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Modeling Brain Function: The World of Attractor Neural Networks Daniel J. Amit
Publisher: Cambridge University Press

The construction of the world's largest functional brain model and large-scale, real-time hardware simulations rely on the same mathematical and neuromorphic methods. University of Pittsburgh researchers have reproduced the brain's complex electrical impulses onto models made of living brain cells that provide an unprecedented view of the neuron activity behind memory formation. Modeling Brain Function: The World of Attractor Neural Networks by Daniel J. Modeling Brain Function: The World of Attractor Neural Networks Daniel J. Amit: Modeling Brain Function: the World of Attractor Neural Networks (Cambridge University Press, Cambridge, 1989). Citation: Kanamaru T, Fujii H, Aihara K (2013) Deformation of Attractor Landscape via Cholinergic Presynaptic Modulations: A Computational Study Using a Phase Neuron Model. Systems can have multiple attractors of any type; the “energy landscape” of a dynamical system can be plotted as a function of how different initial conditions may ultimately fall into the “basin of attraction” for various attractors. Generally, neural networks in real world have very high dimension, which is too hard to study. [Ami], Amit, D.J., Modeling Brain Function : The World of Attractor Neural Networks, Cambridge University Press, Cambridge, UK, 1989. The work pursued here is coordinated with a parallel application that focuses on neural network systems, but the dependencies are arranged to make the present article the main and the more self-contained work, to serve as a conceptual frame and a technical background for the network project. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or Analyzing the structure of behavioral variability may provide evidence for understanding whether the variability is the result of cumulated errors in an imperfectly wired brain (system noise) or whether the variability is under neural control. Modeling Brain Function: The World of Attractor Neural Networks. Hot Deals Modeling Brain Function: The World of Attractor Neural Networks Tags: Best buy! Fortunately, research in anatomy and physiology shows that neurons in biological brains are grouped together into functional circuits [13, 14]. Hot Deals Modeling Brain Function: The World of Attractor Neural Networks order online now. "The dynamics of spiking neural networks are in general highly nonlinear and involve a very large number of degrees of freedom," Fiete tells Phys.org, addressing their analysis of how stored memory in continuous attractor networks will stochastically . Quasi-attractors can also be found in the field of chaotic associative memory in neural networks [16]–[25], in which patterns stored in the network become quasi-attractors and the network exhibits transitive dynamics between stored patterns. Www-psych.stanford.edu % The book itself may be ordered directly from Addison-Wesley in the U.S.. Advanced Series in Dynamical Systems 8, World Scientific. Polymer Surface Dynamics Reihe: .