title: Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals.
abstract: Extracting the input signal of a neuron by analyzing its spike output is an important step toward understanding how external information is coded into discrete events of action potentials and how this information is exchanged between different neurons in the nervous system. Most of the existing methods analyze this decoding problem in a stochastic framework and use probabilistic metrics such as maximum-likelihood method to determine the parameters of the input signal assuming a leaky and integrate-and-fire (LIF) model. In this article, the input signal of the LIF model is considered as a combination of orthogonal basis functions. The coefficients of the basis functions are found by minimizing the norm of the observed spikes and those generated by the estimated signal. This approach gives rise to the deterministic reconstruction of the input signal and results in a simple matrix identity through which the coefficients of the basis functions and therefore the neuronal stimulus can be identified. The inherent noise of the neuron is considered as an additional factor in the membrane potential and is treated as the disturbance in the reconstruction algorithm. The performance of the proposed scheme is evaluated by numerical simulations, and it is shown that input signals with different characteristics can be well recovered by this algorithm.
authors.affiliation: Department of Electrical Engineering, Shahid Bahonar University of Kerman, 22 Bahman Blvd, Kerman, 7616914111, Iran. sseydnejad@uk.ac.ir.
authors.forename: Saeid R
authors.initials: SR
authors.lastname: Seydnejad
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D006801:Humans; D016013:Likelihood Functions; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*
keywords: Interspike interval; Leaky integrate and fire; Neuronal modeling; Neuronal parameter estimation; Presynaptic current; Spiking neuron