ICIEA 2026 Special Session
SS43: Spiking Neural Networks: Sensing, Processing and Control Perspectives
Organized by:
Organizer 1: Paolo Arena
Email: paolo.arena@unict.it
Affiliation: University of Catania, Italy
Organizer 2: Luca Patanè
Email: lpatane@unime.it
Affiliation: University of Messina, Italy
This special session will explore the most recent advances in the field of spiking neural networks (SNNs) and the most exciting perspectives for their application in areas such as real-time sensing, perception, and control. Spiking neural networks (SNNs) are becoming a crucial research topic due to their bio-inspired paradigm, which enables energy-efficient learning and multimodal signal processing in accordance with the biological principles of parallel and distributed computation. These architectures are particularly well suited for handling time-dependent signals and stimuli, as well as for operating in noisy environments. This session will address design methodologies, enabling technologies, learning paradigms, and applications in the field of bio-inspired spiking neural networks and related areas. Topics of interest include, but are not limited to:
SNNs can represent a compelling alternative to traditional non-spiking neural networks, particularly when energy-efficient solutions are required for challenges such as processing time-dependent signals or modeling multiple, concurrent behaviors. These challenges are naturally and effectively addressed in living organisms, where robust and efficient spike-based computation is performed in real time. In recent years, an increasing number of robotic systems based on SNNs has emerged in the research landscape.
From this perspective, embodiment represents another key theme to be further investigated, with the aim of enabling efficient and adaptive autonomous systems in which neural processing is tightly integrated with motor control.
This special session aims to attract contributions from a broad range of scientific disciplines related to SNNs, including neuroscientists seeking to translate biological principles into functional systems, control engineers presenting state-of-the-art bio-inspired control strategies, and hardware-oriented researchers focusing on neuromorphic implementations of spiking neural networks.
A wide variety of SNN models has been proposed in recent years, featuring different neural topologies and synaptic plasticity mechanisms. This rapid development has generated strong interest within the electronics and computer engineering communities, including the neuromorphic research community, which is actively engaged in the design of integrated circuits hosting spiking architectures.
Applications of SNNs are steadily expanding across many domains, often involving robotic platforms that embody neural processing, thereby enabling the development of bio-inspired robotic systems.