Abstract
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 language | English |
|---|---|
| Pages (from-to) | 689-695 |
| Number of pages | 7 |
| Journal | Current Opinion in Neurobiology |
| Volume | 11 |
| Issue number | 6 |
| DOIs | |
| State | Published - 1 Dec 2001 |
| Externally published | Yes |
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.
Funding
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.
| Funders | Funder number |
|---|---|
| Israeli Academy of Science | |
| US-Israel bi-national Science Foundation |