The “Brain-State-in-a-Box” neural model is a gradient descent algorithm☆
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Distributed representations of ambiguous words and their resolution in a connectionist network
2013, Lexical Ambiguity Resolution: Perspective from Psycholinguistics, Neuropsychology and Artificial IntelligenceInformation storage and retrieval analysis of hierarchically coupled associative memories
2012, Information SciencesCitation Excerpt :On the other hand, the inter-group weight matrix Wcor(a,b) is designed to follow the generalised Hebb rule or Outer Product Method algorithm through the selected patterns extracted from the first-level memories. The energy function studied by Golden [12] can be viewed as being a special case of Eq. (9) when γ = 0 and Nr = 0 (individual network). Golden, in his studies, was able to demonstrate that the network energy decreases over time.
Optimal and robust design of brain-state-in-a-box neural associative memories
2010, Neural NetworksCitation Excerpt :Hopfield (1984) analyzed characteristics of a continuous-time BSB neural network using Lyapunov function approach. Golden (1986) proved that all trajectories of the BSB neural network with a real symmetric weight matrix approach to the set of equilibrium points under a certain condition, which will be used as the global stability condition in the present paper. Li, Michel, and Porod (1989) analyzed a continuous-time BSB neural network, which is referred to as the linear system in a saturated mode (LSSM) model, and investigated the stability of equilibrium points in the LSSM model.
Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning
2006, Cognitive Systems ResearchThe BSB neural network in the convex body spanned by the prototype patterns for associative memory
2002, Applied Mathematics and ComputationConvergence of Discrete-Time Cellular Neural Networks with Application to Image Processing
2023, International Journal of Bifurcation and Chaos
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This research was funded in part by Grant BNS-82-14728 from the National Science Foundation, Memory and Cognitive Processes Section to J. A. Anderson.