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@@ -3,3 +3,46 @@
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Data Repository related to the paper "Cerebellar activity predicts vocalization in fruit bats" (currently under minor revision in Current Biology).
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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.
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+Data Guide: Cerebellar Activity Predicts Vocalization in Fruit Bats
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+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.
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+1. Frequency Tuning LFP
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+This dataset includes Local Field Potentials (LFP) data captured from 224 channels, organized by stimulus level and frequency.
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+
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+- ft-P2P (224 channels x levels x frequency)
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+- besterp: Evoked response data across levels (30:15:90) and frequencies (15:5:85).
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+- bestresponse: Best frequency-band level response (BFBL) for each channel.
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+- cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
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+
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+2. Frequency Tuning Spikes
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+This dataset contains spike count data from 224 channels, organized similarly by stimulus level and frequency.
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+- ft – spike count (224 channels x levels x frequency)
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+- N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85).
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+- bestresponse: Best frequency-Best level response (BFBL) for each channel.
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+- cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
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+3. Vocs (Vocalization Data)
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+This dataset provides information about the call parameters and classification data for both echo and communication calls.
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+callparams:
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+- dur2: Duration of calls, categorized by call type.
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+- pf2: Peak frequency of calls, categorized by call type.
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+- finalMatrix: Cross-correlation matrix of different call types.
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+- newClass: Identifiers for calls used in cross-correlation.
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+
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+- SpikeClassifData: Data for classifying spiking responses using Support Vector Machines (SVM).
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+- Ne: Normalized echo call spike data for training the SVM.
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+- Nc: Normalized communication call spike data for training the SVM.
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+- N2: Spike count data.
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+- N3: Identifiers for spike count data.
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+
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+- LFPClassifData: Data for classifying LFP responses using SVM.
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+- echoData: LFP data during echo calls for training the SVM.
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+- commData: LFP data during communication calls for training the SVM.
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+
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+---
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+
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+This guide outlines the structure of the data, which is organized into several Matlab files.
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