DEVELOPMENT...      {
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    "description": "Introduction\nNeurons 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.\nContent\n\nThe 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.\nimport pandas as pd\nwaveforms = pd.read_csv('waveforms.csv', index_col = 'uid')\nwaveforms.info()\nwaveforms.iloc[0, :-1,].plot()  plot the first waveform (last column is organoid)\n\nTo know more\nFor more information visiy my GitHub repository",
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    "date": "2022-03-24 00:59:01",
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    "last_update": "2022-03-24 00:59:01",
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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 amplit "
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