A spike sorting algorithm allows the identification of the activity of each neural source. We published two studies SpikeDeeptector and SpikeDeep-Classifier in the journal of the neural engineering. This study is based on our previously published studies. In this study, we aim to identify the neural activity of each source, online. More importantly, we aim to propose the first supervised learning-based solution to the online spike sorting problem.
This study is divided into two parts. Here, in part 1, we aim to label a large dataset. We aim to develop a pipeline that helps labeling the given dataset, conveniently. Our proposed pipeline does not require any experience in neuroscience for labeling.
Planned steps with estimated time
- Literature review and programming language background (15 days)
- Load Dataset (15 days)
we will use the Utah array datasets, microwire datasets, and several publicly available datasets.
- Feature extraction algorithm & sorting algorithm (15 days)
We will use the principal components analysis (PCA) for feature vector extraction and gaussian mixture model (GMM) for spike sorting.
- Programming user friendly framework (90 days)
A user-friendly (user interface (UI)) framework is required to program.
- Writing Task: Master thesis (45 days)
- Interest, motivation and knowledge in supervised machine learning methods.
- Programming skills in MATLAB (MathWorks Inc).
- Proficient use of scientific work
Begin and duration:
Immediately , 6 months
Muhammad Saif-ur-Rehman, M. Sc.
Tel.: 0208 / 88254806, firstname.lastname@example.org
Prof. Dr. Ioannis Iossifidis
Tel.: 0208 / 88254806, email@example.com