Summary
Artificial intelligence has demonstrated unprecedented advances in pattern and image recognition and is widely expected to significantly increase progress in smart healthcare devices, but continues to rely on inefficient supercomputers, operating remotely. On the other hand, relevant information for these applications mostly exists locally at the physiological level. Smart personalised bioelectronic applications can be tailored to a specific and unique case – or person – with the ability to be adapted, trained and optimised over time.
In this ERC project, organic neuromorphic engineering is combined with bioelectronics to achieve a tuneable neuromorphic platform, locally monitoring and modulating biosignals for the dynamic and adaptive learning control of a proof-of-principle soft robotic actuator.
Due to their compliant and non-linear characteristics soft actuators are difficult to model and thus present an ideal opportunity to demonstrate neuromorphic learning control. Organic electronic materials have been successfully implemented as building blocks in neuromorphic computing and bioelectronic applications. Particularly, mixed ionic-electronic conductors possess exceptional characteristics for use in biological environments.
At the interface between mechanical engineering, materials science, neuromorphic engineering and bioelectronics, neuro-labs will develop an organic neuromorphic platform, by optimisation of organic materials and circuits, and integration of sensors, neuromorphic devices, and microfluidics. We will develop a closed-loop adaptive biocircuit and demonstrate local tuning and neuromorphic learning control of a soft gripper. Finally, we will show optimised biocontrol of the gripper using biohybrid synapses modulated by the neurotransmitter environment, directly tuning the feedforward parameters in hardware. This will open a completely new field of adaptive neuromorphic biointerfaces and inspire a novel conceptual approach for learning control.
In this ERC project, organic neuromorphic engineering is combined with bioelectronics to achieve a tuneable neuromorphic platform, locally monitoring and modulating biosignals for the dynamic and adaptive learning control of a proof-of-principle soft robotic actuator.
Due to their compliant and non-linear characteristics soft actuators are difficult to model and thus present an ideal opportunity to demonstrate neuromorphic learning control. Organic electronic materials have been successfully implemented as building blocks in neuromorphic computing and bioelectronic applications. Particularly, mixed ionic-electronic conductors possess exceptional characteristics for use in biological environments.
At the interface between mechanical engineering, materials science, neuromorphic engineering and bioelectronics, neuro-labs will develop an organic neuromorphic platform, by optimisation of organic materials and circuits, and integration of sensors, neuromorphic devices, and microfluidics. We will develop a closed-loop adaptive biocircuit and demonstrate local tuning and neuromorphic learning control of a soft gripper. Finally, we will show optimised biocontrol of the gripper using biohybrid synapses modulated by the neurotransmitter environment, directly tuning the feedforward parameters in hardware. This will open a completely new field of adaptive neuromorphic biointerfaces and inspire a novel conceptual approach for learning control.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101125598 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 996 143,00 Euro - 1 996 143,00 Euro |
Cordis data
Original description
Artificial intelligence has demonstrated unprecedented advances in pattern and image recognition and is widely expected to significantly increase progress in smart healthcare devices, but continues to rely on inefficient supercomputers, operating remotely. On the other hand, relevant information for these applications mostly exists locally at the physiological level. Smart personalised bioelectronic applications can be tailored to a specific and unique case – or person – with the ability to be adapted, trained and optimised over time.In this ERC project, organic neuromorphic engineering is combined with bioelectronics to achieve a tuneable neuromorphic platform, locally monitoring and modulating biosignals for the dynamic and adaptive learning control of a proof-of-principle soft robotic actuator.
Due to their compliant and non-linear characteristics soft actuators are difficult to model and thus present an ideal opportunity to demonstrate neuromorphic learning control. Organic electronic materials have been successfully implemented as building blocks in neuromorphic computing and bioelectronic applications. Particularly, mixed ionic-electronic conductors possess exceptional characteristics for use in biological environments.
At the interface between mechanical engineering, materials science, neuromorphic engineering and bioelectronics, neuro-labs will develop an organic neuromorphic platform, by optimisation of organic materials and circuits, and integration of sensors, neuromorphic devices, and microfluidics. We will develop a closed-loop adaptive biocircuit and demonstrate local tuning and neuromorphic learning control of a soft gripper. Finally, we will show optimised biocontrol of the gripper using biohybrid synapses modulated by the neurotransmitter environment, directly tuning the feedforward parameters in hardware. This will open a completely new field of adaptive neuromorphic biointerfaces and inspire a novel conceptual approach for learning control.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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