Artificial intelligence has demonstrated unprecedented advances in pattern and image recognition and is widely expected to significantly enhance progress in 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 and 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 local tuneable neuromorphic connection, to monitor and modulate motor neuron signals for the dynamic and adaptive learning control of a proof-of-principle soft robotic actuator.
Soft actuators are difficult to model due to their compliant characteristics and non-linear behaviour and thus present an ideal opportunity for 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 materials and spiking 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 actuator 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.
