Hariharan_CurrentBio2024_CerebellumDataset/README.md

Dataset

Data Repository related to the paper "Cerebellar activity predicts vocalization in fruit bats" (currently under minor revision in Current Biology).

The data is stored in Matlab files containing neural recordings from the cerebellum of fruit-eating bats, captured during the production of both echolocation and social calls.

Data Guide: Cerebellar Activity Predicts Vocalization in Fruit Bats

This repository contains data collected from cerebellar recordings in fruit-eating bats during the production of echolocation and social calls. The data is structured into various Matlab files, with the details of each dataset and its specific parameters explained below.

1. Frequency Tuning LFP This dataset includes Local Field Potentials (LFP) data captured from 224 channels, organized by stimulus level and frequency.

• ft-P2P (224 channels x levels x frequency)
• besterp: Evoked response data across levels (30:15:90) and frequencies (15:5:85).
• bestresponse: Best frequency-band level response (BFBL) for each channel.
• cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).

1. Frequency Tuning Spikes This dataset contains spike count data from 224 channels, organized similarly by stimulus level and frequency.
2. ft – spike count (224 channels x levels x frequency)
3. N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85).
4. bestresponse: Best frequency-Best level response (BFBL) for each channel.

5. cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).

6. Vocs (Vocalization Data) This dataset provides information about the call parameters and classification data for both echo and communication calls.

callparams:

• dur2: Duration of calls, categorized by call type.
• pf2: Peak frequency of calls, categorized by call type.
• finalMatrix: Cross-correlation matrix of different call types.

• newClass: Identifiers for calls used in cross-correlation.

• SpikeClassifData: Data for classifying spiking responses using Support Vector Machines (SVM).

• Ne: Normalized echo call spike data for training the SVM.

• Nc: Normalized communication call spike data for training the SVM.

• N2: Spike count data.

• N3: Identifiers for spike count data.

• LFPClassifData: Data for classifying LFP responses using SVM.

• echoData: LFP data during echo calls for training the SVM.

• commData: LFP data during communication calls for training the SVM.

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This guide outlines the structure of the data, which is organized into several Matlab files.