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Small Scale Modeling of Cerebellar Networks Using GPUs

Publication Type : Conference Paper

Thematic Areas : Learning-Technologies, Medical Sciences, Biotech

Publisher : International Conference on Biotechnology for innovative applications, Amrita Vishwa Vidyapeetham, Kerala.

Source : International Conference on Biotechnology for innovative applications, Amrita Vishwa Vidyapeetham, Kerala, 2013.

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, Computational Neuroscience Laboratory, biotechnology

Verified : Yes

Year : 2013

Abstract : Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Cite this Research Publication : Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Small Scale Modeling of Cerebellar Networks Using GPUs”, in International Conference on Biotechnology for innovative applications, Amrita Vishwa Vidyapeetham, Kerala, 2013.

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