Introduction – Why Now?
Artificial intelligence has advanced at an extraordinary pace, transforming industries and reshaping how we live and work. Yet despite these breakthroughs, today’s AI still operates very differently from the human brain. Traditional deep learning models are powerful pattern recognisers, but they require vast amounts of energy and data to function — conditions far removed from the elegant efficiency of biological intelligence.
Spiking neural networks (SNNs) represent a new frontier: systems that learn and process information not by continuously passing numbers back and forth, but by communicating in spikes, much like the neurons in our own brains. This change may sound subtle, but it opens the door to entirely new possibilities in computing, from radically reduced energy consumption to more natural forms of perception and learning.
At Qognetix, we believe this is the moment to shine a light on SNNs. As the limitations of conventional AI become clearer, the need for brain-inspired approaches is growing. Our work is focused on helping bridge that gap — combining scientific validation with practical advances that bring spiking networks out of theory and into real-world use.
What Are Spiking Neural Networks?
At the most basic level, a spiking neural network (SNN) is an attempt to capture not just what the brain computes, but how it computes. Unlike conventional artificial neural networks, which use continuous numbers to represent information, SNNs rely on spikes — short, sharp signals that only occur when a neuron has something meaningful to transmit.
This difference matters. In the brain, most neurons are silent most of the time. They only “fire” when the timing of a spike carries useful information. An SNN follows this principle, treating time itself as part of the message. In other words, it is not just the presence of a signal that counts, but when it arrives.
To put this in everyday terms, imagine two ways of communicating:
- In one system, you constantly stream data whether or not anything important is happening.
- In the other, you only send a message when there is a reason — like tapping someone on the shoulder at just the right moment.
SNNs are built on the latter principle. This makes them more faithful to biology, but also potentially more efficient and powerful in situations where timing and context matter, such as recognising movement, interpreting sound, or adapting to new environments.
Why They Matter
Spiking neural networks are more than just an academic curiosity. They offer a fundamentally different way of building intelligent systems, one that could solve some of the biggest challenges facing today’s AI.
1. Energy Efficiency
Conventional AI models consume vast amounts of power, both during training and in operation. SNNs, by contrast, are event-driven — neurons only fire when there is something to say. This sparse communication mirrors the brain’s remarkable efficiency and points towards AI systems that can run on far less energy, making them more sustainable and better suited for use in edge devices like sensors, mobile robots, and wearables.
2. Temporal Processing
Unlike static networks, SNNs naturally encode the passage of time. The precise timing of spikes carries information, allowing these systems to handle dynamic data streams such as speech, vision, or motion with an elegance that traditional networks struggle to match. This opens the door to AI that not only recognises patterns, but also perceives and reacts in real time.
3. Continual Learning
Biological brains can learn throughout life without catastrophically forgetting old knowledge. SNNs move in this direction by supporting more flexible and incremental forms of learning. While this is still an active area of research, it suggests a pathway toward AI that can adapt on the fly, rather than requiring constant retraining from scratch.
4. Biological Realism
For neuroscientists, SNNs provide a powerful bridge between computational models and the actual workings of the brain. For technologists, this biological grounding offers reassurance: the closer our systems are to real neural processes, the more likely they are to unlock new, robust forms of intelligence.
Together, these advantages suggest that SNNs are not just a replacement for today’s deep learning, but a step towards an entirely new paradigm of intelligence — one that is efficient, adaptive, and rooted in the same principles that nature has perfected over millions of years.
Challenges Facing the Field
While spiking neural networks hold enormous promise, the path forward is not without obstacles. Like many emerging technologies, SNNs face scientific, technical, and practical challenges that must be addressed before they can reach their full potential.
1. Training Difficulties
Traditional deep learning thrives because of powerful training techniques like backpropagation. Spikes, however, are discrete events — not smooth signals — which makes them difficult to handle with standard gradient-based methods. Researchers have developed approximations and biologically inspired learning rules, but robust, general-purpose training algorithms for SNNs are still in development.
2. Hardware Limitations
SNNs demand a different kind of computing substrate. Conventional GPUs and CPUs are not naturally designed for event-driven, spike-based processing. Specialised neuromorphic hardware platforms — such as Intel’s Loihi, Manchester’s SpiNNaker, and Heidelberg’s BrainScaleS — show what is possible, but they remain largely in research settings. Building cost-effective, widely available platforms remains a hurdle.
3. Benchmarking and Standardisation
There is no universal benchmark for evaluating the performance of SNNs. Unlike image classification or text generation, where metrics are well established, spike-based systems often require custom tests. This makes it hard to compare approaches, slow down progress, and limit industrial adoption.
