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Neuronal networks from Cortical human iPSCs for Machine Learning Processing- NEU-ChiP

Project description

Harnessing the computing power of the human brain

Today’s use of AI and machine learning is becoming ever more prevalent in crucial areas like healthcare, finance, autonomous vehicles and speech recognition. However, enormous investments in the field’s present approaches to machine learning and neuromorphic computing have serious limits, requiring ever-growing computing power and high energy demands. To achieve a breakthrough in this field, the EU-funded NEU-ChiP project will study how human brain stem cells grown on a microchip can be taught to solve problems from data. Using sophisticated 3D computer modelling, an interdisciplinary consortium will conduct an observation of processes of cells’ changes and their plasticity to enable a major change in machine learning technology.


The EU and the rest of the world increasingly rely on artificial intelligence (AI) and machine learning (ML) for everyday functioning. Applications range from decision making in areas such as health and finance, face recognition, autonomous vehicle control, speech recognition and interaction with the internet and social media platforms. Estimated annual global spend on ML and AI is $77.6B in 2022 with a business value of $3.9T. However, current deep-learning machines suffer from inherent and difficult limitations: architectures not adaptable, ineffective learning rules, long training times and computing power, making advances unsustainable.
The NeuChiP project will tackle this issue. We will use emerging stem cell technology to make human neuronal networks that self-organise developmentally using the rules that form the brain. Networks will be made of layered cortical structures and hubs, with guided directional network connections and housed in a fabricated assembly. Input will be by patterned light at cells expressing optogenetic actuators, and output recorded via high resolution 3D multielectrode arrays. Intrinsic physiological mechanisms will enable them to undergo plasticity to designated input patterns. NeuChip will surpass the abilities of conventional artificial neural networks by conducting tasks in dynamically changing environments, exploiting the adaptive, complex and exploratory nature of biological human neural systems. To achieve this we have assembled a cross-disciplinary consortium of neuroscientists, stem cell biologists, bioelectronics developers, statistical physicists, together with machine learning and neuromorphic computing experts. We expect that within 15 years NeuChiP technology, using biological learning rules and powerful human-brain-based circuits will lead to novel and widespread advances in machine learning abilities and beyond, leading to a paradigm-shift in AI technology and applications to benefit society.

Call for proposal


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Net EU contribution
€ 1 068 661,25
Aston triangle
B4 7ET Birmingham
United Kingdom

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West Midlands (England) West Midlands Birmingham
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Participants (5)