Periodic Reporting for period 1 - NEU-ChiP (Neuronal networks from Cortical human iPSCs for Machine Learning Processing- NEU-ChiP)
Reporting period: 2021-09-01 to 2022-08-31
Human society increasingly relies on artificial intelligence (AI) and machine learning (ML) applications for everyday functioning. These include face and voice recognition technologies and applications in the way that we interact with the internet and social media platforms, to control of driverless cars and trains. However, the computers that enable us to do this are quite inefficient, and as our requirements get more complex there are difficulties in designing new computers and in the programs that will enable them to carry out the necessary functions. There is therefore a need to develop better ML and AI methods that will be faster, more efficient, and use less energy.To address this NEU-ChiP has the overall objective of using neuronal networks which are grown from human stem cells. We will use these to test ML processes to see if they use different methods to those used by computers. The results of the project will tell us more about the ways that human neuronal networks process information and this will help us to develop new ways of tackling AI problems that face society.
The project has progressed significantly in the constituent work packages towards meeting their objectives and main project aims which include progress in applying
optogenetic and cMOS MEA interrogation of hIPSC derived human neuronal networks, microfabrication of chemically patterned surfaces for directed cell growth, determining network architecture effects on network firing properties, development of network simulators containing thousands of neurons, and development of a 2-node simulation with excitatory and inhibitory neurotransmitter receptors. The consortium held a fruitful 2-day meeting in Barcelona with presentations and progress discussions. All deliverables for period 1 were submitted.
Main results:
• Generation of human iPSC-derived neural progenitor cell lines stably expressing genetically encoded calcium indicator (GCaMP6).
• Development of functional and reproducible “defined” co-cultures that respond to ionic and pharmacological manipulation.
• Growth of neuronal cells in devices and patterned surfaces.
• Recording from functional 2D and 3D organoid cultures on 3Brain CMOS MEA devices.
• Fabrication and characterisation of Micro-structured devices.
• Use of micro scale devices to direct neurite growth between cell populations.
• Monitoring the development of the human neuronal cultures during the processes of differentiation and maturation using Calcium imaging and MEAs.
• Determination of response of neurons upon stimulation on the MEAs.
• Investigation of effect of chemical stamping and PDMS mould patterning on neuronal network dynamic behaviour.
• Numerical simulations to understand the experimental results and provide data to WP4.
• Development of stimulation protocols to imprint tasks in evolving neural networks.
• Devising methods for inferring functional connectivity from neuronal activity data.
• Probabilistic and statistical models of signal transmission by neurotransmitters between a presynaptic neuron and a postsynaptic neuron.
• Dissemination and exploitation plan – this was developed during a consultation with the beneficiaries (D6.2) and was submitted in M12.
• Kick off meeting online and in-person consortium meeting in Barcelona.
• All deliverables for P1 submitted.
optogenetic and cMOS MEA interrogation of hIPSC derived human neuronal networks, microfabrication of chemically patterned surfaces for directed cell growth, determining network architecture effects on network firing properties, development of network simulators containing thousands of neurons, and development of a 2-node simulation with excitatory and inhibitory neurotransmitter receptors. The consortium held a fruitful 2-day meeting in Barcelona with presentations and progress discussions. All deliverables for period 1 were submitted.
Main results:
• Generation of human iPSC-derived neural progenitor cell lines stably expressing genetically encoded calcium indicator (GCaMP6).
• Development of functional and reproducible “defined” co-cultures that respond to ionic and pharmacological manipulation.
• Growth of neuronal cells in devices and patterned surfaces.
• Recording from functional 2D and 3D organoid cultures on 3Brain CMOS MEA devices.
• Fabrication and characterisation of Micro-structured devices.
• Use of micro scale devices to direct neurite growth between cell populations.
• Monitoring the development of the human neuronal cultures during the processes of differentiation and maturation using Calcium imaging and MEAs.
• Determination of response of neurons upon stimulation on the MEAs.
• Investigation of effect of chemical stamping and PDMS mould patterning on neuronal network dynamic behaviour.
• Numerical simulations to understand the experimental results and provide data to WP4.
• Development of stimulation protocols to imprint tasks in evolving neural networks.
• Devising methods for inferring functional connectivity from neuronal activity data.
• Probabilistic and statistical models of signal transmission by neurotransmitters between a presynaptic neuron and a postsynaptic neuron.
• Dissemination and exploitation plan – this was developed during a consultation with the beneficiaries (D6.2) and was submitted in M12.
• Kick off meeting online and in-person consortium meeting in Barcelona.
• All deliverables for P1 submitted.
By the end of the project, we expect to have achieved the generation of reproducible human neuronal networks on a device enabling specific stimulation and high-resolution recording. The computational capabilities of the device will enable discrimination of numerals and simple shapes. Beyond the project, knowledge gained will inform the ML and AI fields, leading to new applications and discoveries. NEU-ChiP will develop new bioinspired computing technology which in the long term will have the potential to profoundly change the lives of EU citizens. The use of AI and ML permeates every sphere of human society. The enhanced efficiency and advances from NEU-ChiP will lead to new low energy computing devices and enhanced applications for fields such as autonomous vehicles and control systems.