DOI: 10.5281/zenodo.15800087
title: Beyond vision: Response of the mouse visual cortex to multimodal stimulation

description: Beyond vision: Response of the mouse visual cortex to multimodal stimulation

Description of the data and file structure

This dataset contains raw neurophysiological recordings from C57BL/6J mice (V1 region: monocular and binocular zones) in response to three types of sensory stimulation: visual, auditory, and somatosensory (air puff). Signals were recorded in vivo using a custom setup in awake, head-fixed mice. The files are organized by animal ID and recording session, and each session includes data from multiple stimulation trials.

Recordings were acquired using the Spike2 software and are saved in .smr format. Each file includes at least 200 seconds of continuous LFP data. Stimulus events are indicated in the DigMark channel, while the raw electrophysiological activity (LFP and, in selected sessions, single-/multi-unit activity) is present in the ECKHORN channel. Filtering settings were modified in some sessions to isolate spike activity. The data are unprocessed to reduce storage demands (total size exceeds 23 GB) and allow flexible reuse.

These recordings support the analysis of multisensory integration and early sensory convergence in primary visual cortex (V1). For full interpretation, we recommend opening the .smr files with Spike2 software (Cambridge Electronic Design).

Files and variables

File: Data EJN.zip

Description:

This compressed archive contains all the raw electrophysiological recordings used in the study. The structure is organized as follows:

Data EJN/

Folders named after animal IDs (VO8, V013, V016, V017, V022, V023, V024, V025, V026), each representing a single experimental subject.

Inside each mouse folder are session folders, named by date in DDMMYY format (e.g., 020922, 050922...), each corresponding to a separate recording day.

Each session contains three subfolders:

A/ – Auditory stimulation data

V/ – Visual stimulation data

S/ – Somatosensory stimulation (air puff)

Each subfolder contains multiple .smr files recorded using Spike2 software (Cambridge Electronic Design), containing LFP or unit activity.

File Naming Convention:

LFP MONO 200.smr → Local field potential (LFP) recorded from monocular V1, electrode at 200 µm depth

LFP BINO 1000_1.smr → Second LFP recording from binocular V1, 1000 µm depth

Area: MONO or BINO indicates monocular or binocular zone of V1

Number: Indicates depth in microns of electrode tip during the recording

_1, _2, etc.: Identifies repeated recordings at the same depth

Channels and Variables:

ECKHORN: Main analog signal channel. Contains local field potentials (LFPs), or in some sessions, single- and multi-unit activity, depending on recording filter configuration.

DigMark: Digital marker channel used to identify stimulus events. Marker codes:

08: Somatosensory stimulation

d: Auditory stimulation

b: Visual stimulation

MOVEMENT (optional): Present in some sessions. Indicates wheel movement activity detected during head-fixed experiments.

Sampling rate: Typically 10–25 kHz

Units: Analog signals in mV; digital events as time stamps

File Format:

All recordings are stored in .smr format (Spike2 native format). These files must be opened with Spike2 software.

No missing values are present; data is continuous and complete for each session. 

Code/software

The raw data files are in .smr format and were acquired using Spike2 (Cambridge Electronic Design, version 7.20). Spike2 was used for multiple purposes:

Visualization of raw signals (LFP and spikes)

Filtering and spike detection to isolate single-unit and multi-unit activity

Generation of peristimulus time histograms (PSTHs) from detected spikes

Export of data to .mat format for further analysis

Exported .mat files were then processed using:

MATLAB (MathWorks) with the EEGLAB toolbox→ For extracting Event-Related Spectral Perturbations (ERSP) and Inter-Trial Coherence (ITC) measures from LFP data

Python (version 3.11) with custom scripts and standard scientific libraries (e.g., NumPy, SciPy, Matplotlib)→ For computing response latencies and firing rates from the spike data

This workflow enabled detailed quantification of time-frequency dynamics and spiking responses across sensory modalities (visual, auditory, somatosensory).

Note: Custom scripts used in this analysis (e.g., for ERSP/ITC extraction or spike latency/frequency computations) and additional recommendations for working with these data are available upon request. Please contact the corresponding author for access.