Profiles of Hippocampal Principal Neurons

Poster Title: Large-scale genetic profiles of hippocampal principal neurons through Allen Brain Atlas mining
Authors: David J. Hamilton, Charise M. White, Christopher L. Rees, Diek W. Wheeler, Giorgio A. Ascoli

The massive mouse brain gene expression analysis conducted by the Allen Institute provides a wealth of data that when appropriately interpreted can be leveraged to substantially augment the biomarker knowledge in Hippocampome.org.

Biorealistic Hippocampal Modeling

Poster Title: Biologically realistic spiking model of hippocampal circuitry for enhanced machine learning
Authors: David J. Hamilton, Diek W. Wheeler, Siva Venkadesh, Keivan Moradi, Christopher L. Rees, Giorgio A. Ascoli

The field of machine learning, which historically has utilized multilayered artificial neural networks and, more recently, "Deep Learning" to characterize large datasets, will benefit from exploiting specific organizational and functional principles garnered from hippocampal circuitry. Using knowledge available in Hippocampome.org, it is now possible to construct spiking neural network (SNN) models of the rodent hippocampus.

Hippocampome.org v1.1 and beyond

Post Tittle: Hippocampome.org v1.1 and beyond: increasing open-access knowledge of neuron types and their properties for the rodent hippocampus
Authors: Diek W. Wheeler, Charise White, Alexander O. Komendantov, Christopher L. Rees, David J. Hamilton, Siva Venkadesh, Keivan Moradi, Giorgio A. Ascoli

Hippocampome.org was officially launched in 2015. Releases since then and slated for the coming year constitute multidimensional expansions of this knowledge base: Neuron Term Portal (available), Clickable connectivity matrix (available), Firing patterns, Molecular biomarker inferences, Izhikevich modeling parameters, Allen Mouse Brain Atlas data addition, and New neuron types

Models capturing hippocampal neuronal behaviors

Poster Title: Computationally efficient models capturing diverse hippocampal neuronal behaviors
Authors: Siva Venkadesh, Alexander O. Komendantov, David J. Hamilton, Diek W. Wheeler, Stanislav Listopad2, Jeffrey L. Krichmar3, Giorgio A. Ascoli1

Hippocampome.org is a knowledge base of neuron types in the rodent hippocampal formation  upon which we aim to build a full-scale model of the hippocampus. Simulation costs of biophysically detailed Hodgkin-Huxley-type neuronal models often impose limits on the scale of biologically realistic network models. Therefore, our immediate modeling goal is to create computationally efficient models that quantitatively reproduce various firing-pattern features of hippocampal neuron types.

Hippocampome Portal

The Hippocampome 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.

BHM Project

The BHM - Biorealistic Hippocampal Modeling - project'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. This highly curated open-access resource defines rodent hippocampal neuron types primarily based on axonal-dendritic patterning and is informed through dense coverage of peer-reviewed literature.

  • Profiles of Hippocampal Principal Neurons

    Poster Title: Large-scale genetic profiles of hippocampal principal neurons through Allen Brain Atlas mining
    Authors: David J. Hamilton, Charise M. White, Christopher L. Rees, Diek W. Wheeler, Giorgio A. Ascoli

    The massive mouse brain gene expression analysis conducted by the Allen Institute provides a wealth of data that when appropriately interpreted can be leveraged to substantially augment the biomarker knowledge in Hippocampome.org.

  • Biorealistic Hippocampal Modeling

    Poster Title: Biologically realistic spiking model of hippocampal circuitry for enhanced machine learning
    Authors: David J. Hamilton, Diek W. Wheeler, Siva Venkadesh, Keivan Moradi, Christopher L. Rees, Giorgio A. Ascoli

    The field of machine learning, which historically has utilized multilayered artificial neural networks and, more recently, “Deep Learning” to characterize large datasets, will benefit from exploiting specific organizational and functional principles garnered from hippocampal circuitry. Using knowledge available in Hippocampome.org, it is now possible to construct spiking neural network (SNN) models of the rodent hippocampus.

  • Hippocampome.org v1.1 and beyond

    Post Tittle: Hippocampome.org v1.1 and beyond: increasing open-access knowledge of neuron types and their properties for the rodent hippocampus
    Authors: Diek W. Wheeler, Charise White, Alexander O. Komendantov, Christopher L. Rees, David J. Hamilton, Siva Venkadesh, Keivan Moradi, Giorgio A. Ascoli

    Hippocampome.org was officially launched in 2015. Releases since then and slated for the coming year constitute multidimensional expansions of this knowledge base: Neuron Term Portal (available), Clickable connectivity matrix (available), Firing patterns, Molecular biomarker inferences, Izhikevich modeling parameters, Allen Mouse Brain Atlas data addition, and New neuron types

  • Models capturing hippocampal neuronal behaviors

    Poster Title: Computationally efficient models capturing diverse hippocampal neuronal behaviors
    Authors: Siva Venkadesh, Alexander O. Komendantov, David J. Hamilton, Diek W. Wheeler, Stanislav Listopad2, Jeffrey L. Krichmar3, Giorgio A. Ascoli1

    Hippocampome.org is a knowledge base of neuron types in the rodent hippocampal formation  upon which we aim to build a full-scale model of the hippocampus. Simulation costs of biophysically detailed Hodgkin-Huxley-type neuronal models often impose limits on the scale of biologically realistic network models. Therefore, our immediate modeling goal is to create computationally efficient models that quantitatively reproduce various firing-pattern features of hippocampal neuron types.

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