The future of computing might hinge on an unlikely An international team of scientists reports that the liquid produced by bees could become a practical material for building ecological components in neuromorphic computers. These systems imitate the neurons and synapses of the human brain, offering faster performance and dramatically lower power use than traditional machines.
Researchers at Washington State University have demonstrated a method to make these systems more organic. In a study published in the Journal of Physics D, they show that honey can form a memristor, a transistor-like element capable of both processing data and storing it in memory. This compact device, with a simple structure, performs in ways reminiscent of human neurons—an insight that could guide the creation of larger neuromorphic arrays. As co-author Feng Zhao explains, the work hints at the possibility where millions or billions of honey-based memristors could assemble into a functional neuromorphic system that operates like a brain. Lead author Brandon Sueoka and his team describe processing honey in solid form and sandwiching it between metal electrodes to create a synapse-like structure that can emulate neural signaling.
Tests showed that honey memristors can imitate synapses with very fast switching speeds, on the order of 100 to 500 nanoseconds. These devices reproduced key synaptic behaviors, including instantaneous time-dependent plasticity and velocity-dependent plasticity, which are central to how the brain learns and stores new information. The researchers even fabricated honey memristors at the scale of a human hair, and they plan to push toward nanoscale dimensions, enabling the integration of millions or billions of these elements to form a complete neuromorphic computing system. These advances point toward a future where bio-inspired hardware could augment or even redefine traditional computing architectures.
Hair-sized memristors
The memristors demonstrated by Zhao and colleagues not only mimic synaptic functions but also capture essential time-dependent learning properties. By shrinking the devices toward hair-thin dimensions, the team aims to scale up the array size dramatically, moving from individual components to fully realized neuromorphic networks that process and remember information in a brain-like manner. The ambition is clear: scale honey-based memristors to create comprehensive neuromorphic systems that handle cognitive tasks with higher efficiency.
The comparison with conventional architectures is striking. Neuromorphic concepts draw inspiration from the human brain, where synapses adjust their strength as learning occurs. The honey-based approach seeks to replicate this plasticity directly in hardware, reducing energy requirements and potentially boosting speed for certain workloads. As researchers explain, this line of work could lead to chips that operate with less heat and power than today’s most capable supercomputers, while delivering advanced pattern recognition and real-time learning capabilities.
Traditional computer systems typically rely on von Neumann architecture, which separates memory from processing. In such setups, data travels back and forth between CPU, memory, and input/output devices, consuming energy and time. Zhao notes that neuromorphic devices aim to streamline these processes by integrating memory and processing within the same unit, mirroring how biological neurons work and reducing overall energy expenditure. A notable benchmark cited in the discussion is the Fugaku supercomputer, developed by Fujitsu for the RIKEN Center in Kobe, Japan, which consumes tens of megawatts, whereas the human brain operates on only tens of watts. This juxtaposition highlights why researchers are pursuing brain-like hardware as a path to sustainable, scalable computing.
The human brain features more than 100 billion neurons and over 1 trillion synapses, giving it remarkable efficiency for concurrent processing and learning. Neuromorphic engineers aim to capture this architecture in hardware to achieve similar capabilities with far less energy input. The bee-derived memristor work adds a renewable and biodegradable dimension to the field, aligning with efforts from major technology players who are exploring neuromorphic chips with enormous neuron counts. While many current chips still rely on non-renewable and potentially toxic materials, Zhao and peers emphasize the potential of biocompatible, degradable alternatives that could ease end-of-life disposal and reduce environmental impact.
Biodegradable and renewable solutions
Several tech giants, including Intel and IBM, have released neuromorphic chips with neuron-like counts, but even those chips fall far short of the human brain’s capacity. Researchers are actively pursuing biodegradable and renewable materials to power these next-generation systems, maintaining performance while reducing ecological footprints. Zhao’s team is among a growing group exploring protein-based and sugar-derived components, while still seeing particular promise in the bee-produced honey as a stable, long-lasting medium for neuromorphic hardware. Honey’s non-spoilage and very low moisture content attract attention for reliable device fabrication and longevity in diverse environments.
Beyond the lab, the practical advantages include resistance to overheating and easier end-of-life disposal. Honey memristor chips are designed to tolerate lower thermal output than traditional chips, potentially reducing cooling requirements for neuromorphic systems. When these devices reach the end of their life cycle, they can be dissolved in water for environmentally friendly disposal. This green pathway aligns with broader goals to create renewable and biodegradable computing technologies that do not overburden landfills or ecosystems.
The researchers emphasize that honey offers real potential as a renewable resource in neuromorphic hardware. They caution that continued research is needed to optimize fabrication methods, ensure reliability across larger arrays, and verify long-term stability under operational conditions. Still, the promise is clear: a bio-derived, energy-efficient approach to computing that could reshape how machines learn and adapt. The study concludes with a call for continued exploration into honey-based memristors as a viable path toward scalable, brain-inspired computing.
Note: this overview reflects findings published in peer-reviewed outlets and subsequent commentary from the authors. The original research established foundational work on honey memristors, and ongoing investigations are expected to refine materials and integration strategies for real-world neuromorphic systems.