Towards a Complete Description of the Circuitry Underlying Sharp Wave-Mediated Memory Replay

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U19 Project:  Modelers:  Aaron Milstein, Ivan Raikov, and Prannath Moolchand; PI:  Ivan Soltesz; Stanford; modeling hippocampal circuits. The level of biophysical detail of our models is high. We use the simulation environment NEURON. We write mostly code in python. We run our models using collective operations enabled by MPI parallelism. We are comparing alternative theories to explain sparse population codes of space during exploration, and offline memory sequence replay during sleep. Our models point to important roles for synaptic plasticity, nonlinear input summation in dendrites, and specific classes of inhibitory cells.

Soltesz U19 Hippocampal Modeling Project Summary  



  • Description of how Data Science Core is complying to the criteria of the FAIR principles:

We have publicly shared data in the DANDI archive formatted in NWB format. The NWB format ensure that the data has the necessary metadata to be reusable, and posting it publicly makes the data Accessible. We are using this dataset to help the DANDI team develop their platform around user needs, ensuring Interoperability, and to scrape the appropriate data to enable structured search so that the data is Findable.

  • List of Data Types in U19:
    • Optical physiology
      • Calcium imaging
      • Regions on interest and DF/F traces
      • Photometry
    • Behavior
      • 3D point cloud behavioral data
      • Position tracking
      • trials and decisions
    • Extracellular electrophysiology
      • Voltage traces
      • Local field potential
      • Spike times
  • Common Data Elements in U19: All groups hare tracking neuron-level activity in the hippocampus. Many of the associated analysis can be used across labs.
  • Data Sharing goals in U19:
    • ​​​​​​​Facilitate sharing within Ripple U19 using NWB
    • Publicly share datasets with the community in NWB
  • Data science tools being used in project:
  • Data science tools being developed for project:
  • Data science approaches to be shared with other U19’s:​​​​​​​
  • Data science challenges that could benefit from discussion with other U19’s:
    • ​​​​​​​How to lower the barrier of data standardization
    • How to integrate with tools that others are building
    • Provenance tracking

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