# Neurophysiology underlying neural representations of value

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $821,100

## Abstract

Understanding the neural mechanisms underlying decision-making is important because patients with many
psychiatric disorders make mal-adaptive decisions, impacting executive functioning, including emotion and mood
regulation. Historically, the mechanisms underlying decision-making have been most studied using behavioral
paradigms in which subjects repeatedly make decisions about well-controlled stimuli or options, invoking
perceptual, valuation, memory, or other processes. These studies have provided significant insight, but many if
not most decisions in the real world occur in very different circumstances than those realized in the laboratory.
Two paradigmatic types of such decisions are those made in novel situations never encountered before, and
those that require subjects to generate new responses to familiar stimuli, i.e. to flexibly adjust behavior in a new
way. In this grant, we test the hypothesis that mechanisms underlying these 2 forms of decision-making can be
revealed by examining the 'geometry' of neural representations and relating them to behavior in tasks invoking
the 2 types of decisions. The geometry of a representation is defined by the set of all distances between points
in the activity space that represent responses of multiple neurons in different conditions. Measures of a
representational geometry include assessment of its dimensionality. Decisions in novel situations require
generalizing from past experiences to a new one, an ability relying on abstraction. Abstraction constructs
variables describing features shared by instances within and across situations, capturing regularities and
structure in the world. Neural representations of abstracted variables are similar to the widely studied
disentangled representations in machine learning and have lower dimensionality. On the other hand, high
dimensional neural representations support the ability to generate many different responses without changing
the underlying representation. Recent data indicate that neural ensembles in the hippocampus (HPC) and
prefrontal cortex (PFC) achieve geometries with a sufficiently low dimensional ‘scaffold’ to support generalization
in new situations, but the scaffold is embedded in a higher dimensional representation of task variables. This
geometry has specific computational capabilities, but do they actually relate to decision-making? Here we
combine high-channel count electrophysiology, neural network modeling, and carefully designed tasks to provide
evidence for the first time that these 2 aspects of the geometry actually are used to support the 2 distinct types
of decision-making. We examine the geometry of representations in HPC and PFC in relation to decisions of
both types. We ask if the ability to make decisions relying on abstraction correlates with how a key task-relevant
variable is represented in a low-dimensional scaffold (Aim 1). Then we test if the representation encodes many
other variables with higher dimensionality, and if t...

## Key facts

- **NIH application ID:** 10828914
- **Project number:** 5R01MH082017-17
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Stefano Fusi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $821,100
- **Award type:** 5
- **Project period:** 2008-04-10 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828914

## Citation

> US National Institutes of Health, RePORTER application 10828914, Neurophysiology underlying neural representations of value (5R01MH082017-17). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10828914. Licensed CC0.

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