Update 'Dataset guide.txt'

Dataset guide.txt

@@ -1,41 +1,41 @@
-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: Coherence Frequency Modulation Tuning (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)
-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.
-
----
-
-This guide outlines the structure of the data, which is organized into several Matlab files.
+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 
+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)
+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.
+
+---
+
+This guide outlines the structure of the data, which is organized into several Matlab files.