Title |
A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks
|
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Published in |
PLoS Computational Biology, October 2013
|
DOI | 10.1371/journal.pcbi.1003301 |
Pubmed ID | |
Authors |
Evan S. Schaffer, Srdjan Ostojic, L. F. Abbott |
Abstract |
Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 6 | 4% |
Switzerland | 2 | 1% |
United Kingdom | 2 | 1% |
United States | 2 | 1% |
Italy | 1 | <1% |
Japan | 1 | <1% |
France | 1 | <1% |
Unknown | 145 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 50 | 31% |
Researcher | 33 | 21% |
Student > Master | 13 | 8% |
Professor > Associate Professor | 11 | 7% |
Professor | 9 | 6% |
Other | 29 | 18% |
Unknown | 15 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 43 | 27% |
Agricultural and Biological Sciences | 33 | 21% |
Physics and Astronomy | 29 | 18% |
Engineering | 12 | 8% |
Computer Science | 12 | 8% |
Other | 16 | 10% |
Unknown | 15 | 9% |