1 research outputs found
Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise
Spontaneous cortical population activity exhibits a multitude of oscillatory
patterns, which often display synchrony during slow-wave sleep or under certain
anesthetics and stay asynchronous during quiet wakefulness. The mechanisms
behind these cortical states and transitions among them are not completely
understood. Here we study spontaneous population activity patterns in random
networks of spiking neurons of mixed types modeled by Izhikevich equations.
Neurons are coupled by conductance-based synapses subject to synaptic noise. We
localize the population activity patterns on the parameter diagram spanned by
the relative inhibitory synaptic strength and the magnitude of synaptic noise.
In absence of noise, networks display transient activity patterns, either
oscillatory or at constant level. The effect of noise is to turn transient
patterns into persistent ones: for weak noise, all activity patterns are
asynchronous non-oscillatory independently of synaptic strengths; for stronger
noise, patterns have oscillatory and synchrony characteristics that depend on
the relative inhibitory synaptic strength. In the region of parameter space
where inhibitory synaptic strength exceeds the excitatory synaptic strength and
for moderate noise magnitudes networks feature intermittent switches between
oscillatory and quiescent states with characteristics similar to those of
synchronous and asynchronous cortical states, respectively. We explain these
oscillatory and quiescent patterns by combining a phenomenological global
description of the network state with local descriptions of individual neurons
in their partial phase spaces. Our results point to a bridge from events at the
molecular scale of synapses to the cellular scale of individual neurons to the
collective scale of neuronal populations.Comment: 30 pages, 19 figure