Neuromorphic Computing at Cryogenic Temperatures: Bridging Neural Principles, Superconducting Systems, and Extreme-Environment Intelligence

Neuromorphic computing has emerged as a transformative paradigm for achieving energy-efficient and adaptive intelligence by emulating the structure and dynamics of biological neural systems. Significant advances have been made in neuromorphic materials, devices, circuits, and learning algorithms. However, a largely unexplored frontier lies in the realization of neuromorphic systems operating at cryogenic temperatures. This represents a new interdisciplinary direction at the intersection of neuroscience, superconducting electronics, quantum engineering, and ultra-low-power computing, with emphasis on implementing biologically inspired neural computation in extreme environments.Operation at cryogenic temperatures enables distinct device physics, including superconducting switching dynamics, reduced thermal noise, enhanced coherence, and ultra-fast signal propagation. These characteristics create opportunities to implement neural coding strategies such as event-driven and spike-based computation, as well as biologically inspired plasticity and learning mechanisms, in highly energy-efficient systems.In applications such as quantum computing, there is a growing need for intelligent cryogenic control hardware capable of low-latency, low-power signal processing within dilution refrigerators. Conventional complementary metal-oxide semiconductor-based architectures face significant constraints in such environments. Brain-inspired, spike-based computation implemented using cryogenic hardware offers a promising alternative for adaptive and efficient control and inference. Beyond quantum systems, applications include space electronics, sensing, particle detection, and other extreme environments requiring robust neural-inspired computation.This Research Topic aims to establish cryogenic neuromorphic computing as an emerging direction by bringing together work that demonstrates how neuroscience-inspired principles - such as spiking dynamics, synaptic plasticity, and distributed computation - can be realized in cryogenic hardware systems. We invite contributions integrating devices, circuits, architectures, and algorithms to implement neuromorphic functionality under cryogenic conditions, with clear grounding in biological neural systems and system-level behavior.Submissions focusing solely on device physics, materials, or superconducting components without demonstrating their role in neuromorphic computation are out of scope.Topics of interest include:• hardware implementations of biologically inspired neurons and synapses using cryogenic and superconducting technologies • superconducting circuits and architectures supporting spike-based and event-driven computation • cryogenic systems enabling synaptic plasticity, learning mechanisms, and distributed computation • spiking neural networks, neural coding strategies, and plasticity mechanisms in cryogenic neuromorphic systems • algorithms and co-design approaches linking cryogenic hardware with neuromorphic computation • neuromorphic approaches for cryogenic control, sensing, and extreme-environment applications Topic editors: Ahmedullah Aziz, Prasanna Date, Shamiul Alam Source: https://www.frontiersin.org/research-topics/80378/neuromorphic-computing-at-cryogenic-temperatures-bridging-neural-principles-superconducting-systems-and-extreme-environment-intelligence