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NEURO-LABS

Financiado

Neuromorphic Learning in Organic Adaptive Biohybrid Systems

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 supercomp... ver más

31/12/2028

TU/e

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-12-11

ERC-2023-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

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1 Participantes

TU/e

2.00M€ | Lider

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Duración del proyecto: 60 meses Fecha Inicio: 2023-12-11
Fecha Fin: 2028-12-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto 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.

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