# Integrating and separating information sequences in the human cerebral cortex

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $518,417

## Abstract

Project Summary
Many aspects of cognition extend over time. Hearing a fragment of sound, we perceive it as part of a
mockingbird’s melody; hearing one word, we understand it as part of a meaningful sentence. Therefore, our
brains must possess the ability to integrate information over time. However, information cannot be integrated
indiscriminately: the subject of a new sentence is not necessarily related to the verb of the previous sentence,
and we may want to keep these items of information separate. The goal of this work is to understand the
algorithms by which our brains flexibly integrate related information while separating unrelated information. In
addition, we aim to understand how temporal integration and separation are implemented in cortical circuits, and
how we can manipulate these brain processes. Our prior work suggests that brains perform this task in a
distributed manner: almost all regions of the human cortex can integrate information over time. Early sensory
regions integrate over short periods (milliseconds to seconds) and they pass information to higher-order regions,
which integrate over longer periods (seconds to minutes). We propose the following algorithm for this process:
each cortical region maintains its own local memory, and attempts to form a synthesized joint representation of
its local memory and any input that it receives. When this synthesis is successful, a cortical region will pass
forward its synthesized representation to the next stage of processing. But if the synthesis is unsuccessful, then
its local memory will be reset. For example, an early cortical region may synthesize syllables within a word but
then reset its context at the beginning of a new word. To test whether the brain is using this algorithm, we will
model fMRI activity in the brains of people who integrate and separate sequences of information as they listen
to complex narratives. To understand how temporal integration and separation are implemented in the activity
of cortical circuits, we will also measure ECoG signals, which provide a direct read-out of synchronous and
asynchronous activity in cortical neurons. We hypothesize that cortical circuits become less synchronized when
they cannot integrate new input with prior context. We will test whether this decrease in low-frequency
synchronization allows increased information flow from the world into the cortical hierarchy. Finally, we will
develop tools to control the state of temporal integration or separation, using electrical modulation to increase or
decrease synchronization in cortical circuits. Altogether, this work provides tools to detect and manipulate
temporal integration and separation processes in the human brain, as well as a computational framework for
how we parse sequences of information. Our approach is innovative because we develop multi-scale
experimental paradigms and combine data across fMRI, ECoG, and modeling. We expect this work to help reveal
the hierarchical mechanisms...

## Key facts

- **NIH application ID:** 10131031
- **Project number:** 5R01MH119099-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Christopher John Honey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $518,417
- **Award type:** 5
- **Project period:** 2019-04-02 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10131031

## Citation

> US National Institutes of Health, RePORTER application 10131031, Integrating and separating information sequences in the human cerebral cortex (5R01MH119099-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10131031. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*