Dictionary Construction for Accurate and Low-Cost Subspace Learning in Unsupervised Spike Sorting
Majid Zamani, Salinna Abdullah and Andreas Demosthenous (University College London, United Kingdom (Great Britain))
This paper discusses and outlines the construction of highly reliable and power efficient dictionaries as the main block in unsupervised feature learning from evolving sub-spaces. Three types of dictionaries are considered in this paper for unsupervised subspace learning including Hadamard φ_(H_h (k) ), equiangular tight frame φ_ETF(k) and random Bernoulli φ_Bern(k) . The constructed dictionaries are then utilized in unsupervised feature learning algorithm and the classification results are investigated using a library-based neural simulator consists of various noise levels and 300 different average spike shapes. The proposed dictionaries obtain high performance with classification error of around 7% over 100 windows of generated data using the developed neural signals for 3 to 6 clusters and noise levels σ_N between 0.05 and 0.3. In summary, the combination of constructed dictionaries and subspace learning present a new class of implantable feature extractors robust to extreme signal variations and well-suited for hardware implementation.
Conference: UKSim-AMSS 22nd International Conference on Computer Modelling and Simulation, UKSim2020
Published: Mar 25, 2020