# Mapping Algorithmic State Space in the Human Brain

> **NIH NIH U01** · BAYLOR COLLEGE OF MEDICINE · 2021 · $1,509,141

## Abstract

Abstract
Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this
cognitive flexibility is our ability to respond correctly to novel contextual situations on the first attempt, without
prior rehearsal. We refer to this ability as ‘ad hoc self-programming’: ‘ad hoc’ because these new behavioral
repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer
necessary; ‘self-programming’ because the brain has to configure itself appropriately based on task demands
and some combination of prior experience and/or instruction. The overall goal of our research effort is to
understand the neurophysiological and computational basis for ad hoc self-programmed behavior.
 Our previous U01 project (NS 108923) focused on how these programs of action are initially created. Our
results thus far have revealed tantalizing notions of how the brain represents these programs and navigates
through them. In this proposal, therefore, we focus on the question of how these mental programs are
executed. Based on our preliminary findings and critical conceptual work, we propose that the medial temporal
lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental
programs, including concepts such as ‘rules’ and ‘locations’, to allow for effective navigation through the
algorithm.
 These data suggest the existence of an ‘algorithmic state space’ represented in medial temporal and
prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic
state space in humans. By studying humans, we will profit from our species’ powerful capacity for generalization
to understand how such state spaces are constructed. We therefore leverage the unique opportunities available
in human neuroscience research to record from single cells and population-level signals, as well as to use
intracranial stimulation for causal testing, to address this challenging problem.
 In Aim 1 we study the basic representations of algorithmic state space using a novel behavioral task that
requires the immediate formation of unique plans of action. Aim 2 directly compares representations of
algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic
tasks in a virtual reality (VR) environment. Finally, in Aim 3, we test hypotheses regarding interactions between
vPFC and MTL using intracranial stimulation.

## Key facts

- **NIH application ID:** 10199622
- **Project number:** 1U01NS121472-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Sameer Anil Sheth
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,509,141
- **Award type:** 1
- **Project period:** 2021-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10199622

## Citation

> US National Institutes of Health, RePORTER application 10199622, Mapping Algorithmic State Space in the Human Brain (1U01NS121472-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10199622. Licensed CC0.

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