Name
Abstract | ||
|---|---|---|
Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1, indicating a tendency toward spikes being clustered. We show that this behavior emerges naturally in a balanced cortical network model with random connectivity and conductance-based synapses. We employ mean-field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high-conductance states of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1. (C) 2004 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.neucom.2004.01.149 | NEUROCOMPUTING |
Keywords | Field | DocType |
synaptic conductances,response variability,cortical dynamics | Synapse,Premovement neuronal activity,Colors of noise,Mean field theory,Artificial intelligence,Fano plane,Poisson distribution,Conductance,Machine learning,Mathematics,Network model | Journal |
Volume | Issue | ISSN |
58 | 3 | 0925-2312 |
Citations | PageRank | References |
4 | 0.93 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Lerchner | 1 | 15 | 2.14 |
| Mandana Ahmadi | 2 | 15 | 1.80 |
| J Hertz | 3 | 18 | 3.74 |