Low-Order Model of Biological Neural Networks
This work addresses the problem of efficient and robust pattern learning and recognition for researchers interested in biologically inspired AI models.
This paper proposes a biologically plausible low-order model (LOM) for neural networks that can learn and retrieve patterns without relying on differentiation, optimization, or iteration. The model is designed to cluster, detect, and recognize multiple, hierarchical, corrupted, distorted, and occluded temporal and spatial patterns.
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.