Bayesian model for brain computation

Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng ORCID: https://orcid.org/0000-0003-0939-9145 (2005) Bayesian model for brain computation. In: 2005 International Conference on Complex Medical Engineering (CME 2005), 15-18 May 2005, Takamatsu, Japan.


Abstract

[Abstract]: In this paper we describe the human brain as a Bayesian based internal model and use a decomposition strategy to learn and make prediction. This model combines two processes which together contribute to the state estimate and mimic the causal flow of a process by predicting next state given the current state and nerve signal using Bayesian rules.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version due to publisher copyright policy.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013)
Date Deposited: 11 Oct 2007 00:31
Last Modified: 25 Nov 2013 01:06
Uncontrolled Keywords: Bayesian rule, brain model, state estimate, prediction
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080603 Conceptual Modelling
11 Medical and Health Sciences > 1109 Neurosciences > 110903 Central Nervous System
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460903 Information modelling, management and ontologies
32 BIOMEDICAL AND CLINICAL SCIENCES > 3209 Neurosciences > 320903 Central nervous system
40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/908

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