Name
Abstract | ||
|---|---|---|
It is known that, in feed-forward nets, the degree of neural correlation generally increases with firing rate. Here, we study the correlations of neurons that are part of a homogeneous global feedback network, under the influence of partially correlated external input. By using numerical simulations of a network of noisy leaky integrate-and-fire neurons with delayed and smoothed spikedriven feedback, we obtain a non-monotonic relationship between the correlation coefficient and the strength of inhibitory feedback connections. This non-monotonic relationship can be explained by the interplay between the mean rate and the regularity of firing activity caused by the inhibitory feedback connections. We also show that this non-monotonic relationship is robust in both sub-threshold and supra-threshold dynamic regimes, for low and moderate internal noise levels, as well as when the network is heterogeneous. Our results point to a potent functional role for feedback as a modulator of correlated activity in neural networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-12436-0_22 | ADVANCES IN NEURAL NETWORKS - ISNN 2014 |
Keywords | Field | DocType |
Feedback,Correlation,Oscillation,Heterogeneity | Correlation coefficient,Oscillation,Homogeneous,Modulation,Correlation,Artificial intelligence,Artificial neural network,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
8866 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinli Xie | 1 | 7 | 2.94 |
| Zhijie Wang | 2 | 89 | 11.14 |
| Jianyu Zhao | 3 | 0 | 0.68 |