Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653527 | Neurocomputing | 2005 | 7 Pages |
Abstract
Our goal in the present study is to analyze and explain through computational techniques the effect of increased EPSP depression after learning. We apply three different models to simulate the exact reduction in the AHP amplitude: (1) ''Conductance change:'' Controlled by decreasing gkCa by 40 %. (2) ''Moving:'' Shifting the location of the dendritic segment that exhibits active conductances, including AHP conductance, distally from the soma, while decreasing gkCa by only 15%. (3) ''Shrinkage:'' Decreasing the length of the AHP dendritic segment, while increasing gkCa by 9%. Moving the synaptic input distally from the soma enhances EPSP depression by the AHP conductance. Hence, the learning process could be simulated by a “jump” from the control curve to any other curve, representing decreased AHP amplitude. At the same time, the enhanced EPSP depression requires an additional shift of the EPSP input to more distal locations.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
G. Gradwohl, Y. Grossman,