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| Research article summary (published 6 May 2003): |
Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks.
Full Abstract
The concept of bell-shaped persistent neural activity represents a cornerstone of the theory for the internal representation of analog quantities, such as spatial location or head direction. Previous models, however, relied on the unrealistic assumption of network homogeneity. We investigate this issue in a network model where fine tuning of parameters is destroyed by heterogeneities in cellular and synaptic properties. Heterogeneities result in the loss of stored spatial information in a few seconds. Accurate encoding is recovered when a homeostatic mechanism scales the excitatory synapses to each cell to compensate for the heterogeneity in cellular excitability and synaptic inputs. Moreover, the more realistic model produces a wide diversity of tuning curves, as commonly observed in recordings from prefrontal neurons. We conclude that recurrent attractor networks in conjunction with appropriate homeostatic mechanisms provide a robust, biologically plausible theoretical framework for understanding the neural circuit basis of spatial working memory.
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Author information
Author/s: Renart, Alfonso (A); Song, Pengcheng (P); Wang, Xiao-Jing (XJ);
Affiliation: Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
Grants: DA016455 (Agency:United States NIDA) ; MH62349 (Agency:United States NIMH)
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.
Journal: Neuron (Neuron), published in United States. (Language: eng)
Reference: 2003-May; vol 38 (issue 3) : pp 473-85
Dates: Created 2003/05/13; Completed 2003/06/10; Revised 2007/11/14;
PMID: 12741993, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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