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IEEE Transactions on Biomedical Engineering
Article . 2011 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2020
Data sources: DBLP
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Understanding Perception Through Neural “Codes”

Authors: Freeman, Walter J, III;

Understanding Perception Through Neural “Codes”

Abstract

A major challenge for cognitive scientists is to deduce and explain the neural mechanisms of the rapid transposition between stimulus energy and recalled memory-between the specific (sensation) and the generic (perception)-in both material and mental aspects. Researchers are attempting three explanations in terms of neural codes. The microscopic code: cellular neurobiologists correlate stimulus properties with the rates and frequencies of trains of action potentials induced by stimuli and carried by topologically organized axons. The mesoscopic code: cognitive scientists formulate symbolic codes in trains of action potentials from feature-detector neurons of phonemes, lines, odorants, vibrations, faces, etc., that object-detector neurons bind into representations of stimuli. The macroscopic code: neurodynamicists extract neural correlates of stimuli and associated behaviors in spatial patterns of oscillatory fields of dendritic activity, which self-organize and evolve on trajectories through high-dimensional brain state space. This multivariate code is expressed in landscapes of chaotic attractors. Unlike other scientific codes, such as DNA and the periodic table, these neural codes have no alphabet or syntax. They are epistemological metaphors that experimentalists need to measure neural activity and engineers need to model brain functions. My aim is to describe the main properties of the macroscopic code and the grand challenge it poses: how do very large patterns of textured synchronized oscillations form in cortex so quickly?

Country
United States
Related Organizations
Keywords

Cerebral Cortex, Neurons, Sensation, Electroencephalography, Signal Processing, Computer-Assisted, Smell, Multivariate Analysis, Cats, Animals, Cognitive Science, Humans, Computer Simulation, Perception, Rabbits

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Average
Average
Green
hybrid