# Building analysis tools and a theory framework for inferring principles of neural computation from multi-scale organization in brain recordings

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $350,198

## Abstract

Summary
The BRAIN initiative is enabling ground-breaking techniques for brain recordings that
will permit a unique view onto the dynamics of neural activity. However, inferring brain
function from multi-channel physiological recordings is challenging. A key difficulty is
that individual neurons and mesoscopic, often rhythmic, cell populations interact in
complicated and recurrent ways. Such complex neuronal dynamics is hard to analyze but
very likely important to the functioning of the brain. This proposal will address this
problem by developing (1) tools for analyzing brain activity; (2) a theoretical framework
for expressing underlying computations and generating experimental predictions.
The starting point of the project is our earlier discovery that phase structure in
oscillatory local field potentials (LFP) of hippocampal areas CA1/CA3 carry location
information in exquisite detail (Agarwal et al. 2014). We will release software tools that
make the methods for phase decoding and extracting meaningful LFP components
available to the broader community. Further, in collaboration with experimental labs we
will research the mechanistic underpinnings of this discovery in hippocampus (Buzsaki
NYU, Foster, UC Berkeley), and explore how similar approaches can leverage phase
diversity in cortical gamma oscillations (Fries, MPI Frankfurt). The research goal is to
develop analysis tools for decoding and extraction of functional components (Aims 1 and
2), applicable to a broad range of multivariate brain recordings of hippocampal and
cortical activity.
Further, we will develop a flexible two-level theory framework with software tools (Aim
3) to help neuroscientists, in particular experimenters, to formulate putative abstract
computations underlying a brain function under study, and build a concrete mechanistic
circuit model of those computations. The computational description level will leverage
ideas of vector symbolic architectures, a class of connectionist models originally
proposed for describing cognitive reasoning (Plate, 1995; Kanerva, 1996). Models
produced by the software tool will concisely encapsulate assumptions about the
computation and its implementation of a brain function and produce predictions that
can be tested in a next generation of recording experiments. The proposed theory
framework will be tested in building models for navigation in hippocampus and for
visual processing in areas V1 and V4 in cortex.

## Key facts

- **NIH application ID:** 9999578
- **Project number:** 5R01EB026955-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Friedrich T SOMMER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $350,198
- **Award type:** 5
- **Project period:** 2018-09-21 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999578

## Citation

> US National Institutes of Health, RePORTER application 9999578, Building analysis tools and a theory framework for inferring principles of neural computation from multi-scale organization in brain recordings (5R01EB026955-03). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/9999578. Licensed CC0.

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