Bio-Inspired Signal Processing on Image and Audio Data

Dear Colleagues,

Bio-inspired signal processing, grounded in evolutionary computation, cellular automata, and neuromorphic computing, provides powerful paradigms for solving complex and uncertain real-world signal processing problems. By simulating the collective behaviors of simple biological agents and leveraging parallel, self-organizing, and hybrid optimization mechanisms, these approaches enable adaptive, robust, and energy-efficient intelligent systems.

Inspired by biological perception and neural computation, bio-inspired models exhibit complex spatiotemporal dynamics, distributed representations of information, and event-driven processing, while supporting effective cooperation and integration across multimodal signals. Such mechanisms enhance optimization capability, interpretability, and scalability in processing images, audio, and time series data under dynamic and resource-constrained environments.

In this context, this Special Issue aims to present diverse bio-inspired approaches in signal processing. Potential topics include, but are not limited to, the following:

1. Genetic and evolutionary algorithms for adaptive optimization broadly applied to image and audio (and other time series) signal processing under complex and uncertain environments.
2. Cellular automata-based models for distributed, parallel, and self-organizing representation learning, enabling formation of complex spatiotemporal patterns and robust signal analysis.
3. Neuromorphic/neuro-inspired computing and spiking neural network models for energy-efficient, event-driven processing of sensory signals, supporting real-time and low-power intelligent systems that process sensor data in real time.
4. Bio-inspired mechanisms for integrating heterogeneous sensory signals, enabling cooperative multimodal perception and improved interpretability in complex naturalistic environments.
5. Interpretable bio-inspired signal processing frameworks that enhance robustness, scalability, and reliability through biologically grounded structures and functional dynamics.

Dr. Eunsom Jeon
Dr. Pavan Turaga
Dr. Andrew Yang
Guest Editors

Manuscript Submission Information

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• artificial neural networks
• genetic and evolutionary algorithms
• cellular automata
• neuromorphic computing
• event-driven processing
• information fusion
• signal processing
• human-centric processing
• explainable AI