Pioneering high-speed neuromorphic systems: the role of hyperfrequency circuits

Neuromorphic circuits and systems, drawing inspiration from the biological frameworks of neural networks, present a radical shift in artificial intelligence (AI) hardware design. These systems mimic the operations of biological neurons and synapses through spiking neural networks (SNNs), offering a path to more energy-efficient and adaptive computing. Recent strides in hyperfrequency and radiofrequency circuit technology have opened new avenues to enhance these systems' speed, energy efficiency, and signal clarity. This integration facilitates quicker neuron communication and minimizes latency, propelling neuromorphic technologies toward real-time applications in robotics, AI devices, and edge computing, merging insights from neuroscience, AI, and circuit design.

This Research Topic aims to confront the challenges and harness the opportunities of designing neuromorphic circuits at hyperfrequencies. It seeks to address the limitations of current neuromorphic architectures that struggle with communication bottlenecks, power consumption, and scalability when applied to real-time processing tasks. To this end, the application of hyperfrequency designs in neuromorphic systems is crucial for advancing their functionality and efficiency. Innovative techniques such as high-Q resonators, low-loss transmission lines, and rapid signal amplification are set to improve these systems' overall performance. Additionally, the integration of state-of-the-art materials like memristors enhances the compactness and energy efficiency of synaptic emulations.

To push the boundaries of what neuromorphic circuits can achieve, this Research Topic welcomes contributions focused on several key themes:

- Development and optimization of spiking neural networks (SNNs) for high-speed applications
- Integration of hyperfrequency and radiofrequency techniques into neuromorphic systems
- Advanced material use, such as memristors and phase-change devices, in circuit design
- Solutions for reducing latency in neuron communication
- Adaptive algorithms for learning within neuromorphic hardware
- Mixed-signal approaches that improve neuromorphic system performance

We invite a range of articles, including original research, reviews, and perspectives, that explore how hyperfrequency circuit design can revolutionize neuromorphic systems, making them more adept at handling the demanding requirements of modern intelligent technologies. Contributions that blend neuroscience, AI, and advanced circuitry to achieve real-time, biologically plausible processing are particularly encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Neuromorphic Systems, Spiking Neural Networks (SNNs), Hyperfrequency Circuits, Memristors, Real-time Processing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.