Publication Type:

Conference Paper

Source:

Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, ACM, Amrita University, Coimbatore, India (2014)

ISBN:

9781450329088

URL:

http://doi.acm.org/10.1145/2660859.2660961

Keywords:

Computational neuroscience, Integrate and Fire Model

Abstract:

Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing patterns. We were able to reconstruct the firing dynamics of various types of cerebellar neurons and validated with previously published experimental studies. To model the neurons, we exploited particle swarm optimization to fit the parameters. The study showcases the match of electroresponsiveness of the neuronal models to data from biological neurons. Results suggest that models are close reconstructions of the biological data since frequency and spike-timing closely matched known values and were similar to those in previously published detailed computationally intensive biophysical models. Such spiking models have a number of applications including design of large-scale circuit models in order to understand physiological dysfunction and for various computational advantages.

Cite this Research Publication

C. Medini, Vijayan, A., D'Angelo, E., Bipin G. Nair Dr., and Dr. Diwakar, S., “Computationally Efficient Bio-realistic Reconstructions of Cerebellar Neuron Spiking Patterns”, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, Amrita University, Coimbatore, India, 2014.