NDLI: Comparison of electrophysiological spatial pattern changes in short-and long-term learning

Content Provider	IEEE Xplore Digital Library
Author	Freeman, W.J.
Copyright Year	2010
Description	Author affiliation: Dept. of Mol. & Cell Biol., Univ. of California at Berkeley, Berkeley, CA, USA (Freeman, W.J.)
Abstract	In cognition, sensation - "the activity of a sense organ and closely connected nerve structures" - is followed by perception - "the meaningful impression of any object obtained by use of the senses" (Webster). Brains perform the microscopic sensory processes of extracting and processing information from the world. They have been well modeled with cellular neural networks (CNN) by modifying connections in reinforcement learning. Brains perform the mesoscopic perceptual processes of categorizing the information and constructing its meaning by using distributed memories. Perception requires a different topology than CNN, which is provided by random graph theory. Correspondingly cerebral cortex has two kinds of networks that learn. The sensory and motor cortices contain numerous, highly specialized, local networks that are tuned by local, rapid, short-term learning to select features in the input and bind them into sustained discharges of microscopic cell assemblies. These local systems are embedded in brain-wide, scale-free, mesoscopic feedback connections that learn slowly in consolidation. The distributed architecture integrates sensory input with selected memories into sequences of large-scale, distributed neural activity patterns that express meanings of sensory information. The patterns resemble cinematographic frames. The construction of each frame is by a phase transition. The brain topology, process, and activity patterns may be modeled by embedding multiple CNN in a large-scale random graph. That is neuropercolation; its goal is to provide a superior alternative to differential equations, which are now used to model perception.
Starting Page	1
Ending Page	6
File Size	1892076
Page Count	6
File Format	PDF
ISBN	9781424466795
e-ISBN	9781424466801
DOI	10.1109/CNNA.2010.5430291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular neural networks Microscopy Brain modeling Cognition Sense organs Data mining Learning Network topology Graph theory Cerebral cortex scale-free AM patterns cerebral cortex electrocorticologram (ECoG) neuropercolation neurotopology
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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