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Evolutionary algorithm search for network connectivities conducive to periodic behavior at sub-spiking frequencies

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We use an evolutionary algorithm (EA) to search the space of leaky integrate-and-fire (LIF) neuron networks, in order to identify network connectivities producing significant rhythmic activity at sub-spiking frequencies (i.e., 'bursting-like behavior'). We find that the connectivities of the most-fit LIF networks exhibit a relatively broad in-degree distribution and a relatively narrow out-degree distribution. We examine the frequencies of connection motifs in the most-fit LIF networks as compared to random networks. In a network of more realistically modeled neurons, the most-fit network connectivities are observed to produce a broader frequency response as compared to that resulting from random network connectivities.

Figure 1 shows the cumulative in- and out-degree distributions of a representative most-fit network, whose connectivity graph is illustrated in the inset. The orange line represents the cumulative in-degree distribution, and the red line the cumulative out-degree distribution. The blue bars represent the expected cumulative distribution (and error bars) for random network connectivities.

Figure 1
figure1

shows the cumulative in- and out-degree distributions of a representative most-fit network, whose connectivity graph is illustrated in the inset. The orange line represents the cumulative in-degree distribution, and the red line the cumulative out-degree distribution. The blue bars represent the expected cumulative distribution (and error bars) for random network connectivities.

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Correspondence to Daniel T Robb.

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Robb, D.T., Toporikova, N. Evolutionary algorithm search for network connectivities conducive to periodic behavior at sub-spiking frequencies. BMC Neurosci 14, P393 (2013) doi:10.1186/1471-2202-14-S1-P393

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Keywords

  • Animal Model
  • Frequency Response
  • Algorithm Search
  • Evolutionary Algorithm
  • Cumulative Distribution