Synaptic background activity enhances the responsiveness of neocortical
pyramidal neurons.
Nicolas Hô and Alain Destexhe
Journal of Neurophysiology 84: 1488-1496, 2000.
Abstract
Neocortical pyramidal neurons in vivo are subject to an intense synaptic
background activity but little is known of how this activity affects cellular
responsiveness, or what function it may serve. These issues were examined in
morphologically-reconstructed neocortical pyramidal neurons in which synaptic
background activity was simulated based on recent measurements in cat parietal
cortex. We show that background activity can be decomposed into two
components: a tonically active conductance and voltage fluctuations. Previous
studies have mostly focused on the conductance effect, revealing that
background activity is responsible for a decrease in responsiveness, which
imposes severe conditions of coincidence of inputs necessary to discharge the
cell. It is shown here in contrast, that responsiveness is enhanced if
voltage fluctuations are taken into account; in this case the model can
produce responses to inputs that would normally be subthreshold. This effect
is analyzed by dissecting and comparing the different components of background
activity, as well as by evaluating the contribution of parameters such as the
dendritic morphology, the distribution of leak currents, the value of axial
resistivity, the densities of voltage-dependent currents, and the release
parameters underlying background activity. Interestingly, the model's optimal
responsiveness was obtained when voltage fluctuations were of the same order
as those measured intracellularly in vivo. Possible consequences were also
investigated at the population level, where the presence of background
activity allowed networks of pyramidal neurons to instantaneously detect
inputs that are small compared to the classical detection threshold. These
results suggest, at the single-cell level, that the presence of voltage
fluctuations has a determining influence on cellular responsiveness and that
these should be taken into account in models of background activity. At the
network level, we predict that background activity provides the necessary
drive for detecting events that would normally be undetectable. Experiments
are suggested to explore this possible functional role for background activity.
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