Transforming Neural Computations and Representing Time

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 93; no. 26; pp. 15440 - 15444
• Main Author: Hopfield, J. J.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences of the United States of America 24.12.1996; National Acad Sciences; National Academy of Sciences; The National Academy of Sciences of the USA
• Subjects: Action Potentials; Animals; Biological Sciences; Brain; Cerebral Cortex - physiology; Evolution; Glycolysis; Humans; Models, Neurological; Neurobiology; Neurology; Neurons; Neurons - physiology; Odors; Olfactory pathways; Olfactory perception; Perception - physiology; Perceptions; Syllables; Symbolism; Time
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.93.26.15440

Motifs of neural circuitry seem surprisingly conserved over different areas of neocortex or of paleocortex, while performing quite different sensory processing tasks. This apparent paradox may be resolved by the fact that seemingly different problems in sensory information processing are related by transformations (changes of variables) that convert one problem into another. The same basic algorithm that is appropriate to the recognition of a known odor quality, independent of the strength of the odor, can be used to recognize a vocalization (e.g., a spoken syllable), independent of whether it is spoken quickly or slowly. To convert one problem into the other, a new representation of time sequences is needed. The time that has elapsed since a recent event must be represented in neural activity. The electrophysiological hallmarks of cells that are involved in generating such a representation of time are discussed. The anatomical relationships between olfactory and auditory pathways suggest relevant experiments. The neurophysiological mechanism for the psychophysical logarithmic encoding of time duration would be of direct use for interconverting olfactory and auditory processing problems. Such reuse of old algorithms in new settings and representations is related to the way that evolution develops new biochemistry.