Real-Time Detection and Classification of Purkinje-Cell Neural Activity

Real-Time Detection and Classification of Purkinje-Cell Neural Activity

Vrijenhoek, David (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Quantum & Computer Engineering)

Hamdioui, S. (mentor)
Gebregiorgis, A.B. (mentor)
Siddiqi, M.A. (mentor)
Muratore, D.G. (graduation committee)
Strydis, C. (graduation committee)

Delft University of Technology

Computer Engineering

2023-07-05

Purkinje cell is a type of neuron that can be found in the cerebellum. What characterises Purkinje cell neural activity is the fact that it exhibits two types of spiking behaviour; the so-called simple and complex spikes. These two types of spikes are thought to play a role in motor functionality. In order to better understand the relationship between Purkinje cell neural activity and the motor-cortex, neuroscientists record such neural activity in mice. However, current experimental setups pose a challenge as they involve a wired connection between the animal’s head stage and the recording device, which limits the mouse’s natural behaviour by restricting its movement. This work proposes a lightweight neural-spike detection and classification architecture for acquiring Purkinje cell neural activity. The proposed design discards unneeded information, by detecting and classifying spikes in real-time. This type of compression enables data storage on a removable device in the head stage, freeing mice from wires. Its small formfactor allows unrestricted movement during experiments, while a power-efficient design ensures long-termoperation. The performance of the algorithm has been evaluated using a software implementation, yielding a combined accuracy for detection and classification ranging from 92.74% to 94.54%. The system has been synthesised using the 45 nm Nangate Open Cell library resulting in an ASIC with an area of 0.22mm2 and a power consumption of 0.412mW.

Purkinje Cell
Classification
ASIC
Neural Implant

http://resolver.tudelft.nl/uuid:860cb17c-8f3a-49b6-903c-4de6685db7a7

Student theses

master thesis

© 2023 David Vrijenhoek