DOI: 10.5281/zenodo.18260923
title: Katz-BLA-GC-ephys-data

description: Intan extracellular electrophysiology datasets used for research articles:



Mahmood, Abuzar, Jessica Steindler, et al. “Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity.” Journal of Neuroscience, vol. 43, no. 3, Jan. 2023, pp. 386–404. www.jneurosci.org, https://doi.org/10.1523/JNEUROSCI.1412-22.2022.

 

Mahmood, Abuzar, Jessica R Steindler, et al. “Sensory and Palatability Coding of Taste Stimuli in Cortex Involves Dynamic and Asymmetric Cortico-Amygdalar Interactions.” 4 July 2025. Neuroscience, https://doi.org/10.1101/2025.07.01.662567.

 



DATASET DOCUMENTATION:======================

This script exports electrophysiological data into compressed numpy files (.npz format).Each exported file contains neural spike trains and local field potentials (LFP) recordedfrom multiple brain regions during taste delivery experiments.

DATA STRUCTURE:--------------Each .npz file contains the following arrays:

1. 'spikes': Neural spike train data   - Shape: (n_tastes, n_trials, n_neurons, n_timepoints)   - Data type: Typically boolean or integer counts   - Dimensions:     * n_tastes: Number of different taste stimuli presented     * n_trials: Number of trial repetitions per taste     * n_neurons: Number of recorded neurons across all regions     * n_timepoints: Time samples (typically at 1000 Hz sampling rate)

2. 'lfp': Local field potential data   - Shape: (n_tastes, n_regions, n_trials, n_timepoints)   - Data type: Float (voltage measurements)   - Dimensions:     * n_tastes: Number of different taste stimuli presented     * n_regions: Number of brain regions (typically 2: one representative channel per region)     * n_trials: Number of trial repetitions per taste     * n_timepoints: Time samples (typically at 1000 Hz sampling rate)

3. 'region_names': Brain region labels for each LFP channel   - Shape: (n_regions,)    - Data type: String array   - Contains region names (e.g., 'BLA', 'GC') corresponding to each LFP channel (one representative channel per region)

4. 'taste_names': Taste stimulus labels   - Shape: (n_tastes,)   - Data type: String array   - Contains names of taste stimuli used in the experiment

HOW TO ACCESS DATA:------------------```pythonimport numpy as np

# Load a datasetdata = np.load('filename_export.npz')

# Access individual arraysspikes = data['spikes']lfp = data['lfp']region_names = data['region_names']taste_names = data['taste_names']

# Example: Get spikes for taste 0, all trials, neuron 1taste_0_neuron_1 = spikes[0, :, 1, :]

# Example: Get LFP for taste 1, region 1 (second region), all trialstaste_1_region_1 = lfp[1, 1, :, :]

# Example: Find BLA region indexbla_index = np.where(np.array(region_names) == 'BLA')[0][0]```

DATA CORRESPONDENCE:-------------------- The first dimension of both 'spikes' and 'lfp' corresponds to taste conditions- The third dimension of 'spikes' corresponds to individual neurons- The second dimension of 'lfp' corresponds to brain regions (one representative channel per region)- 'region_names' provides the brain region for each LFP channel (same indexing as lfp dimension 1)- 'taste_names' provides labels for the first dimension of both spike and LFP data- Time alignment: Both spike and LFP data share the same temporal dimension and sampling rate- Trial alignment: The trial dimension is matched between spike and LFP data

EXPERIMENTAL DESIGN:-------------------- Data represents neural responses to taste stimuli- Multiple trials per taste condition for statistical analysis- Simultaneous recording of spikes (single-unit activity) and LFP (population activity)- Multi-region recordings allow for cross-regional connectivity analysis- LFP data includes one representative channel per brain region to reduce file size while preserving regional information