Description: Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

This title appears in the Scientific Report : 2018

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of...

Personal Name(s):	Maksimov, Andrei (Corresponding author)
	Diesmann, Markus / van Albada, Sacha (Corresponding author)
Contributing Institute:	Computational and Systems Neuroscience; INM-6 Theoretical Neuroscience; IAS-6 Jara-Institut Brain structure-function relationships; INM-10
Published in:	Frontiers in computational neuroscience, 12 (2018) S. 44
Imprint:	Lausanne Frontiers Research Foundation 2018
DOI:	10.3389/fncom.2018.00044
PubMed ID:	30042668
Document Type:	Journal Article
Research Program:	Supercomputing and Modelling for the Human Brain The Human Brain Project Human Brain Project Specific Grant Agreement 1 Brain-inspired multiscale computation in neuromorphic hybrid systems Theory, modelling and simulation
Link:	Get full text OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.3389/fncom.2018.00044 in citations.
Please use the identifier: http://hdl.handle.net/2128/19324 in citations.

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.