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.

A Decade of NeuroMorpho.Org

Poster Title: A Decade of NeuroMorpho.Org: Lessons Learned and Future Prospects
Authors: Rubén Armañanzas, Sridevi Polavaram, Sumit Nanda, Patricia Maraver, Giorgio A. Ascoli
Affiliation: Krasnow Institute for Advanced Study, GMU

NeuroMorpho.Org is the largest centrally curated online repository of digital reconstructions of axonal and dendritic morphologies.

This public resource freely provides light and electron microscopy tracings contributed by more than 220 labs worldwide from over 500 publications.

Profiles of Hippocampal Principal Neurons

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.

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

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.

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.


BigNeuron: Building consensus among automated morphological reconstructions

By Todd A. Gillette, Hanchuan Peng, Xiaoxiao Liu, Yinan Wan, Giorgio A. Ascoli

A community effort to find out what is exactly the state-of-the-art of single neuron reconstruction, standardize the protocols, and establish a Big Data resource for neuroscience.

Circuitry Profiling in the Drosophila Brain

Poster Title: Circuitry Profiling in the Drosophila Brain through Machine Learning
Authors: Rubén Armañanzas, Sumit Nanda, Giorgio A. Ascoli.

Prominent research efforts are unveiling how circuits constitute the basic functional units of nervous systems.

Among them, Drosophila is currently the species with more promising results in mapping brain-wide connections at the
individual neuron level. The pioneering FlyCircuit Database has already traced and co-registered the neurite wiring corresponding to approximately 10% (v1.0) and 23% (v1.1) of the cells in the Drosophila brain.

Digital Reconstructions of Neuronal Morphology

Poster Title: Design and Implementation of Multi-Signal, Time-Lapse Digital Reconstructions of Neuronal Morphology
Authors: Sumit Nanda, Hanbo Chen, Ravi Das, Hanchuan Peng, Daniel N. Cox, Giorgio A. Ascoli

We present the definition of a novel multichannel file structure and corresponding Vaa3D plug-in to handle this new type of data. We also introduce a design to tag dynamic structural changes in a time-coded manner. Next, we illustrate ongoing progress in using the multichannel/time-lapse system on developing neurons in the Drosophila larva. Time-varying images of overall neuronal morphology along with fluorescently labeled subcellular cytoskeletal components are digitally traced into the aforementioned file structures. These new reconstructions enable complete statistical analysis of the structural changes and the underlying molecular processes.

G Protein-binding SfN Poster

By Justin R. King1, Ming-Kuan Lin2, Nadine Kabbani

"A G protein-binding domain within the α7 nicotinic receptor enables downstream calcium signaling beyond the time course of channel activation"

α7 nicotinic acetylcholine receptors (nAChRs) play an important role in synaptic transmission via regulation of intracellular signaling pathways. In recent studies, we demonstrated an important functional role for α7 nAChR interactions with intracellular heterotrimeric GTP binding proteins (G proteins) in cell signaling.

Skip to toolbar