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Brain-Inspired Organic Modular Lab-on-a-Chip for Cell Classification

Project description

A miniature brain made from organic 'synapses' classifies cells in a tabletop device

Computers have long been equated with brains, but we are only now getting close to truly neuromorphic architectures and processing. Artificial intelligence started out with rules-based classical logic and progressed to learning based on inference and experience. To reach the next level where decisions can be made in novel situations that are not predetermined in any way, scientists are turning toward ever more human-like architectures. Researchers behind the EU-funded BIOMORPHIC project previously developed an organic artificial synapse, mimicking the junction across which two neurons transmit information in the form of an electrochemical signal. BIOMORPHIC plans to take that to incredible new heights, creating an interconnected network of these 'synapses' able to classify cells in an inexpensive microfluidic device.

Objective

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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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

ERC-STG - Starting Grant

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Call for proposal

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(opens in new window) ERC-2018-STG

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Host institution

TECHNISCHE UNIVERSITEIT EINDHOVEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 498 726,00
Address
GROENE LOPER 3
5612 AE Eindhoven
Netherlands

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Region
Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 498 726,00

Beneficiaries (1)

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