--- abstract: 'A neural network model is proposed that forms sparse spatiotemporal memory traces of spatiotemporal events given single occurrences of the events. The traces are distributed in that each individual cell and synapse participates in numerous traces. This sharing of representational substrate provides the basis for similarity-based generalization and thus semantic memory. Simulation results are provided demonstrating that similar spatiotemporal patterns map to similar traces. The model achieves this property by measuring the degree of match, G, between the current input pattern on each time slice and the expected input given the preceding time slices (i.e., temporal context) and then adding an amount of noise, inversely proportional to G, to the process of choosing the internal representation for the current time slice. Thus, if G is small, indicating novelty, we add much noise and the resulting internal representation of the current input pattern has low overlap with any preexisting representations of time slices. If G is large, indicating a familiar event, we add very little noise resulting in reactivation of all or most of the preexisting representation of the input pattern.' altloc: - http://home.comcast.net/~rinkus/CogSci2004Rev1.pdf chapter: ~ commentary: ~ commref: ~ confdates: Aug. 5-7 conference: 26th Annual Meeting of the Cognitive Science Society confloc: 'Chicago, IL, USA' contact_email: ~ creators_id: [] creators_name: - family: Rinkus given: Gerard J. honourific: '' lineage: '' date: 2004 date_type: published datestamp: 2004-04-28 department: ~ dir: disk0/00/00/35/80 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: - family: Forbus given: Kenneth D. honourific: '' lineage: '' - family: Gentner given: Dedre honourific: '' lineage: '' - family: Regier given: Terry honourific: '' lineage: '' eprint_status: archive eprintid: 3580 fileinfo: /style/images/fileicons/application_pdf.png;/3580/1/CogSci2004Rev1.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: inpress issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: '"neural network" spatiotemporal "sequence memory" "episodic memory" connectionist' lastmod: 2011-03-11 08:55:31 latitude: ~ longitude: ~ metadata_visibility: show note: 'This paper has been accepted for publication in the proceedings. ' number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: 'Lawrence Erlbaum, Associates' refereed: TRUE referencetext: | Ans, B., Rousset, S., French, R.M., & Musca, S. (2002) Preventing Catastrophic Interference in Multiple-Sequence Learning Using Coupled Reverberating Elman Networks. Proc. of the 24th Annual Conf. of the Cognitive Science Society. LEA, NJ. Carpenter, G. & Grossberg, S. (1987) Massively parallel architectures for a self-organizing neural pattern recognition machine. Computer Vision, Graphics and Image Processing. 37, 54-115. Coultrip, R. L. & Granger, R. H. (1994) Sparse random networks with LTP learning rules approximate Bayes classifiers via Parzen’s method. Neural Networks, 7(3), 463-476. Lynch, G. (1986) Synapses, Circuits, and the Beginnings of Memory. The MIT Press, Cambridge, MA. McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457. Moll, M. & Miikkulainen, R. (1995) Convergence-Zone Episodic Memory: Analysis and Simulations. Tech. Report AI95-227. The University of Texas at Austin, Dept. of Computer Sciences. O'Reilly, R. C. & Rudy, J. W. (1999) Conjunctive Representations in Learning and Memory: Principles of Cortical and Hippocampal Function. TR 99-01. Institute of Cognitive Science, U. of Colorado, Boulder, CO Palm, G. (1980) On Associative Memory. Biological Cybernetics, 36. 19-31. Rinkus, G. J. (1995) TEMECOR: An Associative, Spatiotemporal Pattern Memory for Complex State Sequences. Proc. of the 1995 World Congress on Neural Networks. LEA and INNS Press. 442-448. Rinkus, G. J. (1996) A Combinatorial Neural Network Exhibiting both Episodic Memory and Generalization for Spatio-Temporal Patterns. Ph.D. Thesis, Graduate School of Arts and Sciences, Boston University. Willshaw, D., Buneman, O., & Longuet-Higgins, H. (1969) Non-holographic associative memory. Nature, 222, 960-962 relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:51:48 subjects: - comp-sci-neural-nets - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: A Neural Model of Episodic and Semantic Spatiotemporal Memory type: confpaper userid: 4757 volume: ~