A leaky integrate and fire model for spike generation in a neuron with variable threshold and multiple‐input–single‐output configuration. Issue 7 (24th January 2019)

• Record Type: Journal Article
• Authors: Ganguly, Chittotosh; Chakrabarti, Saswat
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 30:Issue 7(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-24
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3561
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Comprehensive understanding of the phenomenon of spike transfer from one neuron to another in human brain is essential for developing spiking neuronal network models. Synapses play pivotal role in transferring information from one neuron to another. A leaky integrate and fire model of a representative neuron in multiple‐input and single‐output configuration with threshold variability for neuronal spike transfer process is proposed in this paper. A simple analytical expression of membrane potential including threshold variability over a small time interval is developed. The model captures several important features of a spiking neuron through a minimal set of model parameters. An activity dependent noise model is also suggested. An expression for the probability of generation of an action potential at axon hillock of the post‐synaptic neuron is derived from the model. Analytical and simulation results are provided to explain various aspects of the proposed model. Abstract : A leaky integrate and fire model of a representative neuron in MISO configuration with threshold variability for neuronal spike transfer process is proposed. A simple analytical expression of membrane potential including threshold variability is developed. An activity dependent noise model is also suggested. The model captures several important features of a spiking neuron through a minimal set of model parameters. An expression for the probability of generation of a spike at post‐synaptic neuron is derived from the model.