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Self-Organization of Cortical Information Processing
Book chapter

Self-Organization of Cortical Information Processing

Thomas P. Vogl, Kim T. Blackwell and Daniel L. Alkon
Brain and Values, pp.179-205
Psychology Press, 1
1998
DOI: 10.4324/9780203763834-8

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Abstract

Over the past decade, significant progress has been made in the understanding of cortical organization within the context of extracting information from images and comprehending their content. In those areas of striate cortex in which retinotopic relationships and spatial frequency information is preserved, the organization of receptive fields and relationships of structure and function are beginning to be revealed. At the other end of the chain of visual cortical information processing, inferior temporal (IT) cortex cells have been shown to respond selectively to highly complex stimuli, including specific faces. The cortical information processing paradigm presented here suggests how relatively local features derived from the visual scene by the striate cortex could be transformed into composite features of great specificity that are identifiable in IT cortex. Biophysical properties of dendrites and spines, in particular mechanisms of synaptic modifiability and memory storage in conjunction with often ignored but critically important statistical properties of sensory inputs, facilitates a self-organization of neuronal connectivity for information processing that allow for increasing specificity in successive processing layers. This chapter presents a novel framework for conceptualizing cortical information processing, a framework that is parsimonious with data on the architecture and function of the neocortex. It provides a working hypothesis for mechanisms that transform large sets of features provided by striate cortex into a representation with recognition-specific components. Associative learning and memory require sites for convergence of multiple signals that become associated. The neuroanatomic design for afferent convergence, the electrophysiologic changes observed in close correlation with associative learning and memory, and the imaged biochemical correlates, collectively implicates post-synaptic loci on dendritic branches in memory storage. Neuronal elements are instantiated based on the functional constraints (FCs) among inputs, while the Dendritic Spine Cluster’s (DSC) on the dendritic tree of these neuronal elements are self-selected for specific combinations of constrained inputs. Development of this computer model has begun as an extension of the Dystal associative learning algorithm.
 Receptive Field Dendritic Tree visual cortex Presynaptic Input Information Processing Paradigm It Striate Cortex Synaptic Input Dendritic Branches Associative Memory Storage Inhibitory Intemeurons Associative Learning Dendritic Spine Calcium Dependent Potassium Channel clusters Cortical Information Processing models Multi-layer System Information Processing Nictitating Membrane Conditioning dendrites spines Synaptic Spines Inhibitory Interneurons Neuronal Elements synapses Hypothetical Neuron Pavlovian Conditioning Potassium Channel Conductance neurophysiology FC Purkinje Cell Dendrites Inferiotemporal cortex

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