Abstract
From the biological view, each component of a temporal sequence is represented by neural code in cortical areas of different orders. In whatever order areas, minicolumns divide a component into sub-components and parallel process them. Thus a minicolumn is a functional unit. Its layer IV neurons form a network where cell assemblies for sub-components form. Then layer III neurons are triggered and feed back to layer IV. Considering the delay, through Hebbian learning the connections from layer III to layer IV can associate a sub-component to the next. One sub-component may link multiple following sub-components plus itself, but the prediction is deterministic by a mechanism involving competition and threshold dynamic. So instead of learning the whole sequence, minicolumns selectively extract information. Information for complex concepts are distributed in multiple minicolumns, and long time thinking are in the form of integrated dynamics in the whole cortex, including recurrent activity.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Sun, R., Giles, L.C.: Sequence Learning: From Recognition and Prediction to Sequential Decision Making. IEEE Intell. Syst. 16, 67–70 (2001)
Wang, D., Arbib, A.M.: Complex Temporal Sequence Learning Based on Short-term Memory. Proc. IEEE 78, 1536–1543 (1990)
Wang, D., Yuwono, B.: Anticipation-Based Temporal Pattern Generation. IEEE Trans. Syst. Man Cybern. 25, 615–628 (1995)
Wang, D., Yuwono, B.: Incremental Learning of Complex Temporal Patterns. IEEE Trans. Neural Networks 7, 1465–1481 (1996)
Starzyk, A.J., He, H.: Anticipation-Based Temporal Sequences Learning in Hierarchical Structure. IEEE Trans. Neural Networks 18, 344–358 (2007)
Squire, R.L., Zola, M.S.: The Medial Temporal Lobe Memory System. Science 253, 1380–1386 (1991)
Thompson, F.R., Kim, J.J.: Memory systems in the brain and localization of a memory. PNAS 93, 13438–13444 (1996)
Mayes, A., Montaldi, D., Migo, E.: Associative Memory and the Medial Temporal Lobes. Trends Cogn. Sci. 11, 126–135 (2007)
Creutzfeldt, D.O.: Cortex Cerebri: Performance, Structural and Functional Organization of the Cortex. Oxford University Press, USA (1995)
Mountcastle, B.V.: The Columnar Organization of the Neocortex. Brain 120, 701–722 (1997)
Rumelhart, D.E., McClelland, J.L.: The PDP Research Group: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1. MIT Press, Cambridge (1986)
McClelland, J.L., Rumelhart, D.E.: The PDP Research Group: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Psychological and Biological Models, vol. 2. MIT Press, Cambridge (1986)
Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)
Korner, E., Gewaltig, O.M., Korner, U., Richter, A., Rodemann, T.: A model of computation in neocortical architecture. Neural Networks 12, 989–1005 (1999)
Bliss, P.V.T., Collingridge, L.G.: A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993)
Bear, F.M.: A synaptic basis for memory storage in the cerebral cortex. PNAS 93, 13453–13459 (1996)
Chen, R.W., Lee, S., Kato, K., Spencer, D.D., Shepherd, M.G., Williamson, A.: Long-term modifications of synaptic efficacy in the human inferior and middle temporal cortex. PNAS 93, 8011–8015 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, W. (2008). A Hypothesis on How the Neocortex Extracts Information for Prediction in Sequence Learning. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)