Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator performs numerical computations more energy-efficiently than any human. Yet modern AI models are a thousand times less efficient than the brain. These models have increasingly larger and larger dimension to maximize their representational capacity, requiring GPUs to perform multiplications of huge matrices. In contrast, the brain’s spiking neural networks exhibit factorially explosive encoding capacity even when their size is small. They compute through the polychronization of spikes rather than explicit matrix-vector products, resulting in lower energy requirements. This manifesto proposes a framework for thinking about popular AI models in terms of spiking networks and polychronization, and for interpreting spiking activity as nature’s way of implementing look-up tables. This suggests a path toward converting AI models into a novel class of architectures with much smaller size yet combinatorially large representation capacity, offering the promise of a thousandfold improvement in performance.  The presentation is based on Izhikevich (2025) https://arxiv.org/pdf/2512.11843

Bio:
Founder and CEO of SpikeCore, San Diego, CA
Founder and Chairman of the Board of Brain Corp, San Diego, CA
Founder and Editor-in-Chief of Scholarpedia – the peer-reviewed encyclopedia

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