# Circuit Dynamics for encoding and remembering sequence of events

> **NIH NIH K99** · UNIVERSITY OF WASHINGTON · 2020 · $96,235

## Abstract

We experience the world as a continuous sequence of events, but we remember the events as segmented
episodes (e.g., my sister’s wedding). During encoding, we associate a sequence of relevant events and segment
deviant events. At retrieval, episodic memory utilizes the encoded associations to replay the flow of events. The
encoded associations lead to remembering the sequence of events that occurred within an episode better than
the flow of events across segments. The hippocampus and the prefrontal cortices (PFC) are essential parts of
the neural circuit for segmenting, linking, and retrieving memories of associated events. This proposal aims to
identify neural dynamics in the hippocampus-PFC circuit that support encoding a naturalistic flow of events, i.e.,
sequences of words.
We will determine these neural dynamics using intracranial encephalography (iEEG) acquired from the
hippocampus and PFC of epileptic patients, who have electrodes implemented for pre-surgical seizure
monitoring. I will model associations of words using Natural Language Processing algorithms, and I will combine
the extracted features with advanced data analysis techniques including multivariate pattern analysis to
determine neural dynamics engaged during encoding. I will use speech as a model with an identical flow of
events in the speech stimuli across participants. This consistency will allow validation of effects across a group
of participants. Algorithms for identifying features of speech are well developed and freely available. I will
specifically use elements of speech that distinguish context, word dependencies, and reference points of
pronouns for modeling concurrent changes in patterns of activity in the local field potential recorded from the
hippocampus and PFC. The central hypotheses are that bidirectional communications between the hippocampus
and PFC support the encoding of sequences of events and successful subsequent memory. To address a causal
relationship between hippocampal function and event segmentation, I will study speech comprehension and
speech memory in developmental amnesic patients who suffer from hippocampal damage and have trouble
tracking reference points in a speech.
To achieve the proposal’s goals, I will pursue training under the mentorship of Dr. Elizabeth Buffalo (University
of Washington) that will focus on the advanced analysis of local field potentials. The advanced study of human
iEEG data will include comparable electrophysiology signal analyses that have been applied to the recordings
from the hippocampus of non-human primates in Buffalo’s memory lab. This skill-set along with ongoing
mentoring from Dr. Robert Knight (University of California, Berkeley), who has an established laboratory for
human iEEG, and my previous work on human iEEG will provide a vigorous methodological, conceptual, and
analytical basis for developing an independent research program.
The combination of iEEG, Natural Language Processing modeling, and patients’ b...

## Key facts

- **NIH application ID:** 9894860
- **Project number:** 5K99MH120048-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Anna Jafarpour
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $96,235
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9894860

## Citation

> US National Institutes of Health, RePORTER application 9894860, Circuit Dynamics for encoding and remembering sequence of events (5K99MH120048-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9894860. Licensed CC0.

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