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


Data Reuse Abstract

Our U19 group studies the cellular bases of memory consolidation, particularly how sharp wave ripples serve as a transfer mechanism between hippocampus and neocortex. We use cutting-edge large-scale electrophysiology and optophysiological recording technologies to study and manipulate identified cell types in behaving animals, coupled with data-driven simulations.  Our goal is to elucidate the cellular mechanisms responsible for memory replay and its role in memory transfer and consolidation. The methods used by our group can be applied to many situations in which brain mechanisms of behavior and cognition are explored.

Our data spans several neurophysiological techniques that provide insights into the mechanisms sharp wave ripples:
- Large-scale electrophysiological recordings of freely moving mice and rats in various learning and navigational tasks. 
- Behavioral measures span from classic position-and-heading-direction measures to a high-resolution continuous monitoring of the entire movement repertoire of the animal
- Calcium imaging data and extraction of sharp wave ripples from the optical signal.
- Novel fiber photometry and voltage imaging techniques that provide unprecedented information about cell types-specific contributions to population cooperativity.

We are particularly interested in application of analytical tools that could help determine specific cellular connectivity and contributions to the sharp wave ripple processes, and can assist our large-scale computational model building. Related analytical topics could include:
- Tuning properties of hippocampal cells in various tasks
- Examination and reliability of sharp wave ripples extracted from calcium imaging data
- Novel analysis or visualization techniques applied to the simulated neural activity output by our large-scale computational model

Link to Data/Model Reuse abstract, [Link] 


Data harmonization

One of the goals of our project is sharing the tools coming out of your data science core with the other U19 projects to promote harmonization across the entire BRAIN Initiative. One of the ways we have done this is have the tool builders from this U19 give in-depth breakout sessions during the NWB user training events. Biafra from the Schnitzer Lab presented on ciapkg. Peter Petersen from the Buzsaki Lab gave a similar presentation on CellExplorer. These in-depth breakout sessions are recorded and available on the NWB youtube channel. This has proven to be a powerful mechanism to disseminate information. You will see from that link that Biafra’s talk has 464 views. Our plan for the future will be to integrate these tools directly on DANDI Hub so that they will be freely available in the cloud for anyone to run on published NWB data.



2021 Brain PI Meeting


Link to Poster:



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