This title appears in the Scientific Report :
2017
Please use the identifier:
http://dx.doi.org/10.1186/s12868-017-0371-2 in citations.
Please use the identifier: http://hdl.handle.net/2128/16048 in citations.
Dynamics of cell assemblies in binary neuronal networks
Dynamics of cell assemblies in binary neuronal networks
Connectivity in local cortical networks is far from random: Not only are reciprocal connections over-represented [1], but there are also larger subgroups of neurons which are stronger connected among each other than to the remainder of the network [2,3]. These observations provide a growing evidence...
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Personal Name(s): | Keup, Christian (First author) |
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Kühn, Tobias / Helias, Moritz (Corresponding author) | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | 2017 |
Imprint: |
2017
|
DOI: |
10.1186/s12868-017-0371-2 |
Conference: | 26th Annual Computational Neuroscience Meeting, Antwerp (Belgium), 2017-07-15 - 2017-07-20 |
Document Type: |
Abstract |
Research Program: |
Theory of multi-scale neuronal networks Theory, modelling and simulation |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/16048 in citations.
Connectivity in local cortical networks is far from random: Not only are reciprocal connections over-represented [1], but there are also larger subgroups of neurons which are stronger connected among each other than to the remainder of the network [2,3]. These observations provide a growing evidence for the existence of neuronal assemblies, that is groups of neurons with stronger and/or more numerous connections between members compared to non-members. To study quantitatively the dynamics of these building blocks, we consider a single assembly of binary neurons embedded in a larger randomly connected EI-network and explore its properties by analytical methods and simulation. Extending [4] to the three population case, we obtain expressions for mean activities, auto- and cross-correlations, and response to input fluctuations using a Gaussian closure. For sufficiently strong assembly self-feedback, this mean-field theory predicts a bifurcation from a mono-stable to a bistable regime. The critical regime around the bifurcation is of interest, as input variations can drive the assembly to high or low activity states and large spontaneous fluctuations are present. These could be a source of neuronal avalanches observed in cortex [5] and the robust response to input could constitute attractor states as in [6]. In this regime however, the approximation is not accurate (Figure 1) due to large fluctuation corrections. We therefore work on a path-integral formulation of such systems built on developments in the application of statistical field theory to neuronal networks [7]. This formulation allows the derivation of an effective potential, a systematic treatment of approximations and the quantification of the response to inputs. |