| Title |
Emergent dynamics in a model of visual cortex
|
|---|---|
| Published in |
Journal of Computational Neuroscience, March 2013
|
| DOI | 10.1007/s10827-013-0445-9 |
| Pubmed ID | |
| Authors |
Aaditya V. Rangan, Lai-Sang Young |
| Abstract |
This paper proposes that the network dynamics of the mammalian visual cortex are highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call 'multiple-firing events' (MFEs). Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1. When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including spontaneous background patterns, orientation-specific responses, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. It suggests that along with firing rates, MFE characteristics can be a powerful signature of a regime. Testable predictions based on model observations and dynamical analysis are proposed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 4% |
| United States | 2 | 4% |
| Japan | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 44 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 24% |
| Student > Master | 11 | 22% |
| Researcher | 9 | 18% |
| Student > Bachelor | 6 | 12% |
| Professor > Associate Professor | 4 | 8% |
| Other | 7 | 14% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 10 | 20% |
| Agricultural and Biological Sciences | 10 | 20% |
| Mathematics | 8 | 16% |
| Engineering | 7 | 14% |
| Physics and Astronomy | 4 | 8% |
| Other | 9 | 18% |
| Unknown | 2 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2013.
All research outputs
#18,333,600
of 22,703,044 outputs
Outputs from Journal of Computational Neuroscience
#222
of 306 outputs
Outputs of similar age
#149,915
of 197,452 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 3 outputs
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