Abstract
Background: Distributed Delay Framework (DDF) has suggested a mechanism to incorporate
the delay factor in the evolution of the membrane potential of a neuron model in terms of
distributed delay kernel functions. Incorporation of delay in neural networks provide comparatively
more efficient output. Depending on the parameter of investigation, there exist a number of choices
of delay kernel function for a neuron model.
Objective: We investigate the Leaky integrate-and-fire (LIF) neuron model in DDF with hypoexponential
delay kernel. LIF neuron with hypo-exponential distributed delay (LIFH) model is
capable to regenerate almost all possible empirically observed spiking patterns.
Methods: In this article, we perform the detailed analytical and simulation based study of the LIFH
model. We compute the explicit expressions for the membrane potential and its first two moment
viz. mean and variance, in analytical study. Temporal information processing functionality of the
LIFH model is investigated during simulation based study.
Results: We find that the LIFH model is capable to reproduce unimodal, bimodal and multimodal
inter-spike- interval distributions which are qualitatively similar with the experimentally observed
ISI distributions.
Conclusion: We also notice the neurotransmitter imbalance situation, where a noisy neuron exhibits
long tail behavior in aforementioned ISI distributions which can be characterized by power law
behavior.
Keywords:
Fokker planck equation, hypo-exponential distribution, ISI distribution, LIF neuron, laplace transform, multimodal
distribution, power law.
Graphical Abstract
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