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

Project Information

NEU-ChiP

Grant agreement ID: 964877

DOI

10.3030/964877

Project closed

EC signature date 20 January 2021

Start date 1 September 2021

End date 28 February 2025

Funded under

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Total cost

€ 3 461 780,00

EU contribution

€ 3 461 780,00

Coordinated by

ASTON UNIVERSITY
United Kingdom

Periodic Reporting for period 2 - NEU-ChiP (Neuronal networks from Cortical human iPSCs for Machine Learning Processing- NEU-ChiP)

Reporting period: 2022-09-01 to 2025-02-28

Summary of the context and overall objectives of the project

Human society increasingly relies on artificial intelligence (AI) and machine learning (ML) applications for everyday functioning. These include face and voice recognition technologies, applications in the way that we interact with the internet and social media platforms and the control of driverless cars. However, the computers and algorithms that enable us to carry out these tasks are quite inefficient in terms of their training needs and power consumption. As our requirements get more complex so does the power consumption needed and the algorithmic complexity requirements to carry out the necessary functions. There is therefore an unmet need to develop better ML and AI methods that will be faster, more efficient, and use less energy. To address this, NEU-ChiP had the overall objective of using neuronal networks which are grown from human stem cells, with the aim of determining whether human neuronal networks processed information more efficiently than ML processes used by computers. Achieving the aims of NEU-ChiP would address issues facing society such as the efficiency of computer systems and their increasing use of electrical power which with the advent of popular AI is becoming unsustainable.
In NEU-ChiP we developed different approaches and analysis methods to determine the functional properties of human neuronal networks and demonstrated, to the level of proof-of-concept, that power-efficient human-cortical-based devices can be produced. The results from NEU-ChiP give us confidence that further technical and scientific developments of these technologies can lead to significant advances in the fields of computing and AI, contributing to major economic and societal change.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The project and its constituent work packages and tasks have now been completed. Some work continues in analysis of the data from final NEU-ChiP prototype in preparation for publication. The project developed methods to grow human stem cell derived neuronal networks and approaches for their electrical and optical interrogation in fabricated devices, together with analysis and inference methods to determine network structure and plasticity changes. A prototype was developed as a proof-of-concept, with continuing analysis and experiments beyond the project life. In addition to these scientific advances, we organised and participated in significant symposia and workshops, namely a joint symposium with a Japanese biological AI consortium at the 2023 NOLTA conference in Sicily, a satellite symposium at the 2024 FENS meeting on “Cortical neurons for biological computing”, which we believe is the first of its kind, and a workshop on “Maturation and Plasticity in Neural Networks” in October 2024 in Cargèse, Corsica. Other dissemination activities included a number of journal publications with four having contributions from multiple NEU-ChiP partners. Six innovations were identified for potential exploitation. These are at different readiness levels, with one having additional local accelerator funding and one being pursued for exploitation by the academic institution. The success of the project also contributed to the generation of new funding from the UK national centre for neuromorphic computing.
Main results:
• Generation of human iPSC-derived neural progenitor cell lines stably expressing a genetically encoded calcium indicator (GCaMP6).
• Fabrication and characterisation of micro-structured devices.
• Use of PDMS micro-structured devices to direct neurite growth between cell populations.
• Capacity to monitoring the development of human neuronal cultures during the processes of differentiation and maturation, using calcium imaging and MEAs, along several weeks and within micro-structured devices.
• Determination of response of neurons upon stimulation on the MEAs and characterization of functional remodelling due to plasticity.
• Investigation of effect of chemical stamping and PDMS mould patterning on neuronal network dynamic behaviour.
• Development of stimulation protocols to imprint tasks in evolving neural networks.
• Devise methods for inferring functional connectivity from neuronal activity data.
• Realizing probabilistic and statistical models of signal transmission by neurotransmitters between a presynaptic neuron and a postsynaptic neuron.
• Demonstration of long-term potentiation in neuronal networks in response to specific spatially defined electrical stimulation using the 3Brain CMOS MEA.
• Successful integration and optimisation of PDMS devices with 3Brain CMOS MEA system, and development of protocols shared across partner laboratories for the formation and long-term culture of neural cell circuits.
• Integration of ‘digital mirror devices’ with PDMS devices and 3Brain CMOS MEAs to provide bespoke patterned optical stimulation.
• Development of a ‘numerical simulations package’ (in silico neuronal networks) to
include aspects inspired by neuroengineering, such as guidance of axons, topographical modulation of substrate where neurons sit, and modular organization. This package has helped to understand experimental results, provide feedback and make predictions, and support theoretical models.
• Investigation of ‘reservoir computing’ in silico and in vitro, observing a good agreement between simulations and experiments in preparations of rat primary cultures.
• Advancement in the theoretical understanding of some empirical observations about the accessibility of computational tasks under stimulation protocols, which have been done in the context of the first algorithm.
• Development a method for visual informatics analysis of neuronal activity patterns, which allows to identify key developmental stages and the impact of stimulation on network behaviour.
• Development of a Bayesian method for inferring effective synaptic strength, neuronal type, as well as connectivity.
• Utilization of the aforementioned methodologies to study the effects of Long-Term Potentiation (LTP) stimulation on neuronal network plasticity.
• Development of a reservoir computing system using volatile memristors as leaky integrators for MNIST digit recognition with different input methods (1D, 2D, and Parity), achieving classification accuracies of 84%, 91%, and 93% respectively.
• Benchmarking of the neuromorphic electronic circuit implementation against biological
performance.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

NEU-ChiP achieved many of its objectives and progressed significantly towards its main aim. We have developed microfabrication approaches, neuronal network configuration methods
enabling specific stimulation and high-resolution recording (electrical and optical). A prototype was developed to enable testing and benchmarking. Experiments and analysis are continuing in this stream beyond the project end to determine the computational capabilities of the device in enabling discrimination of numerals. An important aspect was the identification and contact with groups around the world with related scientific questions, approaches and technology. These groups are based in the EU, USA, Australia and Japan, and face similar —though we believe not insurmountable— technical challenges. This renews our confidence that the knowledge gained from NEU-ChiP and its subsequent developments will enrich the fields of machine learning and AI, paving the way for new applications and discoveries.

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