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Publication Type : Conference Paper
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Kochi, India .
Source : Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Kochi, India (2015)
Url : http://www.scopus.com/record/display.uri?eid=2-s2.0-84946218667&origin=resultslist&sort=plf-f&src=s&st1=Amrita+vishwa+vidyapeetham&nlo=&nlr=&nls=&sid=0701DC8BF90C317371F6497CCF9B9702.WlW7NKKC52nnQNxjqAQrlA%3a7990&sot=b&sdt=cl&cluster=scopubyr%2c%222015%22(link is external).
Campus : Amritapuri
School : School of Biotechnology
Center : Computational Neuroscience and Neurophysiology, Amrita Mind Brain Center, Biotechnology
Department : Computational Neuroscience Laboratory, biotechnology
Year : 2015
Abstract : Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which is critical for maintaining posture and balance. Purkinje cell (PC) plays a prominent role in this fine-tuning through association of inputs and output alongside learning through error correction. Several classical studies showed that PC follows perceptron like behavior, which can be used to develop cerebellum like neural circuits to address the association and learning. With respect to the input, the PC learns the motor movement through update of synaptic weights. In order to understand how cerebellar circuits associate spiking information during learning, we developed a spiking neural network using adaptive exponential integrate and fire neuron model (AdEx) based on cerebellar molecular layer perceptron-like architecture and estimated the maximal storage capacity at parallel fiber-PC synapse. In this study, we explored information storage in cerebellar microcircuits using this abstraction. Our simulations suggest that perceptron mimicking PC behavior was capable of learning the output through modification via finite precision algorithm. The study evaluates the pattern processing in cerebellar Purkinje neurons via a mathematical model estimating the storage capacity based on input patterns and indicates the role of sparse encoding of granular layer neurons in such circuits.
Cite this Research Publication : Asha Vijayan, Chaitanya Medini, Anjana Palolithazhe, Bhagyalakshmi Muralidharan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Modeling Pattern Abstraction in Cerebellum and Estimation of Optimal Storage Capacity”, in Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Aug 10-13, 2015, Kochi, India.