Overview: EuroNMC is a pan-European research network dedicated to neuromorphic computing, an alternative computing paradigm inspired by the brain's parallel, sparse, and event-driven information processing. Approved by COST in May 2026 and coordinated by Forschungszentrum Jülich, the network brings together 139 researchers from 80 institutions across 24 countries, spanning materials science, device engineering, high-performance computing, brain-inspired AI, and neuroscience. Its goal is to bridge Europe's fragmented neuromorphic landscape by connecting bio-inspired models with hardware, software, and industrial applications.

Work Groups:

• WG1 – Foundations & Theoretical Models: Establish the conceptual and theoretical groundwork linking biological computation principles to neuromorphic systems.
• WG2 – Neuromorphic Devices & Circuits: Advance novel memory devices, chip design, and hardware architectures for energy-efficient, event-driven computing.
• WG3 – Neuromorphic Architectures & Brain-Inspired Algorithms: Develop architectures and algorithms that translate biological computation into scalable, hardware-ready systems.
• WG4 – Applications & Use Cases: Identify and demonstrate real-world applications across AI, robotics, and energy-efficient edge computing.
• WG5 – Training, Education & Resource Pooling: Coordinate training schools, gatherings, and shared resources to build cross-disciplinary expertise.
• WG6 – Dissemination & Outreach: Communicate the network's progress and results to the scientific community, industry, and the wider public.

Scope: Neuromorphic computing and engineering, novel computing architectures, novel electronic devices, brain-inspired computing and architectures — covering the full value chain from biologically inspired models to hardware, software, and industrial use.

Impact: EuroNMC strengthens coordination across a previously fragmented European research landscape, fostering interdisciplinary exchange and training to accelerate the development of low-power, brain-inspired computing technologies. By connecting academia, industry, and policy stakeholders across 24 countries, the network positions Europe as a leading contributor to sustainable, energy-efficient AI and computing.

Overview: PIONEER seeks to decode biological active sensing principles, leveraging low-power and low-latency neuromorphic sensing and computing to develop an intelligent system that optimizes visual information processing. The project develops a fully neuromorphic end-to-end active vision architecture, assesses biological realism in neuromorphic vision, and integrates active embodied object perception in humanoid robotics using the iCub platform.

Work Packages:

• WP1 – Neuromorphic Track: Develop integrated fully neuromorphic end-to-end active vision architecture with log-polar retina, V1 edge extraction, and OMS cells for motion detection using Speck neuromorphic hardware.
• WP2 – Biologically Plausible Track: Explore benefits of incorporating greater biological realism by replacing cortical stages with biologically detailed recurrent SNN models of primate V1 and V2, determining optimal abstraction level for robotics.
• WP3 – Active Vision for Object Perception: Integrate Sensorimotor Contingency Theory (SMCT) rules for embodied object perception through saccadic and wrist movements, enabling object discrimination and motion prediction on iCub robot.

Technologies: Speck neuromorphic platform with DYNAP-CNN chip, DAVIS346 COLOR event-driven cameras, iCub humanoid robot with 3D-printed camera mounts, pan-and-tilt unit (PTU), spiking neural networks, YCB object dataset.

Impact: PIONEER advances low-latency, energy-efficient processing by implementing a fully active neuromorphic system for object perception. The project bridges neuroscience, robotics, and AI, supporting EU Sustainable Development Goals by developing low-power intelligent systems. It establishes new benchmarks for neuromorphic robotic vision and offers alternatives to conventional feedforward computer vision approaches that rely on large datasets.

Overview: ENDEAVOR is a cutting-edge project that enhances robotic vision using event-driven cameras, spiking neural networks, and infant-inspired active exploration mechanisms. Unlike traditional vision systems that rely on static frames, ENDEAVOR enables robots like iCub to move, observe, and interact with objects in a bioinspired and energy-efficient manner.

Objectives:

• O1 – EDAV Model: Adapt a frame-based vision model to event-driven input using iCub's head and wrist movements.
• O2 – SNN EDAV: Implement a spiking neural network version using neuromorphic hardware for real-time, low-power operation.
• O3 – Benchmarking: Compare models based on accuracy, latency, and energy use on the YCB object dataset.

Technologies: DAVIS346 event-driven cameras, Speck neuromorphic chip with sCNN support, iCub humanoid robot with 3D-printed mounts, Sensorimotor Contingency Theory (SMCT) for perception-action coupling.

Impact: ENDEAVOR bridges neuroscience, robotics, and AI to push forward energy-efficient perception in robotics. The project supports the EU's Sustainable Development Goals by advancing low-power intelligent systems, and aims to establish benchmarks for neuromorphic robotic vision.