DOI: 10.5061/dryad.dz08kps72
title: Closed-loop modulation of remote hippocampal representations with neurofeedback

description: Humans can remember specific remote events without acting on them and
 influence which memories are retrieved based on internal goals. However,
 animal models typically present sensory cues to trigger memory retrieval
 and then assess retrieval based on action. Thus, it is difficult to
 determine whether measured neural activity patterns relate to the cue(s),
 the memory, or the behavior. We therefore asked whether retrieval-related
 neural activity could be generated in animals without cues or a behavioral
 report. We focused on hippocampal “place cells” which primarily represent
 the animal’s current location (local representations) but can also
 represent locations away from the animal (remote representations). We
 developed a neurofeedback system to reward expression of remote
 representations and found that rats could learn to generate specific
 spatial representations that often jumped directly to the
 experimenter-defined target location. Thus, animals can deliberately
 engage remote representations, enabling direct study of retrieval-related
 activity in the brain.
description: Collected via tetrode recordings from hippocampal CA1 in wild
 type long evans rats. Raw neurophysiology data was decoded with an
 clusterless realtime decoding algorithm (see Coulter et al 2024 and Chu et
 al 2024).
description: # Closed-loop modulation of remote hippocampal representations with
 neurofeedback
 [https://doi.org/10.5061/dryad.dz08kps72](https://doi.org/10.5061/dryad.dz08kps72) ## Description of the data and file structure Realtime spatial decoding results from hippocampal CA1 extracellular recordings in Long Evans rats while they performed a neurofeedback task that rewarded the generation of remote spatial hippocampal representations. Each file is the spatial decoding results from one recording session. File naming convention is: [date + time] [rat name] [recording session].rec_merged.h5. The data within each file includes: hippocampal spiking (encoder-data), rat's position (occupancy-data), remote representation (event) detection times (event-lockout and event-message), and hippocampal spatial representation (deocder-data). This data is contained within several dataframes that can be open using this code snippet (python): ``` # load real-time recording file import os import pandas as pd file_name = '20220317_103945_20220317_arthur_02_r1.rec_merged.h5' os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" # extract data into individual dataframes decoder-data = pd.read_hdf(file_name, key="rec_4") occupancy-data = pd.read_hdf(file_name, key="rec_7") encoder-data = pd.read_hdf(file_name, key="rec_3") event-lockout = pd.read_hdf(file_name, key="rec_11") event-message = pd.read_hdf(file_name, key="rec_12") ``` One file ("coulter_all_figures_zip.tar.gz") contains the processed data use to generate plots in the accompanying manuscript: Coulter et al, bioRxiv, 2024. These plots can be created using the jupyter notebook titled: *"*single_cell_summary_plots-share-v3.ipynb" at https://github.com/LorenFrankLab/hippocampus_content_neurofeedback.