Recent advances in spike-based neural coding for tactile perception.

Area of Science:

• Neuromorphic Engineering
• Artificial Tactile Perception
• Biologically Inspired Computing

Background:

• Traditional von Neumann architecture limits artificial tactile systems with high latency and energy inefficiency.
• Neuromorphic engineering offers a bio-inspired alternative using event-driven, spike-based coding.
• This mirrors neural signaling in human somatosensory systems.

Purpose of the Study:

• Systematically review spike-based neural coding techniques for tactile perception.
• Focus on encoding strategies, neuromorphic hardware, and decoding methodologies.
• Outline a roadmap for advanced artificial tactile systems.

Main Methods:

• Comparison of rate coding and temporal coding for biological plausibility and efficiency.
• Evaluation of hardware platforms: oscillator circuits, CMOS/memristor neurons, triboelectric sensors.
• Analysis of decoding mechanisms: spike-timing-dependent plasticity, spiking neural networks.

Main Results:

• Identified efficient encoding and decoding strategies for tactile data.
• Evaluated diverse neuromorphic hardware for tactile sensing and processing.
• Emphasized co-design for integrated sensing, encoding, and processing.

Conclusions:

• Spike-based coding and neuromorphic hardware enable efficient artificial tactile perception.
• Achieved systems offer millisecond latency and sub-milliwatt power consumption.
• Essential advancements for robotics, prosthetics, and wearable electronics.