  A biologically plausible low-order model (LOM) of biological neural networks
is a recurrent hierarchical network of dendritic nodes/trees,
spiking/nonspiking neurons, unsupervised/ supervised covariance/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/hierarchical corrupted,
distorted, and occluded temporal and spatial patterns.

Optimization and Control;Neural and Evolutionary Computing;Neurons and Cognition