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@@ -3,47 +3,3 @@
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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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---
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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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-
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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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-- 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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-- 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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