03549nas a2200637 4500008004100000245010400041210006900145260002000214520156500234653001001799653002301809653003101832653002601863653002201889653003101911653001201942653001101954653001001965653001901975653002501994653002202019653003502041653003002076653002702106653003102133653003702164653003702201653002102238653001902259653001902278653002902297653002902326653002302355653001702378653002002395653001602415653001202431653001702443653002402460653001702484653003002501653002902531653003002560653002502590653004002615653003502655653002702690653002202717100001902739700002002758700001902778700002502797700002002822700001902842856005002861 2015 eng d00aGPGPU implementation of information theoretic algorithms for the analysis of granular layer neurons0 aGPGPU implementation of information theoretic algorithms for the aIEEEbIEEEcDec3 aMethods originally developed for communication systems are widely used in computational neuroscience to understand the information representation and processing performed by neurons and neural circuits in the brain. Information theoretic quantities Entropy and Mutual Information (MI) have been used in neuroscience as a metric to estimate the efficiency of information representation by neurons. These quantities are used here to measure the stimulus discrimination reliability of the cerebellar granule neurons using simulated response trains produced by a multi-compartmental model of Wistar rat neuron. With 1011 granule neurons in the cerebellum, understanding spatio-temporal processing in such structures demands efficient, fast algorithms. Since the serial version of the algorithm had multiple estimation loops which increased the process time considerably with the problem size, we re-implemented the MI algorithm in GPGPU hardware as an efficient way of parallelizing the MI computations. Task-level parallelism and GPU optimizations were used to improve the process time. Estimates on GPGPUs showed 15X time efficiency compared to the CPU version of the algorithm. In order to understand learning inside the cerebellar circuit, synaptic plasticity conditions were simulated in the neuron model. We were able to quantify the stimulus discrimination reliability of granule neurons under control, LTP and LTD conditions and the analysis revealed that stimulus discrimination capability of the neuron was increased during high plasticity state.
10aBrain10acerebellar circuit10aCerebellar granule neurons10aCommunication systems10aComplexity theory10aComputational neuroscience10aentropy10aFiring10aGPGPU10aGPGPU hardware10aGPGPU implementation10aGPU optimizations10agranular layer neuron analysis10agraphics processing units10aInformation processing10ainformation representation10ainformation theoretic algorithms10ainformation theoretic quantities10aInstruction Sets10aLTD conditions10aLTP conditions10aMI computation algorithm10amulticompartmental model10amutual information10aneural chips10aneural circuits10aneural nets10aNeurons10aneuroscience10aParallel processing10aprocess time10asimulated response trains10aspatiotemporal phenomena10aspatiotemporal processing10aspike train analysis10astimulus discrimination reliability10asynaptic plasticity conditions10atask-level parallelism10aWistar rat neuron1 aNair, Manjusha1 aMadhu, Prasanth1 aMohan, Vyshnav1 aRajendran, Arathi, G1 aNair, Bipin, G.1 aDiwakar, Shyam uhttps://ieeexplore.ieee.org/document/7411162/