# Neural signatures of learning complex environments in the amygdala-prefrontal network

> **NIH NIH K99** · UNIVERSITY OF PENNSYLVANIA · 2020 · $91,800

## Abstract

The ability to learn and think about complex situations is central to a range of human cognitive
functions, including navigation, reasoning, and decision making. Numerous theories across these
domains rely on representations of states of these external and internal environments, but how
they acquire such representations remains unknown. My overall goal is to understand how
animals, including humans, can reason and learn in such complex environments. In this project,
we propose to investigate how animals are able to learn these representations in a complex
sequential decision making task in monkeys. Using a novel behavioral task inspired by the board
game battleship, monkeys search for hidden shapes on a screen. There are millions of possible
shapes, and yet monkeys are capable learners, vastly outperforming classic reinforcement
learning algorithms. How monkeys can learn the shapes so quickly remains mysterious. In
addition to these unknown computational foundations for learning, the neural mechanisms that
support this behavior are also unexplored. Recent studies including electrophysiology and lesion
research have found signatures of state representations in the amygdala (AMYG) and the
orbitofrontal cortex (OFC). However, these studies have only used very few states that only
require associations to learn. Moreover, the interactions and computational roles of the regions
have not been characterized. In light of these gaps in our understanding of learning in complex
tasks, we will use the battleship task to elucidate 1) the aspects of the environment that drive
learning representations of complex states, 2) the computational foundations of this learning
using behavioral model fitting and deep neural networks, and 3) the neural mechanisms that
underwrite this capacity in the AMYG-OFC circuit. We hypothesize that OFC represents hidden
task states, those that cannot be fully defined in terms of perceptible stimuli and outcomes. We
further hypothesize that AMYG plays a central role in learning and updating these representations
by constructing an online representation of the current environment using input from OFC as well
as from sensory processing and memory regions, representing current stimuli, outcomes, and
associations. We posit an observer-critic architecture underlies learning representations of
complex tasks, with AMYG activity computing and sending a teaching signal to OFC that learns
and updates task state representations. As part of this planned research, I will be trained in
advanced modeling and neural analysis techniques, and complete a course of study on the use of
deep neural networks. This training will take place under the guidance of Dr. Stefano Fusi and Dr.
C Daniel Salzman in the Zuckerman Mind Brain Behavior Institute at Columbia University.

## Key facts

- **NIH application ID:** 10249424
- **Project number:** 7K99DA048748-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** David Barack
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $91,800
- **Award type:** 7
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249424

## Citation

> US National Institutes of Health, RePORTER application 10249424, Neural signatures of learning complex environments in the amygdala-prefrontal network (7K99DA048748-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10249424. Licensed CC0.

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