Biomimetic Architectures for Sensing & Signal Processing

The biological systems are capable of sensing, processing and communicating, information robustly with extremely low-power consumption, using hybrid neural network architectures constructed with slow, less accurate, highly heterogeneous neural elements, neurons and synapses. Most of this sensory and neural processing performed using spikes-based computations. Investigating and understanding these inherent spikes-based computational methods used in the sensors, sensory-pathways and cortex may help us to envisage and build future technologies that are energy-efficient and intelligent.

Initially, the project investigates the spike-based sensing, communicating and processing principles by mimicking biological sensory and processing architectures in modern hardware technologies. Additionally, the student will also design and implement conventional biomedical architecture to sense and process a bio-signal to minimise noise and power consumption. Then, the student will investigate the ability to incorporate the bio-inspired techniques in sensors and signal processing architectures to realise low-power biomedical sensors and circuits that are less susceptible to noise and fault tolerance or robust to variability.

The work carried out involve implementation of neuromorphic hardware cochleae, using a novel spike encoding algorithm and Gammatone filters, mimicking the operation of the biological cochlea. In parallel, the student will also develop fetal ECG signal capturing and signal processing architecture using conventional electronics. Then, the student will investigate and implement bio-inspired spikes-based techniques to detect ECG and fetal ECG signals.