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SUMMARY:Relation between firing statistics of spiking neuron with delayed
feedback and without feedback
DTSTART;VALUE=DATE-TIME:20181204T102500Z
DTEND;VALUE=DATE-TIME:20181204T104500Z
DTSTAMP;VALUE=DATE-TIME:20230330T001251Z
UID:indico-contribution-30@indico.bitp.kiev.ua
DESCRIPTION:Speakers: Olha Shchur (Bogolyubov Institute for Theoretical Ph
ysics of the National Academy of Sciences of Ukraine)\nWe consider a class
of spiking neuronal models with threshold 2\, defined by a set of conditi
ons typical for basic threshold-type models\, such as the leaky integrate-
and-fire or the binding neuron model and also for some artificial neurons.
A neuron is fed with a Poisson process. Each output impulse is applied to
the neuron itself after a finite delay $\\Delta$. This impulse is identi
cal to those delivered from the input stream. We derive a general relation
which allows calculating exactly the probability density function (pdf) $
p(t)$ of output interspike intervals of a neuron with feedback based on kn
own pdf $p^0(t)$ for the same neuron without feedback\, intensity of the i
nput stream and the properties of the feedback line (the $\\Delta$ value)
.\n In addition to this\, we calculate exactly the model-independent ini
tial segment of pdf $p(t)$ for a neuron with feedback that is the same for
any neuron satisfying the imposed conditions. Also\, relations between mo
ments of pdf $p(t)$ for a neuron with feedback and pdf $p^0(t)$ for the s
ame neuron without feedback are derived. The obtained expressions are chec
ked numerically by means of Monte Carlo simulation.\nThe course of $p(t)$
has a $\\delta$-function peculiarity\, which makes it impossible to approx
imate $p(t)$ by Poisson or another simple stochastic process.\n\nhttps://i
ndico.bitp.kiev.ua/event/2/contributions/30/
LOCATION:
URL:https://indico.bitp.kiev.ua/event/2/contributions/30/
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