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Spike-waveforms

Spike-waveforms

active ARFF Attribution 4.0 International (CC BY 4.0) Visibility: public Uploaded 24-03-2022 by Stewart
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Introduction Neurons in the brain use electrical signals communication. Different spike waveforms correspond to different cell types or different neuron morphologies (Henze et al., J. Neurophysiol. 2000). In learning about different spike waveforms, we may identify different neuronal types in our sample. Content The data contains 150 numerical categories (0 to 149, in columns) corresponding to the sampling of a 5-millisecond extracellular voltage at 30 kHz. Since the extracellular voltage amplitude related to the squared of the distance to the cell, we normalize the whole trace to the minimal voltage (through) that corresponds to the peak of the spike. To load it in Python. import pandas as pd waveforms = pd.read_csv('waveforms.csv', index_col = 'uid') waveforms.info() waveforms.iloc[0, :-1,].plot() plot the first waveform (last column is organoid) To know more For more information visiy my GitHub repository

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19 properties

741
Number of instances (rows) of the dataset.
151
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
150
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
Average class difference between consecutive instances.
99.34
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0.2
Number of attributes divided by the number of instances.

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