Publications

Peer-reviewed journals and proceedings

Timcheck J, Kadmon J, Boahen K, and Ganguli S. Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays, PLoS Comp. Bio, 2022

Kadmon J, Timcheck J and Ganguli S Predictive coding in balanced neural networks with noise, chaos and delays, NeurIPS, 2020
Bahri Y, Kadmon J, Pennington J, Schoenholz S, Sohl-Dickstein J and Ganguli, Statistical mechanics of deep learning, Annual Reviews in Condensed Matter, 2020

Kadmon J and Ganguli S, Statistical mechanics of low-rank tensor decomposition, J Stat Mech: Theory and Experiments, 2019
Marshel, Kim, Machado, Quirin, Kadmon, Benson, Raja, Chibukhchyan, Ramakrishnan, Inoue, Shane, McKnight, Susumu, Kato, Ganguli and Deisseroth, Layer-specific neural ensembles contributing to ignition and plasticity of perception, Science, 2019
Wagner MJ, Kim TH, Kadmon J, Nguyen ND, Ganguli S, Schnitzer MJ and Luo L, Cortex-cerebellum dynamics in the execution and learning of a motor task, Cell, 2019

Kadmon J and Ganguli S Statistical Mechanics of Low-Rank Tensor Decomposition, NeurIPS, 2018

Kadmon J and Sompolinsky H, Optimal architectures in a solvable model of deep networks, NeurIPS, 2016

Kadmon J and Sompolinsky, H., Transition to chaos in random neuronal networks. Physical Review X, Physical Review X, 2015

Kadmon J, Ishay JS and Bergman DJ, Properties of ultrasonic acoustic resonances for exploitation in comb construction by social hornets and honeybees, Physical Review E, 2009
Tsfadia Y, Friedman R, Kadmon J, SelzerA, Nachliel E and Gutman M, Molecular dynamics simulations of palmitate entry into the hydrophobic pocket of the fatty acid binding protein, FEBS letters, 2007

Schwarcz J, Lottem E, and Kadmon J, Network dynamics implement optimal inference in a flexible timing task, Cosyne 2023
Bauer JP , Kadmon J, and Helias M, Discrete communication mediates effective regularization in recurrent neural networks, Cosyne 2023
Timchek J, Kadmon J, Ganguli S, Boahen K, Harnessing noise and disorder to rescue optimal predictive coding, Cosyne 2019
Kadmon J and Sompolinsky H, Analytical study of simple deep networks with recursive learning. Cosyne 2018
Kadmon J and Sompolinsky H, Sensory processing by deep-networks with layer-localized learning. Cosyne 2017
Kadmon J and Sompolinsky H, Optimal architecture in a solvable model for deep networks. Bernstein Conference 2016
Kadmon J and Sompolinsky H, Chaos in random neuronal networks of Inhibitory and Excitatory neurons, NCCD 2015
Kadmon J and Sompolinsky H. Dynamic mean-field for cortical circuits. ISFN 2014