Songbird neural data analysis: an open-source software in Python
Presenting author:
Our team records birds (zebra finches - Taeniopygia guttata) neural and singing activity to investigate motor-related neural mechanisms involved in vocal behaviour. In 2018, we started developing an open-source computational tool in Python for songbird data analysis. We aimed to have an internal pipeline and offer a new tool for the songbird electrophysiology research community.
The current version of the program allows the extraction of analogical and spike train signals from Spike 2 format files (*.smr) and saving them into individual .txt or .npy files for further analyses at a higher speed. Moreover, it is possible to plot different types of graphs (e.g., visualisation of the analogical signals and spike trains, spike shapes, spectrogram, power spectrum), create peri-stimulus time histograms (PSTH), inter-spike interval histograms (ISIH) and perform the correlation analyses between the neuronal data and specific acoustic features. A set of scripts for across-directories automated analysis is also available.
In the following years, we aim to expand our software by adding new features and focusing on data standardisation. We plan to use/adapt to our needs already available packages such as Elephant and Odmltables to implement a new set of analyses and annotate metadata information into neo/nix files, respectively. This would allow us to obtain more insights from the songbird neural data (e.g. LFP-spikes relationships, spike trains statistics) and be in line with the data standardisation movement happening in the field. Additionally, we want to make our tool more flexible towards other recording types. In 2018, we worked solely with single-wire tungsten electrodes, but now we have tetrode recordings and are moving towards Neuropixel data. Furthermore, we would like to develop an interactive interface to facilitate its use by non-programmers.
Implementing all these additional features could make our program more attractive and powerful, enhancing the chance of being used by several other groups, and contributing to standardising the scientific community’s methods and tools. From a long-term perspective, we also aim to propose a data format and on-line repository for data exchange and analysis cross-validation between labs doing songbird research.
Our program is available at https://github.com/aleblois/SongbirdNeuralDataAnalysis/, and documentation/tutorials can be found at https://aleblois.github.io/SongbirdNeuralDataAnalysis/.
The current version of the program allows the extraction of analogical and spike train signals from Spike 2 format files (*.smr) and saving them into individual .txt or .npy files for further analyses at a higher speed. Moreover, it is possible to plot different types of graphs (e.g., visualisation of the analogical signals and spike trains, spike shapes, spectrogram, power spectrum), create peri-stimulus time histograms (PSTH), inter-spike interval histograms (ISIH) and perform the correlation analyses between the neuronal data and specific acoustic features. A set of scripts for across-directories automated analysis is also available.
In the following years, we aim to expand our software by adding new features and focusing on data standardisation. We plan to use/adapt to our needs already available packages such as Elephant and Odmltables to implement a new set of analyses and annotate metadata information into neo/nix files, respectively. This would allow us to obtain more insights from the songbird neural data (e.g. LFP-spikes relationships, spike trains statistics) and be in line with the data standardisation movement happening in the field. Additionally, we want to make our tool more flexible towards other recording types. In 2018, we worked solely with single-wire tungsten electrodes, but now we have tetrode recordings and are moving towards Neuropixel data. Furthermore, we would like to develop an interactive interface to facilitate its use by non-programmers.
Implementing all these additional features could make our program more attractive and powerful, enhancing the chance of being used by several other groups, and contributing to standardising the scientific community’s methods and tools. From a long-term perspective, we also aim to propose a data format and on-line repository for data exchange and analysis cross-validation between labs doing songbird research.
Our program is available at https://github.com/aleblois/SongbirdNeuralDataAnalysis/, and documentation/tutorials can be found at https://aleblois.github.io/SongbirdNeuralDataAnalysis/.