The mechanisms for synchrony and for stability of persistent
activity in large neuronal networks
David Hansel1 and German Mato2
1Laboratoire de Neurophysique et Physiologie du Systeme Moteur
2Comision Nacional de Energia Atomica and CONICE
We study how synchrony emerges in highly connected
heterogeneous networks consisting of two interacting
populations of integrate-and-fire or conductance based neurons.
The populations are one excitatory (E) and one inhibitory (I).
We find that three generic mechanisms exist:
1) Synchrony of spikes through inhibition in the I population.
2) Synchrony of spikes through the mutual interactions
between the two populations.
3) Synchrony of network bursts through recurrent excitation.
These mechanisms can be controlled by choosing the strength and time
constants of the interactions between the populations,
by heterogeneities and noise.
For a large class of integrate-and-fire networks
this was shown by computing analytically the regions
in parameter space where the different mechanisms occur.
For conductance-based neuronal models, we have relied on numerical
The cellular and synaptic properties underlying
stable persistent state (PS) of activity in neuronal
circuits have been debated recently in the context of models of
working memories. Using our analytical approach
we have derived conditions for the stability of PS.
We have shown that PS with level of activity of the order of 5-20 Hz
can be stable in a broad range of parameters of a two population
network of neurons interacting with AMPA and GABA synapses,
provided the level of heterogeneities in the intrinsic
properties of neurons is sufficient and the inhibitory
interactions between the inhibitory neurons are sufficiently strong.
This is in contrast with previous theoretical studies
which have suggested that saturating NMDA synapses are
required to stabilize cortical persistent activity with
physiologically realistic firing rates.