Neuromorphic computing takes inspiration from biological nervous systems to build computing algorithms, hardware and sensors. It does not recreate a complete brain: it explores ways to process information that may suit particular tasks, such as responding to sensor events or running neural simulations.
In an interview published on 1 October 2026, Oliver Rhodes, Senior Lecturer in Bio-Inspired Computing at the University of Manchester, explains how those ideas work, what platforms such as SpiNNaker can do, and why their future applications should not be mistaken for mature products or clinical services.
What does “neuromorphic computing” mean?
Neuromorphic computing is a broad field that draws ideas from the nervous system at several levels: algorithms, computing architectures, chips and sensors. As Rhodes puts it, “Neuromorphic computing is quite a broad subject, which essentially looks to biology as inspiration to develop next-generation computing systems.”
Spiking neural networks are one approach within the field, not another name for all artificial intelligence. In these networks, activity is represented by discrete spikes—events that can be sent between processing elements. Other neuromorphic work focuses on the hardware or sensors, whether or not the full system uses a spiking algorithm.
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The aim is not to reproduce every feature of a brain. It is to borrow principles that could be useful in a particular computing system, while recognizing that much about how brains represent information is still not understood.
How can event-driven processing work?
Compute when activity arrives
In a conventional frame-based vision system, a camera regularly produces image frames, including areas that may not have changed. An event-based sensor instead reports changes at pixels as events. In an event-driven design, a processor can remain inactive when no relevant spikes arrive and respond when activity does arrive.
That approach can avoid processing or transmitting unchanged information, which may be useful for sparse or rapidly changing signals. It is a design strategy, not a guarantee that every neuromorphic device will use less energy than a conventional computer. The benefit depends on the workload and the system that processes it.
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Keep information close to computation
Rhodes also points to the brain’s distributed storage as inspiration. Many conventional computers move data between a processor and separate memory; neuromorphic designs can instead keep information closer to where it is used, an idea related to in-memory computing. Reducing data movement may help for suitable workloads, but the hardware places limits on which algorithms can be implemented efficiently.
That makes co-design important: researchers have to consider the algorithm and its target hardware together, then work out how to map and distribute computation across the system.
What is SpiNNaker, and how does it fit?
SpiNNaker is a large-scale research platform developed at the University of Manchester to model spiking neural networks. Rhodes describes it as a one-million-core system developed over a 20-year effort. The university’s platform description specifies that it incorporates over one million ARM mobile-phone processors and can model spiking networks at mouse-brain scale in biological real time. Those are the university’s descriptions of the platform, not a general performance measure for neuromorphic computing.
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SpiNNaker combines many low-power processing elements. Its routing architecture sends small packets representing neural spikes among processors, making it a concrete example of distributing event-based computation across specialized hardware.
SpiNNaker2 is a second-generation system. Intel’s Loihi is another platform based on related principles; the University of Manchester’s International Centre for Neuromorphic Systems describes Loihi 2 hardware hosted there for the Edgy Organism project. The available descriptions do not establish a comparable ranking of these systems by price, energy use, throughput or performance on a shared workload.
Where is neuromorphic computing being used now?
Event-based vision sensors
Event-based vision is among the more mature applications discussed by Rhodes. These sensors report pixel changes rather than repeatedly outputting full image frames. Rhodes says this can be helpful in fast-moving or high-contrast scenes. As an illustration, he describes a rocket launch in which a conventional image is saturated by the ignition, while event-based footage retains detail in the plume and sky. That is an example from the interview, not a quantified comparison of camera performance.
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Rhodes says event-based vision sensors are commercially available, but users often process their output with conventional AI. That reflects a practical maturity gap: conventional AI algorithms are more accessible than neuromorphic processors and algorithms. The Centre also describes research that combines vision with processing near the sensor. No particular camera model or price is established by these descriptions.
Neural simulation
SpiNNaker was designed in part to help accelerate neural simulations. Rhodes recounts a milestone in which a cortical model ran in real time, while noting that newer conventional computers have since surpassed that record. The interview does not give the benchmark paper or test conditions, so this should be understood as his account of a past milestone rather than a current comparative performance result.
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Patient-specific neuroscience models
Rhodes discusses the possibility of tailoring simulations to an individual—for example, to explore aspects of Alzheimer’s disease or responses to deep brain stimulation for Parkinson’s disease. He describes this as an early research area, not an available diagnostic or treatment service. “We are not quite at the point where your local doctor will be able to run a model like this, but we’d like to see things get to that point.”
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Edge devices, robotics and smart glasses
Processing information near a sensor could be useful in remote or resource-constrained settings, and Rhodes sees potential for edge devices and smart glasses. One example is NimbleAI, which combines event-based vision, foveated sensing and a small hardware accelerator to direct higher-resolution sensing toward regions of interest. The Centre describes its contribution as foveated-sensing algorithms and real-time near-sensor hardware. NimbleAI was an EU Horizon Europe project that ended in March 2026; those project activities do not establish that a finished consumer smart-glasses product is available.
What limits the field, and how should systems be compared?
Neuromorphic systems do not yet have the mature software stack available for GPU-based machine learning. Mapping a model onto specialist hardware can change its performance, and compiling or distributing work across those devices remains a research problem. Rhodes also cautions against comparing academic neuromorphic work directly with systems such as ChatGPT when the resources used for training differ.
Learning is another open challenge. Online learning and reinforcement learning are active areas of research, but the interview does not claim that human-like learning has been achieved. Likewise, possible benefits in energy use or latency need to be judged for a particular workload; the interview supplies no general savings figure or universal advantage.
A fair comparison should therefore ask what task is being run, how activity is represented, where data is stored and moved, and how mature the required hardware and software are. Without comparable tests on the same workload, the names of platforms alone cannot establish which system is best.
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