Axon shape as a basis for multinode functional units in a hierarchical neural model

• Published in: Journal of theoretical biology Vol. 130; no. 1; p. 95
• Main Author: Eilbert, J L
• Format: Journal Article
• Language: English
• Published: England 07-01-1988
• Subjects: Animals; Axons - anatomy & histology; Brain - anatomy & histology; Diptera; Humans; Interneurons - anatomy & histology; Mathematics; Mesencephalon - anatomy & histology; Models, Neurological; Nervous System Physiological Phenomena; Neurons - classification; Visual Cortex - anatomy & histology
• ISSN: 0022-5193
• DOI: 10.1016/S0022-5193(88)80166-8

The ability of animals to perform fixed action patterns and to access information by categories suggests that there are several types of hierarchical organization in the nervous system. This paper employs data about axon shape and neurotransmitter effect to demonstrate the emergence of hierarchical structure in a neural model. Two dimensions of neural classification, axon shape and neurotransmitter effect, are used to generate a five-node-type neural model. Neurons are classified as interneurons, relay cells, and monoamine transmitters on the basis of axon shape; the transmitter classifications include excitatory, inhibitory, and parameter-changing. The five types of nodes in the model correspond to all the biologically observed combinations: excitatory and inhibitory short-range, excitatory and inhibitory long-range-directional, and long-lasting long-range-diffuse nodes. The emergence of multinode functional units (MFUs) from the five-node-type model is mathematically demonstrated. These units correspond to cortical columns anatomically defined by the axon fields of relay cells, and are called columnar multinode functional units (CMFUs). CMFUs may, in turn, be part of larger functional groups designated coherent populations, which consist of widely distributed CMFUs in retinotopically equivalent locations. The existence of coherent populations imposes a three-level hierarchical structure on the model. To represent this hierarchical structure, a new type of CMFU node, which has a set of vector-valued inputs and outputs, is introduced. Each CMFU node contains a system of short-range nodes which supplies it with vector-valued inputs. Sets of long-range-diffuse nodes are also treated as vector-valued nodes whose outputs control the size and number of coherent populations. The role of coherent populations and hierarchical organization in the nervous system is discussed for such cognitive tasks as visual perception, attention and learning. Physiological and behavioral evidence are cited which support the existence of a similar three-level hierarchy in vertebrate brains.