Name
Abstract | ||
|---|---|---|
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1371/journal.pcbi.1005583 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Computational neuroscience,Population,ENCODE,Nonlinear system,Information processing,Random neural network,Computer science,Artificial intelligence,Winner-take-all,Spiking neural network | Journal | 13 |
Issue | ISSN | Citations |
6 | 1553-7358 | 5 |
PageRank | References | Authors |
0.47 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Koch Ocker | 1 | 13 | 1.33 |
| Kresimir Josić | 2 | 36 | 5.49 |
| Eric Shea-Brown | 3 | 323 | 37.92 |
| Michael Buice | 4 | 12 | 1.97 |