4 papers
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…
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…
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…
Efficient coding with chaotic neural networks: A journey from neuroscience to physics and back
Jonathan Kadmon
This essay, derived from a lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023, explores the mutually beneficial relationship between theoretical neurosc…