Neural Comput - A low-order model of biological neural networks.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.

Resumo Limpo

biolog plausibl loword model lom biolog neural network propos lom recurr hierarch network model dendrit node tree spike nonspik neuron unsupervis supervis covari accumul learn mechan feedback connect scheme maxim general compon model motiv necessit make lom learn retriev easili without differenti optim iter cluster detect recogn multipl hierarch corrupt distort occlud tempor spatial pattern four model dendrit node given describ hyperbol polynomi act like exclusiveor logic gate model dendrit node input two binari digit model dendrit encod network model dendrit node encod input result code orthogon properti code store synaps unsupervis covari learn supervis covari learn unsupervis accumul learn depend type postsynapt neuron mask matrix dendrit tree whose upper part compris model dendrit encod enabl maxim general corrupt distort occlud data mathemat organ ideal dendrit tree overlap nest input vector model nonspik neuron transmit inhibitori grade signal modul neighbor model spike neuron model spike neuron evalu subject probabl distribut spd label input model dendrit encod generat spike train spds fire rate feedback connect higher layer differ number unitdelay devic reflect differ signal travel time enabl lom fulli util tempor spatial associ inform biolog plausibl compon model discuss numer exampl given demonstr lom oper retriev general unsupervis supervis learn

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