Modeling brain function: the world of attractor neural networks, 1st Edition

By D. Amit. 1989

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In Neuroscience

Citations in the corpus

Low-dimensional dynamics for working memory and time encoding (2020, background): “It is based on the hypothesis that there are neural circuits that behave like an attractor neural network (5, 7, 8) (Fig.”
A single pair of neurons links sleep to memory consolidation in Drosophila melanogaster (2015, background): “…et al., 2000) enhancing MB potentiation via an excitatory feedback loop (Yu et al., 2005; Keene and Waddell, 2007) similar to what has been proposed to occur in the mammalian hippocampus (Hebb, 1949; Hopfield, 1982; Amit, 1989; Treves and Rolls, 1994; Battaglia and Treves, 1998; Lisman, 1999).”
Stochastic p-bits for Invertible Logic (2016, methodology): “The system shows robust behavior even in the presence of large levels of noise. chronous dynamics” [20, 56].”
Neural primitives for motion control (2004, background): “This, together with a dissipative component, can then be interpreted as a Lyapunov function that describes the dynamics of an attractor neural network [86].”
Nengo: a Python tool for building large-scale functional brain models (2014, background): “Many models in theoretical neuroscience are based on attractor networks (Amit, 1992; Deco and Rolls, 2003).”
Multistability and Perceptual Inference (2012, background): “Gibbs sampling operating over the MRF gives rise to what is known as Glauber dynamics (Glauber, 1963), a well-studied phenomenon in the neural networks literature (Amit, 1989).”
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, methodology): “This is the condition for linearity, under which the circuit starts to behave as a first order low-pass filter, as described in Section II.”
Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework (2008, background): “Small synchronously updated Hopfield networks [27,28] provide a class of examples that are computationally tractable and have interesting dynamics.”
Ising models for networks of real neurons (2006, background): “[2] DJ Amit, Modeling Brain Function: The World of Attractor Neural Networks (Cambridge University Press, Cambridge, 1989).”
Iterative retrieval of sparsely coded associative memory patterns (1996, background): “Thus the main concern of thispaper is binary Hebbian learning.3.”
An Empirical Exploration of Recurrent Network Architectures (2015, background): “There is a view that the information processing that takes place in biological recurrent neural networks is based on attractors (Amit, 1992; Plaut, 1995; Hinton & Shallice, 1991).”
Laminar selectivity of the cholinergic suppression of synaptic transmission in rat hippocampal region CA1: computational modeling and brain slice physiology (1994, background): “Most previous models of autoassociative and heteroassociative memory function externally set the dynamics appropriate for learning or recall within the network (Anderson, 1972; Hopfield, 1984; Amit, 1988; Kohonen, 1988).”
Progressive transformation of hippocampal neuronal representations in "morphed" environments. (2005, unknown): “It is commonly thought that this ability reflects the operation of attractor-based neural networks (Hopfield, 1982; Amit, 1989).”