03964nas a2200589 4500008004100000245014300041210006900184260003300253520204000286653002202326653006902348653000902417653003102426653001002457653001902467653002402486653003102510653002402541653002302565653001502588653004102603653002702644653003102671653003502702653002602737653000802763653002902771653003002800653001402830653004202844653003802886653002302924653002002947653003002967653000802997653002903005653002603034653001603060653002903076653001203105653001303117653002703130653002303157653002803180653002003208100001903228700001603247700002203263700002003285700001903305856005003324 2015 eng d00aEfficient simulations of spiking neurons on parallel and distributed platforms: Towards large-scale modeling in computational neuroscience0 aEfficient simulations of spiking neurons on parallel and distrib aTrivandrum, IndiabIEEEcDec3 aHuman 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.
10aAdaptation models10aadaptive exponential leaky integrate and fire mathematical model10aAdEx10aBiological system modeling10aBrain10aBrain modeling10acerebellar circuits10aCerebellar granule neurons10acerebellar networks10acerebellar neurons10acerebellum10acerebellum granular layer simulation10acomputational modeling10aComputational neuroscience10adistributed computing approach10adistributed platforms10aGPU10agraphic processing units10agraphics processing units10aHH neuron10ahigh performance computing techniques10aHodgkin-Huxley mathematical model10amathematical model10aMessage passing10amessage passing interface10aMPI10amulticompartmental model10amultiGPU architecture10aneural nets10aneuronal firing patterns10aNeurons10aparallel10aParallel architectures10aparallel platforms10aphysiological disorders10aspiking neurons1 aNair, Manjusha1 aSurya, Shan1 aKumar, Revathy, S1 aNair, Bipin, G.1 aDiwakar, Shyam uhttps://ieeexplore.ieee.org/document/7488425/