Clustered arrangement of inhibitory neurons can lead to oscillatory dynamics in a model of activity-dependent structural plasticity

Barnard, Rosanna C, Kiss, Istvan Z, Farmer, Simon F and Berthouze, Luc (2017) Clustered arrangement of inhibitory neurons can lead to oscillatory dynamics in a model of activity-dependent structural plasticity. In: van Ooyen, Arjen and Butz-Ostendorf, Markus (eds.) The rewiring brain: a computational approach to structural plasticity in the adult brain. Oxford: Academic Press, pp. 123-154. ISBN 9780128037843

[img] PDF - Published Version
Restricted to SRO admin only

Download (5MB)

Abstract

The balance between excitation and inhibition in a neuronal network is considered to be an important determinant of neural excitability. Various processes are thought to maintain this balance across a range of stimuli/conditions. However, the developmental formation of this balance remains an open question, especially regarding the interplay between network blue-print (the spatial arrangement of excitatory and inhibitory neurons) and homeostatic processes. In this chapter, we use a published model of activity-dependent growth to show that the ratio between the number of excitatory and inhibitory neurons (E/I ratio) alone cannot accurately predict system behaviour but rather it is the combination of this ratio and the underlying spatial arrangement of neurons that determine both activity in, and structure of, the resulting network. In particular, we highlight the role of spatial clustering of inhibitory neurons. We develop measures that allow us to characterise the relationship between this spatial clustering and system behaviour in both the 1-dimensional (1D) and 2-dimensional (2D) forms of the model. Our results reveal that, for a given E/I ratio, networks with high levels of inhibitory spatial clustering are more likely to experience oscillatory behaviour of their connectivity and electrical activity than networks with lower levels of clustering. This instability of the network structure can be thought of as a pathological outcome. We discuss implications these results may have on future modelling studies in this field and speculate about their relationship to neuro-developmental physiology and pathophysiology.

Item Type: Book Section
Schools and Departments: School of Engineering and Informatics > Informatics
School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Sussex Neuroscience
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry
Related URLs:
Depositing User: Luc Berthouze
Date Deposited: 08 Aug 2017 10:15
Last Modified: 08 Aug 2017 10:15
URI: http://sro.sussex.ac.uk/id/eprint/69672

View download statistics for this item

📧 Request an update