Summary: We are developing methodology for analysis of large-scale neural data, primarily two-photon calcium imaging data. We aim to develop an end-to-end modeling and analysis framework for multi-trial neuronal activity across multiple modalities and spatiotemporal scales:
1) low-level processing of raw calcium imaging data ,
2) mid-level organization of extracted interconnected neuronal time-traces,
3) high-level analysis of evolving network of neurons and behavior over long term learning.
For imaging analysis, we have developed methods for ROI extraction and time-trace demixing of 2p data and parcellation of widefield calcium imaging data. Our approach does not directly model calcium dynamics and can apply to motion-corrected high-dimensional imaging data. To further validate and extend our approaches we would be happy to receive cellular-level 1p data, voltage imaging and spatial transcriptomics datasets.
We are developing tools for visualization of dynamics and analysis of a network of neurons as it learns a task (longitudinal studies), with preliminary results in artificial networks. To apply this tool we need cellular activity from a large identified group of neurons during learning, alongside behavior and/or stimulus.
Finally we are developing tools for unsupervised and semi-supervised behavior annotation. Complex recurring behavior from multiple animals (of the same species) would assist in further developing and validating our approach. For the semi-supervised approach, labels for part of the data / spatial trajectories (such as Deep lab cut) are required.