Name
Title | ||
|---|---|---|
Geometric analysis of neuronal firing patterns in network models with fast inhibitory synapses |
Abstract | ||
|---|---|---|
We demonstrate how geometric dynamical systems techniques can yield insight into network behavior in models of coupled neurons. Such an approach is useful for understanding general mechanisms by which firing patterns, such as synchrony and clustering, arise and for computing how cells’ intrinsic and synaptic properties shape network behavior. We focus here on biophysical models, based on the Hodgkin–Huxley formalism, relevant to thalamic activity during sleep and paroxysmal discharges and especially on the role of fast inhibitory coupling in synchronous oscillations. A key finding is that qualitative differences in synchronization mechanisms appear in models with different complexity levels. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0925-2312(99)00039-9 | Neurocomputing |
Keywords | Field | DocType |
Synchronization,Oscillations,Inhibition,Singular perturbation | Synapse,Synchronization,Geometric analysis,Inhibitory postsynaptic potential,Dynamical systems theory,Artificial intelligence,Cluster analysis,Network behavior,Machine learning,Mathematics,Network model | Journal |
Volume | ISSN | Citations |
26-27 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan E. Rubin | 1 | 235 | 31.34 |
| David Terman | 2 | 0 | 0.34 |