# Biologically Plausible Computational Models of Perirhinal Cortex

> **NIH NIH K00** · UNIVERSITY OF CALIFORNIA BERKELEY · 2024 · $78,646

## Abstract

PROJECT SUMMARY
Humans and other animals are able to seamlessly integrate sensory and mnemonic information. As a medial
temporal lobe structure at the apex of high-level sensory cortices, perirhinal cortex (PRC) is ideally situated to
support perceptual-mnemonic integration. Indeed, PRC has been shown to play a causal role in diverse
behaviors, including familiarity-based recognition, visual object perception, and fear conditioning. Yet this
confluence of functions has proven difficult to formalize, and there is considerable debate over the mechanisms
that enable PRC to play a role in these diverse behaviors. By integrating traditional neuroscientific methods
within a novel deep learning computational framework, this proposal aims to formalize and evaluate
computational theories of PRC function. I have three specific aims:
Aim 1. My graduate work has already laid the foundation to formalize PRC function. By integrating lesion,
electrophysiological, and behavioral data within a deep learning framework, this work resolves decades of
seemingly inconsistent experimental findings surrounding PRC involvement in visual object perception. More
specifically, I find that a biologically plausible computational proxy for the primate ventral visual stream (VVS)
approximates the visual discrimination behaviors of PRC-lesioned (human and non-human) primates, directly
from experimental stimuli. Conversely, PRC-intact participants are able to outperform PRC-lesioned behaviors,
as well as these computational proxies for the VVS—a finding that implicates PRC in these behaviors.
Aim 2. The work proposed during the F99 phase will build upon my previous work to include a computational
account of PRC-dependent visual discrimination behaviors. First, I will develop biologically plausible
computational models of PRC-intact visual behaviors able to achieve the performance of PRC-intact human
participants on concurrent visual discrimination tasks. Then, I will identify which of these PRC-models best fit in-
vivo (fMRI) measurements of PRC function. This will provide extensive training in building deep learning models
of visual behaviors, alongside neuroimaging expertise, harnessing resources available at my graduate institution.
Aim 3. Work proposed in the K00 phase will build upon these perceptual models to include PRC's known
mnemonic functions. I intend to gain extensive experience with reinforcement learning (RL) models of mnemonic
behaviors. By integrating deep learning and reinforcement learning within a biologically plausible computational
framework, I will build towards an integrated model of PRC-dependent perceptual-mnemonic behaviors.
Collectively, this proposal offers a novel framework for characterizing typical and atypical perceptual-mnemonic
behaviors, promising new insights into the neurobiology of perception and memory, while outlining the training I
need to be an independent researcher at the forefront of computational cognitive neuroscience. It is my hope...

## Key facts

- **NIH application ID:** 10794905
- **Project number:** 8K00EY037496-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Tyler Bonnen
- **Activity code:** K00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $78,646
- **Award type:** 8
- **Project period:** 2022-06-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10794905

## Citation

> US National Institutes of Health, RePORTER application 10794905, Biologically Plausible Computational Models of Perirhinal Cortex (8K00EY037496-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10794905. Licensed CC0.

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