Stepping out of the box: Information processing in the neural networks of the basal ganglia

Izhar Bar-Gad, Hagai Bergman*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

144 Scopus citations


The Albin-DeLong 'box and arrow' model has long been the accepted standard model for the basal ganglia network. However, advances in physiological and anatomical research have enabled a more detailed neural network approach. Recent computational models hold that the basal ganglia use reinforcement signals and local competitive learning rules to reduce the dimensionality of sparse cortical information. These models predict a steady-state situation with diminished efficacy of lateral inhibition and low synchronization. In this framework, Parkinson's disease can be characterized as a persistent state of negative reinforcement, inefficient dimensionality reduction, and abnormally synchronized basal ganglia activity.

Original languageAmerican English
Pages (from-to)689-695
Number of pages7
JournalCurrent Opinion in Neurobiology
Issue number6
StatePublished - 1 Dec 2001

Bibliographical note

Funding Information:
This study was supported in part by the Israeli Academy of Science and the US–Israel Bi-national Science Foundation. We thank Opher Donchin, Genela Morris and Eilon Vaadia for their critical reading and helpful suggestions. We thank Aeyal Raz, Gali Heimer, Joshua A Goldberg, Sharon Maraton, Thomas Boroud, Rony Paz, David Arkadir and Genella Morris for their physiological studies that form the basis of this manuscript.


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