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- G. Wenning, P. Kallerhoff,
T. Hoch, and K. Obermayer. Detection of Pulses by Integrate-and-fire
Neurons in a Colored Noise Setting.
.
In Soc. Neurosci. Abstr. 30, 2004.
CD-ROM.
Cortical neurons are exposed to a considerable amount of synaptic
background activity, which increases the neurons? conductance and which leads
to a fluctuating membrane potential that on average is close to the
threshold. Here we investigate, how the presence and the properties of this
background noise affects the ability of a neuron to detect transient inputs,
a task which is important for coincidence detection as well as for the
detection of synchronous spiking events in a neural system. Using a leaky
integrate-and-fire (LIF) as well as a biologically more realistic
Hodgkin-Huxley (HH) type point neuron we find that noise enhances the
detection of subthreshold input pulses and that the phenomenon of stochastic
resonance occurs. When the noise is colored, pulse detection becomes more
robust, because the number of false positive detection events decreases with
increasing temporal correlations while the number of correctly detected
events is almost unaffected. Therefore, the optimal variance of the noise
also changes with the degree of temporal correlations of the background
activity. For the LIF model these effects can be explained using a simple
Gaussian assumption for the distribution of the neuron?s membrane potential,
and numerical simulations show that the LIF and HH neurons behave
qualitatively similar. Since background noise leads to fluctuations of the
membrane potential which are below its average value for approximately half
of the time, the fraction of correctly detected subthreshold pulses may never
become better than approximately 50 percent for a single neuron. We therefore
report on the results of simulations employing populations of LIF neurons,
which receive common signal input and independent additive
noise.
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