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Publication Type : Conference Paper
Thematic Areas : Learning-Technologies, Medical Sciences, Biotech
Publisher : IEEE
Source : Proceedings of the Sixth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2017), Manipal University, Karnataka, India, Sept 13-16, 2017.
Keywords : Biological system modeling, biology computing, Brain, Brain modeling, cerebellum, computational modeling, data abstractions, data analysis, Data reduction, data visualisation, Data visualization, Dimensionality reduction, Information theoretic visualization, Information theory, large scale cerebellar network, large scale simulation, mathematical model, neural nets, Neurons, Neurophysiology, scientific data analysis, small scale cerebellar networks, Spiking Neural Networks, spiking neuron models, spiking neuron simulation environment, time varying volume visualization, Visual analysis, Visualization
Campus : Amritapuri
School : School of Biotechnology, Department of Computer Science and Engineering, School of Engineering
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology
Department : Computer Science, biotechnology, Sciences
Year : 2017
Abstract : Visualization is a flexible way to analyze simulated data and serves as a means for scientific discovery. Large scale neural simulations using high performance and distributed computing techniques produce huge amount of data for which visual analysis is generally difficult to perform. In this paper, a spiking neuron simulation environment was created to model and simulate networks of neurons of the cerebellum. Traditional visualization techniques were used to highlight relevant findings from small scale cerebellar networks. Time varying volume visualization using traditional techniques was found infeasible as network size increased. New data abstractions were required to depict the data that changes over time. With large scale cerebellar networks, Information theoretic methods were used to reduce dimensionality and to extract valuable information from data. We suggested that, information theory can be used as an efficient scientific data analysis and visualization tool to evaluate and validate computational models of cerebellar like structures.
Cite this Research Publication : Manjusha Nair, Akshaya Puthenpeed Suresh, Anjana Manoharan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Information theoretic visualization of spiking neural networks”, in Proceedings of the Sixth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2017), Manipal University, Karnataka, India, Sept 13-16, 2017.