Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals

Area of Science:

• Neuroscience
• Biotechnology
• Data Science

Background:

• Rapid advancements in neurotechnology have led to high-density neuronal probes capable of recording from thousands of channels simultaneously.
• Acquisition systems generate vast amounts of data (e.g., 17 GB/hour for a 128-channel system), posing significant challenges for data analysis.
• Extracting meaningful information from this data is crucial for understanding brain functions, diagnosing neurological diseases, and developing treatments.

Purpose of the Study:

• To provide an overview of popular open-source tools for analyzing extracellularly recorded neuronal signals in vitro.
• To cover essential analyses including spike trains, local field potentials (LFPs), and spike sorting.
• To outline future research directions in neurodata analysis.

Main Methods:

• Review and compilation of existing open-source software tools.
• Focus on tools applicable to extracellularly recorded neuronal signals in vitro.
• Categorization of tools based on analysis type: spike trains, LFPs, and spike sorting.

Main Results:

• Identification of several widely used open-source tools for neuronal data analysis.
• Discussion of the applicability of these tools for specific analyses like spike sorting and LFP analysis.
• Highlighting the availability and adaptability of these open-source solutions for the neuroscientific community.

Conclusions:

• Open-source tools are essential for managing and analyzing the large datasets generated by modern neurotechnological tools.
• Effective analysis of neuronal signals using these tools can significantly advance brain function decoding and disease diagnostics.
• Further development and application of these tools are critical for future neuroscience research.