Update 'README.md'

README.md

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-# 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).
-
-### 2. Frequency Tuning Spikes 
+# %Cerebellum 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 .mat 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 .mat files, with 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.
+%
+% Variables:
+% - ft-P2P: Local Field Potential data (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)
+
+%% 2. Frequency Tuning Spikes
 % This dataset contains spike count data from 224 channels, organized similarly by stimulus level and frequency.
-% -	ft – spike count (224 channels x levels x frequency)
-% -	N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85).
-% -	bestresponse: Best frequency-Best level response (BFBL) for each channel.
-% -	cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT).
-
-### 3. Vocs (Vocalization Data)
+%
+% Variables:
+% - ft: Spike count (224 channels x levels x frequency)
+% - N2w: Peri-Stimulus Time Histogram (PSTH) data across levels (30:15:90) and frequencies (15:5:85)
+% - bestresponse: Best frequency-Best level response (BFBL) for each channel
+% - cfmt: Characteristic Frequency/Minimum Threshold for each channel (CFMT)
+
+%% 3. Vocs (Vocalization Data)
 % This dataset provides information about the call parameters and classification data for both echo and communication calls.
+%
+% Variables:
+% - 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:
+%    - 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:
+%    - echoData: LFP data during echo calls for training the SVM
+%    - commData: LFP data during communication calls for training the SVM
 
-% 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.
- 
 % ---
- 
-% This guide outlines the structure of the data, which is organized into several Matlab files.
-
+% This guide outlines the structure of the data, which is organized into several .mat files.
+% MATLAB version: R2021a.