A low-order model of biological neural networks for hierarchical or temporal pattern clustering, detection and recognition

Abstract

A low-order model (LOM) of biological neural networks, which is biologically plausible, is herein reported. LOM is a recurrent hierarchical network composed of novel models of dendritic trees for encoding information, spiking neurons for computing subjective probability distributions and generating spikes, nonspiking neurons for transmitting inhibitory graded signals to modulate their neighboring spiking neurons, unsupervised and supervised covariance learning and accumulation learning mechanisms, synapses, a maximal generalization scheme, and feedback connections with different delay durations. An LOM with a main network that learns without supervision and clusters similar patterns, and offshoot structures that learn with supervision and assign labels to clusters formed in the main network is proposed as a learning machine that learns and retrieves easily, generalizes maximally on corrupted, distorted and occluded temporal and spatial patterns, and utilizes fully the spatially and temporally associated information.