Literature DB >> 23972236

Neural representations: some old problems revisited.

P M Milner1.   

Abstract

Hebb's (1949) cell assembly, originally conceived as an explanation for stimulus equivalence, also serves as a neural representation of stimuli. Association between cell assemblies was a major theme of Hebb's book, but the state of physiological knowledge at the time was such that no satisfactory basis for it could he devised. Subsequent theory has been more concerned with the recognition and "attractor" features of the cell assembly than its capacity to represent and associate concepts. This is unfortunate because while generalization is important, so is discrimination, which is not well served by an attractor model. This dilemma is avoided by postulating that stimulus representation and stimulus equivalence involve different neural circuits. Human beings can instantly form and use associations between many more concepts than there are synapses on the average cortical neuron, indicating that the associative links between engrams are sparse. The connections within an engram, on the other hand. must be dense to ensure that a weak input can activate all its neurons. It would appear that two processes are anatomically and physiologically different, which may account for the fact that engrams remain distinct in spite of being associated with each other. The fact that a single concept may have very many associations puts a heavy demand on the process of selective attention to avert complete chaos. I propose that attention is a manifestation of motivation. Motivation facilitates responses, which in turn facilitate engrams of associated stimuli. The enhanced engram activity is fed back through centrifugal paths to intensify sensory input that has previously played a part in executing the planned responses. Attention may also contribute to a mechanism that prevents the engrams of component parts of an object from being assimilated into the engram of the whole.

Entities:  

Year:  1996        PMID: 23972236     DOI: 10.1162/jocn.1996.8.1.69

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  8 in total

Review 1.  Neural syntax: cell assemblies, synapsembles, and readers.

Authors:  György Buzsáki
Journal:  Neuron       Date:  2010-11-04       Impact factor: 17.173

2.  Distributed cell assemblies for general lexical and category-specific semantic processing as revealed by fMRI cluster analysis.

Authors:  Friedemann Pulvermüller; Ferath Kherif; Olaf Hauk; Bettina Mohr; Ian Nimmo-Smith
Journal:  Hum Brain Mapp       Date:  2009-12       Impact factor: 5.038

3.  Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

Authors:  Max Garagnani; Thomas Wennekers; Friedemann Pulvermüller
Journal:  Cognit Comput       Date:  2009-06       Impact factor: 5.418

4.  A neuroanatomically grounded Hebbian-learning model of attention-language interactions in the human brain.

Authors:  Max Garagnani; Thomas Wennekers; Friedemann Pulvermüller
Journal:  Eur J Neurosci       Date:  2008-01       Impact factor: 3.386

5.  Newly-formed emotional memories guide selective attention processes: Evidence from event-related potentials.

Authors:  Harald T Schupp; Ursula Kirmse; Ralf Schmälzle; Tobias Flaisch; Britta Renner
Journal:  Sci Rep       Date:  2016-06-20       Impact factor: 4.379

6.  Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain.

Authors:  J Gerard Wolff
Journal:  Front Psychol       Date:  2016-11-03

7.  The Emergent Engram: A Historical Legacy and Contemporary Discovery.

Authors:  Bryan D Devan; Kyle Berger; Robert J McDonald
Journal:  Front Behav Neurosci       Date:  2018-08-07       Impact factor: 3.558

Review 8.  Thinking in circuits: toward neurobiological explanation in cognitive neuroscience.

Authors:  Friedemann Pulvermüller; Max Garagnani; Thomas Wennekers
Journal:  Biol Cybern       Date:  2014-06-18       Impact factor: 2.086

  8 in total

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