Running AI like neurons, using neurons.
At Neuraffica, mechanistic understanding of neuronal computation drives everything we build. Through targeted experiments, we reveal how biological circuits process signals and generate intelligence—not just their anatomical structure, but their functional logic. We translate these principles into neuro-inspired algorithms that mirror cortical computation and biological hardware platforms where living neurons accelerate AI training. This approach creates intelligent systems that are efficient by design, interpretable at their core, and grounded in how nature solved intelligence.
Our name tells our story. Neur anchors us in neuroscience—the mechanistic study of how biological circuits compute. Ffica captures the dynamic efficiency we're engineering: efficacy in computation, traffic of signals flowing through networks. Neurons are evolution's solution to efficient signal propagation, refined over millions of years. Neuraffica signals our commitment to harnessing that biological efficiency for next-generation AI.
Intelligence grounded in biological reality, not infrastructure constraints.
We envision a future where AI systems are efficient by design, interpretable at their core, and naturally aligned—built on mechanistic understanding of how biological computation actually works rather than optimized for existing hardware. Where insights from mapping neuronal intelligence become foundational knowledge that accelerates progress across robotics, neuromorphic computing, and AI research, benefiting the entire deep tech ecosystem and making advanced AI accessible to all.
Neuraffica decodes biological intelligence to engineer the next generation of AI.
We place living neurons at the center of our approach. Using breakthrough neurotechnology, we map not just brain structure but the functional dynamics and operational rules that generate computation—revealing which neuronal assemblies contribute to intelligence and which don't. We translate these mechanistic principles into neuro-inspired AI architectures and biological computing hardware that are efficient, interpretable, and aligned by design. As an AI-native company, our intelligent systems eliminate cross-disciplinary collaboration bottlenecks, seamlessly documenting expertise and building shared context across neuroscience, engineering, and AI.
Neuraffica is developing two complementary technologies that address AI's core limitations:
Current transformer models waste over 90% of their computation on redundant operations—a consequence of all-to-all connectivity that doesn't reflect how biological circuits route information. We're building architectures that translate neuroscience principles of signal propagation in neuronal circuits to eliminate this computational waste.
Living neuronal networks can learn with a fraction of the energy required by silicon systems, but existing approaches treat them as black boxes. We're building the first designable neuronal computing platforms—mapping how these networks learn to engineer and control their computational properties.
Our experimental platforms generate fundamental insights into how intelligence works—knowledge from targeted research at the intersection of AI and mechanistic neuroscience. We share these insights to accelerate progress in new compute strategies like quantum and neuromorphic computing, AI research, robotics, and bio-inspired computing, while improving traditional silicon-based computing through algorithms that are less artificial.
Neuraffica is built on a simple thesis: the next AI breakthrough requires neuroscientists who deeply understand AI, engineers who think like biologists, and seamless collaboration between fields that rarely intersect.
Our team spans experimental neuroscience, computational neuroscience, machine learning, electrical engineering, optics, and molecular biology—each bringing irreplaceable expertise from years of specialized research. We're AI-native by necessity: intelligent systems preserve each expert's context, generate documentation, and maintain cross-disciplinary alignment, ensuring that a decade of neuroscience intuition isn't lost in translation to engineering—or vice versa.
This combination is our moat: deep biological insight, technical implementation capability, and the infrastructure to make them work together seamlessly.
Insights on AI, neuroscience, and our vision for the future.
Coming soon. Stay tuned for updates on our research and progress.
We're looking for exceptional talent across neuroscience, engineering, biology, and AI! Whether you're mapping neural circuits, building algorithms, designing hardware, or optimizing biological systems—if you share our belief that understanding biological intelligence is the key to breakthrough AI, we want to hear from you.