title: Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model.
abstract: The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation.
authors.affiliation: UTSA Neurosciences Institute, The University of Texas at San Antonio, San Antonio, Texas, United States of America.
authors.forename: Wondimu
authors.initials: W
authors.lastname: Teka
authors.affiliation: UTSA Neurosciences Institute, The University of Texas at San Antonio, San Antonio, Texas, United States of America.
authors.forename: Toma M
authors.initials: TM
authors.lastname: Marinov
authors.affiliation: UTSA Neurosciences Institute, The University of Texas at San Antonio, San Antonio, Texas, United States of America.
authors.forename: Fidel
authors.initials: F
authors.lastname: Santamaria
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001703:Biophysics; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D008564:Membrane Potentials; D008568:Memory; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D017966:Pyramidal Cells / Q000166:cytology; D051381:Rats