Brain-inspired (neuromorphic) computing has recently demonstrated advancements in pattern and image recognition as well as classification of unstructured (big) data. However, the volatility and energy required for neuromorphic devices presented to date significantly complicate the path to achieve the interconnectivity and efficiency of the brain. In previous work, recently published in Nature Materials, the PI has demonstrated a low-cost solution to these drawbacks: an organic artificial synapse as a building-block for organic neuromorphics. The conductance of this single synapse can be accurately tuned by controlled ion injection in the conductive polymer, which could trigger unprecedented low-energy analogue computing.
Hence, the major challenge in the largely unexplored field of organic neuromorphics, is to create an interconnected network of these synapses to obtain a true neuromorphic array which will not only be exceptionally pioneering in materials research for neuromorphics and machine-learning, but can also be adopted in a multitude of vital medical research devices. BIOMORPHIC will develop a unique brain-inspired organic lab-on-a-chip in which microfluidics integrated with sensors, collecting characteristics of biological cells, will serve as input to the neuromorphic array. BIOMORPHIC will combine modular microfluidics and machine-learning to develop a novel platform for low-cost lab-on-a-chip devices capable of on-chip cell classification.
In particular, BIOMORPHIC will focus on the detection of circulating tumour cells (CTC). Current methods for the detection of cancer are generally invasive, whereas analysing CTCs in blood offers a highly desired alternative. However, accurately detecting and isolating these cells remains a challenge due to their low prevalence and large variability. The strength of neuromorphics precisely lies in finding patterns in such variable data, which will result in a ground-breaking CTC classification lab-on-a-chip.
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