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  A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”.

Engelken, R., Farkhooi, F., Hansel, D., van Vreeswijk, C., & Wolf, F. (2016). A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”. Faculty of 1000 Research, 5: 2043. doi:10.12688/f1000research.9144.1.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-ED4A-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-2620-F
Genre: Journal Article

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https://f1000research.com/articles/5-2043/v1 (Publisher version)
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Engelken, R., Author
Farkhooi, F., Author
Hansel, D., Author
van Vreeswijk, C., Author
Wolf, Fred1, Author              
Affiliations:
1Research Group Theoretical Neurophysics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063289              

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 Abstract: Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

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Language(s): eng - English
 Dates: 2016-08-22
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.12688/f1000research.9144.1
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Title: Faculty of 1000 Research
Source Genre: Journal
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Pages: 10 Volume / Issue: 5 Sequence Number: 2043 Start / End Page: - Identifier: -