3 papers
cond-mat.dis-nn2026
Predictable Mean-Field Chaos in Random Recurrent Neural Networks
Alkesh Yadav, Vladimir Shaidurov, Jonathan Kadmon
Dynamical mean-field theory (DMFT) maps deterministic chaos in random recurrent neural networks to an effective Gaussian process, usually treated as an ensemble description rather…
q-bio.NC2026
Discrete signaling mediates chaotic regularization in recurrent neural networks
Jan Bauer, Christian Keup, Jonathan Kadmon +1
Cortical circuits operate in a regime of intrinsic chaos, where even tiny changes in input can lead to divergent neural responses. Yet, remarkably, population codes in the brain va…
cs.LG2026
Training Large Neural Networks With Low-Dimensional Error Feedback
Maher Hanut, Jonathan Kadmon
Training deep neural networks typically relies on backpropagating high dimensional error signals a computationally intensive process with little evidence supporting its implementat…