A self-consistent mean-field theory, exact in the N-->∞ limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. Home » Chaos in random neural networks. We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. We propose an analytically tractable neural connectivity model with power-law distributed synaptic strengths. View Article PubMed/NCBI Google Scholar 16. Firing patterns in the central nervous system often exhibit strong temporal irregularity and heterogeneity in their time averaged response properties. pmid:10039285 . EPL (Europhysics Letters). Chaos, in the sense that neighboring orbits separate exponentially in time, is a feature common to a wide class of dynamical systems [1,2]. Tirozzi B, Tsodyks M. Chaos in highly diluted neural networks. Harnessing Chaos in Recurrent Neural Networks Dean V. Buonomano1,2,3,* 1Department of Neurobiology 2Department of Psychology 3Brain Research Institute University of California, Los Angeles, Los Angeles, CA 90095, USA *Correspondence: dbuono@ucla.edu DOI 10.1016/j.neuron.2009.08.003 Chaos in random neural networks. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making … Publication Type: Journal Article. A continuous-time dynamic model of a network of N nonlinear elements interacting via random asymmetric couplings is studied. Chaos in random neural networks. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics. Recent Publications. We produce a bifurcation parameter independent of the connectivity that allows a sustained activity and the occurrence of chaos when reaching a critical value. We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Authors: Haim ... Search form. It is studied here for a randomly diluted architecture. The occurrence of chaos in recurrent neural networks is supposed to depend on the architecture and on the synaptic coupling strength. Sompolinsky H, Crisanti A, Sommers HJ. 1991;14(8):727–732. Physical Review Letters. is that random residual networks for many nonlinearities such as tanh live on the edge of chaos, in that the cosine distance of two input vectors will converge to a fixed point at a polynomial rate, rather than an exponential rate, as with vanilla tanh networks. The occurrence of chaos in recurrent neural networks is supposed to depend on the architecture and on the synaptic coupling strength. 1988;61(3):259–262. Previous studies suggested that these properties are outcome of an intrinsic chaotic dynamics. Full text Get a printable copy (PDF file) of the complete article (1.0M), or click on a page image below to browse page by page. Chaos in a Model of Random Neural Networks | SpringerLink Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. It is studied here for a randomly diluted architecture. Search . A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits.

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