| Abstract No.: | 208 |
| Country: | Canada |
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| Title: | INHIBITORY NETWORK MODELING |
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| Authors/Affiliations: | 1 Frances K. Skinner*;
1 Toronto Western Research Institute, ON, Canada
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| Content: | Inhibitory networks are known to be critical controllers of several population rhythms that are associated with normal and pathological brain states. However, how best to understand network dynamics from a cellular-based perspective is difficult given their highly nonlinear natures. While it is clear that nonlinearity prevents purely experimental approaches from being enough to provide us with an understanding, it is not clear how computational and modeling efforts can best fill the gap. Mathematical models incorporating various levels of detail are used for a variety of reasons (such as availability of experimental data, computational ease and analytical possibilities), and brain dynamics need to be considered in some well-defined context but this is often not possible.
In this talk I will present three ideas that represent an objective approach in working toward cellular-based mechanistic understandings of inhibitory networks. These ideas are relevant in the context of spike synchrony and heterogeneity. In the first idea we suggest using maximal heterogeneities as a way to obtain optimal parameter values in conductance-based models. In the second idea, we show and thus suggest that small network mechanisms can be mapped onto larger networks to provide insight and further constraints. Finally, in the third idea we show how extracting inhibitory/excitatory conductance balances can be used to link to mathematical network models.
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