Abstract
In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.
Highlights
The open question of structure-function relationship has attracted lot of interests in Systems Neuroscience
Neuronal signals recorded by means of neuronal probes require rigorousprocessing and analysis
The wide variety of electrode size and dimensions allow different types of neuronal signals to be recorded from the extracellular space
Summary
The open question of structure-function relationship has attracted lot of interests in Systems Neuroscience. Developed neural probes allowed neuroscientists to investigate neural processing by monitoring groups of neurons and their activation patterns at unprecedented resolution (Brown et al, 2004; Giocomo, 2015), contributing to bridge the gap between neuronal network activity and behavior (Berenyi et al, 2014). They provided deep insights on the pathological basis of brain disorders (Friston et al, 2015). Some of the challenges and bottlenecks the community is currently facing will be identified and highlighted, and development perspectives which, in our opinion, will facilitate result reproducibility, flexibility, and standardization will be provided
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