Publication Type:

Conference Paper

Source:

2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), IEEE, Trivandrum, India (2015)

URL:

https://ieeexplore.ieee.org/document/7488425/

Keywords:

Adaptation models, adaptive exponential leaky integrate and fire mathematical model, AdEx, Biological system modeling, Brain, Brain modeling, cerebellar circuits, Cerebellar granule neurons, cerebellar networks, cerebellar neurons, cerebellum, cerebellum granular layer simulation, computational modeling, Computational neuroscience, distributed computing approach, distributed platforms, GPU, graphic processing units, graphics processing units, HH neuron, high performance computing techniques, Hodgkin-Huxley mathematical model, mathematical model, Message passing, message passing interface, MPI, multicompartmental model, multiGPU architecture, neural nets, neuronal firing patterns, Neurons, parallel, Parallel architectures, parallel platforms, physiological disorders, spiking neurons

Abstract:

Human brain communicates information by means of electro-chemical reactions and processes it in a parallel, distributed manner. Computational models of neurons at different levels of details are used in order to make predictions for physiological dysfunctions. Advances in the field of brain simulations and brain computer interfaces have increased the complexity of this modeling process. With a focus to build large-scale detailed networks, we used high performance computing techniques to model and simulate the granular layer of the cerebellum. Neuronal firing patterns of cerebellar granule neurons were modeled using two mathematical models Hodgkin-Huxley (HH) and Adaptive Exponential Leaky Integrate and Fire (AdEx). The performance efficiency of these modeled neurons was tested against a detailed multi-compartmental model of the granule cell. We compared different schemes suitable for large scale simulations of cerebellar networks. Large networks of neurons were constructed and simulated. Graphic Processing Units (GPU) was employed in the pleasantly parallel implementation while Message Passing Interface (MPI) was used in the distributed computing approach. This allowed to explore constraints of different parallel architectures and to efficiently load balance the tasks by maximally utilizing the available resources. For small scale networks, the observed absolute speedup was 6X in an MPI based approach with 32 processors while GPUs gave 10X performance gain compared to a single CPU implementation. In large networks, GPUs gave approximately 5X performance gain in processing time compared to the MPI implementation. The results enabled us to choose parallelization schemes suitable for large-scale simulations of cerebellar circuits. We are currently extending the network model based on large scale simulations evaluated in this paper and using a hybrid - heterogeneous MPI based multi-GPU architecture for incorporating millions of cerebellar neurons for assessing physiolo- ical disorders in such circuits.

Cite this Research Publication

Manjusha Nair, Shan Surya, Revathy S Kumar, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Efficient simulations of spiking neurons on parallel and distributed platforms: Towards large-scale modeling in computational neuroscience”, in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, 2015.