Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

URL:

https://ieeexplore.ieee.org/document/8125894/

Keywords:

Adaptation models, AdEx cluster, bioelectric potentials, Biological system modeling, Brain, brain circuits, cellular biophysics, computational modeling, Computational neuroscience, CPU implementation, GPGPU, GPU implementation, graphic processing uni, graphics processing units, Integrated circuit modeling, mathematical model, Medical computing, Microprocessor chips, multi compartmental modeling, multicompartmental Hodgkin-Huxley type neurons, multicompartmental spiking neuron models, neural model, neural nets, neural responses, neural simulations, neuron models, Neurons, Neurophysiology, parallel computing, parallel hardware, parallel simulation, Physiological models, point neuron models, serial hardware, single compartmental spiking neuron models, single-compartmental-adaptive-exponential-integrate-and-fire-model, spatiotemporal information processing, spiking neuron models

Abstract:

Characterizing neural responses and behavior require large scale simulation of brain circuits. Spatio-temporal information processing in large scale neural simulations often require compromises between computing resources and realistic details to be represented. In this work, we compared the implementations of point neuron models and biophysically detailed neuron models on serial and parallel hardware. GPGPU like architectures provide improved run time performance for multi compartmental Hodgkin-Huxley (HH) type neurons in a computationally cost effective manner. Single compartmental Adaptive Exponential Integrate and Fire (AdEx) model implementations, both in CPU and GPU outperformed embarrassingly parallel implementation of multi compartmental HH neurons. Run time gain of CPU implementation of AdEx cluster was approximately 10 fold compared to the GPU implementation of 10-compartmental HH neurons. GPU run time gain for Adex against GPU run time gain for HH was around 35 fold. The results suggested that careful selection of the neural model, capable enough to represent the level of details expected, is a significant parameter for large scale neural simulations.

Cite this Research Publication

Manjusha Nair, Krishnapriya Ushakumari, Athira Ramakrishnan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Comparing parallel simulation of single and multi-compartmental spiking neuron models using gpgpu”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.