About this BHM Hub

This BHM, Biorealistic Hippocampal Modeling, Hub’s goal is to conceptualize a cognitive processor inspired by evolutionarily prescribed biological systems. The hippocampal formation computational component of this processor is based on biologically realistic circuitry derived from Hippocampome.org.

It is now possible to implement the Hippocampal Spiking Neural Network (HSNN) with biological analogy at the cellular circuitry level. We propose performing HSNN simulations within the operational context of a cognitive container to facilitate enhanced machine learning.

  • Robin Hanson CN3 presentation

    Dr. Robin Hanson will give a presentation to the CN3 faculty and students on his new book …
    The Age of Em“Work, Love and Life when Robots Rule the Earth”

    Time: 11am   Date: September 27, 2016
    Place: 229 Krasnow
    George Mason University
    Fairfax, Va. 22030

    Broadcast details: This onAir broadcast will be streamed live from this post as well as the BHM You Tube here.

  • BHM Digest – Sept. 2016

    Digest for September 2016 (in progress)

    Featured News:  “One Hundred Year Study on Artificial Intelligence (AI100)”
    “The Concept of ‘Cat Face”
    “IBM’s Watson Detected Rare Leukemia In Just 10 Minutes”

  • BHM Digest – Aug. 2016

    Digest for the period of August 3 to Sept. 3, 2016

    Featured Post: Brain Informatics & Web Intelligence Conferences

    Samsung turns IBM’s brain-like chip into a digital eye
    IBM scientists emulate neurons with phase-change technology
    Putting a computer in your brain is no longer science fiction
    AGI Revolution: An Inside View of the Rise of Artificial General Intelligence

  • IBM scientists emulate neurons with phase-change technology

    Scientists at IBM Research in Zurich have developed artificial neurons that emulate how neurons spike (fire). The goal is to create energy-efficient, high-speed, ultra-dense integrated neuromorphic (brain-like) technologies for applications in cognitive computing, such as unsupervised learning for detecting and analyzing patterns.

    Applications could include internet of things sensors that collect and analyze volumes of weather data for faster forecasts and detecting patterns in financial transactions, for example.

Featured Event

  • Brain Informatics & Web Intelligence Conferences

    Conference theme is “Connecting Network and Brain with Big Data”.
    October 13-16, 2016 in Omaha Nebraska, USA

    The University of Nebraska at Omaha College of Information Science & Technology will be hosting International Conference on Web Intelligence 2016 (WI ’16) and the International Conference on Brain Informatics & Health 2016 (BIH ’16).

  • Complementary Learning Systems

    Complementary Learning Systems (CLS) theory holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences.

    Included in this post is some materials from D Kumaran et al’s latest update article to CLS theory – What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated.

  • Name-calling in the hippocampus

    Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties

    In order to alleviate this nomenclature confusion regarding hippocampal neuron types and properties, we introduce a new functionality of Hippocampome.org: a fully searchable, curated catalog of human and machine-readable definitions, each linked to the corresponding neuron and property terms. Furthermore, we extend our robust approach to providing each neuron type with an informative name and unique identifier by mapping all encountered synonyms and homonyms.

    By D. J. Hamilton, D. W. Wheeler, C. M. White, C. L. Rees, A. O. Komendantov, M. Bergamino, G. A. Ascol
    Brain Informatics | June 9, 2016

  • Hippocampome Portal

    Hippocampome.org is a curated knowledge base of the circuitry of the hippocampus of normal adult, or adolescent, rodents at the mesoscopic level of neuronal types. Knowledge concerning dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex is distilled from published evidence and is continuously updated as new information becomes available.

    Each reported neuronal property is documented with a pointer to, and excerpt from, relevant published evidence, such as citation quotes or illustrations.

  • The Age of Em

    Age of Em“Work, Love and Life when Robots Rule the Earth”
    By Robin Hanson

    From Oxford University Press Overview:
    A unique look into the possible technological future of the human race

    Draws upon an unusually wide command of academic consensus and standard analytical tools across economics, engineering, computing, physical sciences, and the human and social sciences

    Encyclopedic in scope

  • Demis Hassabis AGI videos

    Dr. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world’s leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition.

    Videos in this post cover a number of issues and solutions related to Artificial General Intelligence and Computational Neuroscience.

  • David Hamilton

    Senior Software Engineer, Northrop Grumman
    Neuroscientist, PhD from George Mason University
    Dissertation title, “Machine-readable Knowledge Management of Neuron Properties.”

    Dr Hamilton has said “Neuroscience is the most interesting and potentially useful field of study available to me at this stage in my career. I was trained as an electrical engineer, worked most of my life as a software engineer, but desire to learn how the brain works to glean useful architectural aspects for continued advancement in problem solving.”

  • Computational Neuroanatomy Group

    The Computational Neuroanatomy Group, part of CN3 and the Krasnow Institute for Advanced Studies at George Mason University, is a multidisciplinary research team devoted to the study of basic neuroscience.

    We are specifically interested in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology. In the long term, we seek to create large-scale, anatomically plausible neural networks to model entire portions of a mammalian brain (such as a hippocampal slice, or a cortical column).

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