Representation of Information

The central enigma in brain science is how information is represented within nervous systems. Understanding mechanisms of the vast capacities for storage, retrieval, and processing of information within the brain would be of enormous benefit not only to neuroscience, but to workers in computer science, particularly artificial intelligence (AI). Indeed, vast strides have been made in AI by utilizing "good guesses" about brain information processing and, conversely, understanding of brain functions and capabilities is being advanced by AI related theory including neural nets and parallel connectionism.

The underlying assumption about brain function and the comparative basis for AI considers parallel networks of connected units in which neurons and their synaptic connections are the fundamental substrates. However individual neurons perform a significant amount of analog processing both at the level of dendrites and within their cytoskeleton. For example, modification of synaptic transmission threshold, the cornerstone of neural net learning models, is regulated by the cytoskeleton and its cytoplasmic connections. Viewing neurons as fundamental digital substrates (switches or gates in a computer) may be overlooking an important dimension available for the organization of intelligence.

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