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J Neurophysiol. 2020 Feb 12. doi: 10.1152/jn.00402.2019. [Epub ahead of print]

Progressive Alignment of Inhibitory and Excitatory Delay May Drive a Rapid Developmental Switch in Cortical Network Dynamics.

Author information

1
ENS, France.
2
Pharmacology and Physiology, The George Washington University, United States.
3
Mathematics, Brandeis University, France.
4
Ecole Normale Superieure (Paris), France.

Abstract

Nervous system maturation occurs on multiple levels -- synaptic, circuit, and network -- at divergent time scales. For example, many synaptic properties mature gradually, while emergent network dynamics can change abruptly. Here, we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous and sensory evoked thalamocortical activity necessary for the development of vision. Inspired by in vivo measurements of time-scales and amplitudes of synaptic currents, we extend the Wilson and Cowan model to take into account the relative onset timing and amplitudes of inhibitory and excitatory neural population responses. We study this system as these parameters are varied within amplitudes and timescales consistent with developmental observations to identify the bifurcations of the dynamics that might explain the network behaviors in vivo. Our findings indicate that the inhibitory timing is a critical determinant of thalamocortical activity maturation; a gradual decay of the ratio of inhibitory to excitatory onset time drives the system through a bifurcation that leads to a sudden switch of the network spontaneous activity from high-amplitude oscillations to a non-oscillatory active state. This switch also drives a change from a threshold bursting to linear response to transient stimuli, also consistent with in vivo observation. Thus we show that inhibitory timing is likely critical to the development of network dynamics and may underlie rapid changes in activity without similarly rapid changes in the underlying synaptic and cellular parameters.

KEYWORDS:

Cortex; Development; Mean fields; Neural Networks

PMID:
32049596
DOI:
10.1152/jn.00402.2019

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