%A Kozloski,James %A Wagner,John %D 2011 %J Frontiers in Neuroinformatics %C %F %G English %K Distributed Computing,Hodgkin-Huxley,Neural Tissue,Numerical methods,Parallel Computing,simulation,Ultrascalable,Whole Brain %Q %R 10.3389/fninf.2011.00015 %W %L %M %P %7 %8 2011-September-19 %9 Methods %+ Dr James Kozloski,IBM Research,Computational Biology Center,1101 Kitchawan Rd.,T. J. Watson Research Center, Room 05-144,Yorktown Heights,10598,NY,United States,kozloski@us.ibm.com %# %! The Neural Tissue Simulator %* %< %T An Ultrascalable Solution to Large-scale Neural Tissue Simulation %U https://www.frontiersin.org/articles/10.3389/fninf.2011.00015 %V 5 %0 JOURNAL ARTICLE %@ 1662-5196 %X Neural tissue simulation extends requirements and constraints of previous neuronal and neural circuit simulation methods, creating a tissue coordinate system. We have developed a novel tissue volume decomposition, and a hybrid branched cable equation solver. The decomposition divides the simulation into regular tissue blocks and distributes them on a parallel multithreaded machine. The solver computes neurons that have been divided arbitrarily across blocks. We demonstrate thread, strong, and weak scaling of our approach on a machine with more than 4000 nodes and up to four threads per node. Scaling synapses to physiological numbers had little effect on performance, since our decomposition approach generates synapses that are almost always computed locally. The largest simulation included in our scaling results comprised 1 million neurons, 1 billion compartments, and 10 billion conductance-based synapses and gap junctions. We discuss the implications of our ultrascalable Neural Tissue Simulator, and with our results estimate requirements for a simulation at the scale of a human brain.