4. Translating Biology into Technology
Although inspired by the brain, no one expects SNNs to replicate it exactly. The challenge lies in deciding how much biological realism is necessary for practical systems, and where abstraction makes more sense. Striking the right balance is an ongoing research question.
These challenges are not insurmountable — in fact, they are precisely what makes the field exciting. Solving them will require collaboration between neuroscientists, computer scientists, and technologists. For companies like Qognetix, this is an opportunity to help define the next generation of intelligence by tackling these gaps head-on.
Our Perspective at Qognetix
At Qognetix, we see spiking neural networks not only as an exciting research frontier, but as the foundation for a new generation of intelligent systems. Our work focuses on making these models scientifically credible, technically practical, and accessible beyond the walls of specialist laboratories.
We have already achieved a key milestone: a platform capable of demonstrating biophysically faithful single-neuron spiking behaviour on standard, widely available hardware. This means our approach does not rely on exotic chips or costly infrastructure, but can run on the kinds of machines already familiar to researchers and developers.
Our technology is designed with scalability in mind. The same methods that allow us to model a single cell can extend to multi-cell networks, and ultimately toward cortical-scale architectures. The vision is to provide a substrate where both neuroscientists and AI researchers can explore new forms of computation — grounded in biological realism yet engineered for practical use.
Crucially, we are committed to scientific validation. We are actively working with academic partners to benchmark our system against canonical models, ensuring that what we produce is not only efficient but also faithful to the underlying biology. This collaborative approach helps position Qognetix as a bridge between fundamental neuroscience and applied artificial intelligence.
We intentionally keep the details of our core methods under wraps — this is our intellectual property. What we can say is that the engine at the heart of our system is a novel memory-based computational approach, one that allows for both precision and efficiency without the need for specialised silicon.
For us, the importance lies not only in what the technology achieves today, but in what it unlocks tomorrow.
Applications & Future Outlook
Spiking neural networks are more than an academic exercise. Their unique properties create opportunities across a wide range of domains — from immediate, practical uses to long-term breakthroughs that redefine what intelligence can be.
1. Edge AI and Robotics
Because SNNs communicate through sparse, event-driven spikes, they are naturally suited to low-power environments. This makes them ideal for robots, autonomous systems, and IoT devices that must operate in real time with limited energy budgets. Imagine drones that can navigate complex spaces on a single charge, or wearable devices that deliver intelligent insights without constant cloud connectivity.
2. Healthcare and Neuroscience
SNNs offer a closer link between biological and artificial models of the brain. This makes them valuable not only for computing, but also for advancing research into neurological conditions, drug discovery, and brain–machine interfaces. By capturing neural dynamics more faithfully, these systems could help accelerate our understanding of the brain and support new medical technologies.
3. Sustainable Intelligence
AI’s energy footprint is becoming a global concern. The event-driven nature of SNNs offers a pathway to sustainable, low-carbon intelligence. Deploying efficient, spike-based systems at scale could reduce the environmental cost of computation while expanding access to AI in resource-constrained settings.
4. Toward Synthetic Intelligence
The longer-term promise lies in the emergence of Synthetic Intelligence — systems that are not just trained to mimic tasks, but that grow and adapt in ways inspired by biology. By enabling scalable, biophysically grounded substrates for computation, SNNs may form the foundation of intelligence that is flexible, resilient, and deeply human-compatible.
At Qognetix, our roadmap reflects this trajectory. Starting with single-cell fidelity, extending to multi-cell and network-level models, and ultimately scaling to cortical substrates, our aim is to build both the scientific foundation and the technological bridge toward this future.
Conclusion & Call to Action
Spiking neural networks represent more than a technical innovation — they mark a turning point in how we think about intelligence itself. By shifting from continuous, data-hungry computation to event-driven, biologically inspired processes, SNNs offer a path toward systems that are more efficient, adaptive, and sustainable.
The field is still young, and challenges remain. But this is precisely what makes it so vital. As researchers, engineers, and entrepreneurs, we have the opportunity to shape the foundations of a new paradigm — one that is not simply a continuation of today’s AI, but a step toward Synthetic Intelligence that more closely mirrors the dynamics of the human brain.
At Qognetix, we are proud to be part of this journey. Our work is focused on scientific validation, practical deployment, and long-term vision: bringing spiking neural networks from theory into reality, and from single cells into networks of scalable intelligence.
We believe the next chapter in AI will not be written by any one company or discipline, but by a community committed to building systems that serve both people and planet. If you are a researcher, institution, or partner who shares this vision, we would love to hear from you.
