structure in population neural recordings using maximum entropy processes (MEPs)." Bernstein Network Workshop on Dimensions of Neural Coding, Computation and Communication, Berlin, Germany, Sept. 2018.
"Maximum entropy processes for population-level hypothesis testing." Jazayeri Lab Meeting, Massachusetts Institute of Technology, Boston, MA, July 2018.
L. Sibener et al. Columbia University Neuroscience Outreach (CUNO): Publicly available curriculum to share science with our local community. Society for Neuroscience, Chicago, Illinois. 2019.
S. Bittner, Alex Piet, Chunyu Duan, Agostina Palmigiano, Kenneth D. Miller, Carlos Brody, and J. Cunningham. Examining models in theoretical neuroscience with degenerate solution networks
Bernstein Network Computational Neuroscience Conference, Berlin, Germany, 2019.
A. Dmitrienko, S. Bittner, and J. Cunningham. Investigating the structure of abstraction in neural networks with
population-level hypothesis testing
. Joint Statistical Meeting, Denver, Colorado, 2019.
S. Bittner and J. Cunningham. Approximating exponential family models (not single distributions) with a two-network
. Workshop on Invertible Neural Networks and Normalizing Flows, ICML, Long Beach, California, 2019.
S. Bittner and J. Cunningham. Degenerate solution networks (DSNs) for theoretical neuroscience
. CoSyNe, Lisbon, Portugal, 2019.
A. Russo, S. Bittner, J. Seely, S. Perkins, B. London, N Marshall, A. Lara, A. Miri, A. Kohn, T. Jessell, L. Abbott, J. Cunningham, and M. Churchland.
Motor cortical activity reflects a detangled version of muscle activity.
Neural Control of Movement. Dublin,
A. Russo, S. Bittner, J. Seely S. Perkins, B. London, N. Marshall, A. Lara, A. Miri, A. Kohn, T. Jessell, L. Abbott, J.
Cunningham, and M. Churchland. Changes in motor cortex population structure between movement types.
Society for Neuroscience. San Diego, CA, 2016.
S. Bittner, R. Williamson, A. Snyder, A Litwin-Kumar, B. Doiron, S. Chase, M. Smith, and B. Yu. Effects of
excitatory versus inhibitory neuron sampling on outputs of dimensionality reduction.
CoSyNe, Salt Lake City, Utah, Feb. 2016.
S. Bittner, R. Williamson, A. Litwin-Kumar, B. Doiron, and B. Yu. Dimensionality reduction on membrane potentials
from neural populations.
School on Neurophysiology for Neural and Biomedical Engineering, Zermatt, Switzerland, Aug. 2015.