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

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.

[1]  G. Shepherd.  The Synaptic Organization of the Brain , 1979 .

[2]  T. Wiesel,et al.  Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex , 1979, Nature.

[3]  Richard M. Salter,et al.  Modeling neural networks in Scheme , 1986 .

[4]  Stuart Geman,et al.  A chaos hypothesis for some large systems of random equations , 1982 .

[5]  J. Pearson,et al.  Processing capability of the primary visual cortex and possible physiologic basis for an apparent motion illusion , 1983, Experimental Neurology.

[6]  W. A. Little,et al.  Analytic study of the memory storage capacity of a neural network , 1978 .

[7]  L L Iversen.  The chemistry of the brain. , 1979, Scientific American.

[8]  J. Szentágothai.  The modular architectonic principle of neural centers. , 1983, Reviews of physiology, biochemistry and pharmacology.

[9]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[10]  S. Grossberg.  How does a brain build a cognitive code , 1980 .

[11]  S. Grossberg.  Processing of Expected and Unexpected Events During Conditioning and Attention: A Psychophysiological Theory , 1982 .

[12]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[13]  Donald O. Walter,et al.  Mass action in the nervous system , 1975 .

[14]  L. Palmer,et al.  The retinotopic organization of lateral suprasylvian visual areas in the cat , 1978, The Journal of comparative neurology.

[15]  L. Nadel,et al.  The Hippocampus as a Cognitive Map , 1978 .

[16]  M. Colonnier.  Synaptic patterns on different cell types in the different laminae of the cat visual cortex. An electron microscope study. , 1968, Brain research.

[17]  G. Moruzzi,et al.  Brain stem reticular formation and activation of the EEG. , 1949, Electroencephalography and clinical neurophysiology.

[18]  T. Peele.  The Neuroanatomic Basis for Clinical Neurology , 1962 .

[19]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

[20]  P. Somogyi,et al.  The study of golgi stained cells and of experimental degeneration under the electron microscope: A direct method for the identification in the visual cortex of three successive links in a neuron chain , 1978, Neuroscience.

[21]  T. Powell,et al.  A qualitative and quantitative electron microscopic study of the neurons in the primate motor and somatic sensory cortices. , 1979, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[22]  J. Sprague,et al.  Corticofugal projections from the visual cortices to the thalamus, pretectum and superior colliculus in the cat , 1974, The Journal of comparative neurology.

[23]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[24]  E. John,et al.  ELECTROPHYSIOLOGICAL CORRELATES OF DIFFERENTIAL APPROACH‐AVOIDANCE CONDITIONING IN CATS1 , 1960, The Journal of nervous and mental disease.

[25]  S. Geman.  Almost Sure Stable Oscillations in a Large System of Randomly Coupled Equations , 1982 .

[26]  A. Luria.  The Working Brain , 1973 .

[27]  George L. Gerstein,et al.  Identification of functionally related neural assemblies , 1978, Brain Research.

[28]  D. Kahneman,et al.  Attention and Effort , 1973 .

[29]  P. Schiller,et al.  Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. , 1976, Journal of neurophysiology.