Self-sustained asynchronous irregular states and Up/Down states
in thalamic, cortical and thalamocortical networks of nonlinear
integrate-and-fire neurons.
Alain Destexhe
Journal of Computational Neuroscience 27: 493-506, 2009.
Abstract
Randomly-connected networks of integrate-and-fire (IF) neurons are
known to display asynchronous irregular (AI) activity states, which
resemble the discharge activity recorded in the cerebral cortex of
awake animals. However, it is not clear whether such activity states
are specific to simple IF models, or if they also exist in networks
where neurons are endowed with complex intrinsic properties similar
to electrophysiological measurements. Here, we investigate the
occurrence of AI states in networks of nonlinear IF neurons, such as
the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This
model can display intrinsic properties such as low-threshold spike
(LTS), regular spiking (RS) or fast-spiking (FS). We successively
investigate the oscillatory and AI dynamics of thalamic, cortical and
thalamocortical networks using such models. AI states can be found in
each case, sometimes with surprisingly small network size of the
order of a few tens of neurons. We show that the presence of LTS
neurons in cortex or in thalamus, explains the robust emergence of AI
states for relatively small network sizes. Finally, we investigate
the role of spike-frequency adaptation (SFA). In cortical networks
with strong SFA in RS cells, the AI state is transient, but when SFA
is reduced, AI states can be self-sustained for long times. In
thalamocortical networks, AI states are found when the cortex is
itself in an AI state, but with strong SFA, the thalamocortical
network displays Up and Down state transitions, similar to
intracellular recordings during slow-wave sleep or anesthesia.
Self-sustained Up and Down states could also be generated by
two-layer cortical networks with LTS cells. These models suggest that
intrinsic properties such as adaptation and low-threshold bursting
activity are crucial for the genesis and control of AI states in
thalamocortical networks.
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