Lyapunov spectra of chaotic recurrent neural networks
This article is part of the Physical Review Research collection titled Physics of
Neuroscience. Recurrent networks are widely used as models of biological neural circuits …
Neuroscience. Recurrent networks are widely used as models of biological neural circuits …
Sparseprop: Efficient event-based simulation and training of sparse recurrent spiking neural networks
R Engelken - Advances in Neural Information Processing …, 2023 - proceedings.neurips.cc
Abstract Spiking Neural Networks (SNNs) are biologically-inspired models that are capable
of processing information in streams of action potentials. However, simulating and training …
of processing information in streams of action potentials. However, simulating and training …
On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools
Recurrent neural networks (RNNs) have been successfully applied to a variety of problems
involving sequential data, but their optimization is sensitive to parameter initialization …
involving sequential data, but their optimization is sensitive to parameter initialization …
[PDF][PDF] Chaotic neural circuit dynamics
R Engelken - 2017 - d-nb.info
Chaotic Neural Circuit Dynamics Page 1 Chaotic Neural Circuit Dynamics Dissertation for the
award of the degree “Doctor rerum naturalium” Division of Mathematics and Natural Sciences of …
award of the degree “Doctor rerum naturalium” Division of Mathematics and Natural Sciences of …
Statistical mechanics of phase-space partitioning in large-scale spiking neuron circuits
Synaptic interactions structure the phase space of the dynamics of neural circuits and
constrain neural computation. Understanding how requires methods that handle those …
constrain neural computation. Understanding how requires methods that handle those …