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Biologically Realistic Neocortical-Hippocampal Navigation Simulation

This project aims to simulate a system with up to 10^6 neurons in real-time, creating emotionally intelligent decision-making agents based on episodic memory. Through interdisciplinary collaboration, the goal is to develop large-scale biologically realistic models of cortical microcircuit dynamics for human-robot interaction. Accomplishments include a 100,000-cell model running real-time and the development of a GPU version of a Neural Simulator.

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Biologically Realistic Neocortical-Hippocampal Navigation Simulation

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  1. Neuroscience • Project Objectives: • Simulate a system up to 105 and 106 neurons real-time: • Neocortical-HippocampalNavigation • Create emotionally intelligent decision making agents, founded on biological episodic memory and neuromodulator-driven reward • Construct shared-memory cluster to support AI goals Large-Scale Biologically Realistic Models of Cortical Microcircuit Dynamics for Human Robot InteractionFrederick C. Harris, Jr./University of Nevada, Reno10/1/09-9/30/12 Mesocircuit Modeling Software/Hardware Engineering • Technical Approach: • Interdisciplinary (neuroscience, computer science, biomedical engineering) • Incorporate latest bench neuroscience into large-scale supercomputer simulations to extract mesocircuit dynamics and neuromodulation • Robotic instantiation of real-time human interaction and episodic memory • Implement new hybrid 64-bit CPU cluster with shared memory + GPUs • Accomplishments/Impact/Transitions: • Neocortical-Hippocampal Navigational Learning • 100,000 cell model running real-time • Development of a GPU version of our Neural Simulator [NCS] • Multi-Box/Multi-GPU • Hypothalamic Trust • Robust and functional architecture • Emotional Speech Processing • Reward Learning • Several peer-reviewed papers & presentations supported in part by this grant Robot/Human Loops

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