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Coarse-grained statistics for attributing criticality to heterogeneous neural networks

Authors
Journal
BMC Neuroscience
1471-2202
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
12
Identifiers
DOI: 10.1186/1471-2202-12-s1-p235
Keywords
  • Poster Presentation
Disciplines
  • Biology
  • Computer Science

Abstract

Coarse-grained statistics for attributing criticality to heterogeneous neural networks POSTER PRESENTATION Open Access Coarse-grained statistics for attributing criticality to heterogeneous neural networks Thomas Gregory Corcoran*, Andy Philippides, Thomas Nowotny From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 Multi-scale modelling has great use in computational neuroscience [1]. This typically involves a stage of coarse graining. However, the assumptions (e.g. that the sum of ensemble activities are dominated by the mean and fluctuations are Gaussian) that underlie a coarse- grained description have yet to be qualified, especially in the biological context where intrinsic diversity can con- tribute to complex activity patterns [2]. Recent work has reported on the effects that intrinsic properties can have on overall network activity [3], while parametric var- iance is proposed to have self-regulatory and tuning effects [4,5]. Here, we report on multi-scale analysis of data from large-scale neuronal network simulations which involves a coarse-graining procedure of the observed spike pat- terns. At each scale of coarse-graining, observables are calculated and the scale-dependence of summary statis- tics is determined. We observe whether the activity forms contiguous events, also known as avalanches, and measure their size. This study differs from others in that it uses coarse graining in both time and space. Our model system contains a heterogeneous popula- tion of adaptive exponential integrate-and-fire model neurons with various heterogeneities on a spatial sheet with toroidal boundary conditions. We then examine locally averaged spike rates calculated across space and time on different scales to form a hierarchical descrip- tion. We then analyse these coarse grained variables in a detrended fluctuation analysis (DFA). We find that there is an appropriate scale for intermit- tency in the coarse-grained observables. We

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