Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks

Alfonso Renart, Pengcheng Song, Xiao-Jing Wang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)473-485
Number of pages13
JournalNeuron
Volume38
Issue number3
DOIs
StatePublished - May 8 2003

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Short-Term Memory
Synapses
Head
Neurons
Spatial Memory
Direction compound

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. / Renart, Alfonso; Song, Pengcheng; Wang, Xiao-Jing.

In: Neuron, Vol. 38, No. 3, 08.05.2003, p. 473-485.

Research output: Contribution to journalArticle

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