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Spatial Coding in a Full-Scale Computational Model of the Hippocampus Ivan Raikov, Aaron Milstein, Grace Ng, Ivan Soltesz Department of Neurosurgery, Stanford University Blue Waters Symposium 2018 06/04/18 1 Information Coding in the


  1. Spatial Coding in a Full-Scale Computational Model of the Hippocampus Ivan Raikov, Aaron Milstein, Grace Ng, Ivan Soltesz Department of Neurosurgery, Stanford University Blue Waters Symposium 2018 06/04/18 1

  2. Information Coding in the Hippocampus Knierim, 2012 06/04/18 2

  3. T owards a Complete Description of the Circuitry Underlying Sharp Wave-Mediated Memory Replay Colgin, 2017. 06/04/18 3

  4. Understanding Neural Systems 1. Computational level : What is the task? What logic is used to solve it? 2. Algorithmic level : How is relevant information represented and manipulated to perform the task? 3. Implementational level : How does hardware implement the algorithms? Marr, D.; Poggio, T. (1976). 06/04/18 4

  5. Understanding Neural Systems Modeling hippocampal Cell and circuit-level parameter optjmizatjon neuroanatomy and connectjvity structure Investjgatjng hippocampal spatjal and temporal coding at scale 06/04/18 5

  6. Modeling the Hippocampus at Full Scale Hippocampus of rat in 3D brain volume 3D distribution of 1,000,000 granule cells Unique synthetic Axonal distributions granule cell morphology 06/04/18 6

  7. Modeling the Hippocampus at Full Scale ~1.2M neurons ~18B synapses 06/04/18 7

  8. Simulation of hippocampal spatial navigation 06/04/18 8

  9. Spatial coding of full-scale hippocampal simulation on Blue Waters GC Hz 5 s physical time / 2048 BW nodes for 3 hrs 06/04/18 9

  10. Nested parallel multi-objective optimization https://github.com/neurosutras/nested 06/04/18 10

  11. Realistic synaptic mechanisms compatible with full-scale simulations Saturating AMPA-R + Facilitating NMDA-R -- Blue Waters can simulate full-scale networks with unique synapses, but this is an ineffjcient use of resources -- new synapse mechanism implementation combines biophysical realism with computational effjciency 06/04/18 11

  12. Synaptic weight distributions Buzsáki & Mizuseki Nat. Rev. Neurosci., 2014 06/04/18 12

  13. A scalable framework for parallel IO and analysis of full-scale neuronal network structure 06/04/18 13

  14. A scalable framework for generation, simulation, and analysis of full-scale neuronal network structure 06/04/18 14

  15. Thank You! Project funded by BRAIN grant 1U19NS104590 06/04/18 15

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