Neuromorphic Computing

Your laptop uses about 65 watts to run. Your brain uses about 20. And your brain is doing far more: integrating sensory streams, maintaining autobiographical continuity, running predictive models of the world, coordinating movement, processing language, and managing thousands of concurrent processes—all on the energy budget of a dim lightbulb.

The gap between biological and silicon efficiency isn't just large. It's embarrassing. And it's unsustainable. Current AI systems require data centers that consume megawatts to achieve what evolution did with milliwatts.

Neuromorphic computing is the attempt to close that gap: building hardware that computes the way brains compute, with spikes and timing and event-driven processing instead of continuous floating-point operations on synchronized clocks. It's not just mimicry. It's recognizing that biological brains discovered computational principles that silicon has yet to implement.

Why This Matters for Coherence

Brains maintain coherence across time with radically different computational principles than conventional computers. Understanding those principles—event-driven processing, temporal coding, local learning rules, sparse activation—illuminates what coherence looks like when implemented efficiently in physical substrates.

Neuromorphic computing isn't just about efficiency. It's about understanding what kinds of computation naturally support coherence maintenance in real-time physical systems.

What This Series Covers

This series explores neuromorphic computing and its implications for understanding brain-like intelligence, efficiency, and the future of AI. We'll examine:

• How biological neurons actually compute using spikes and timing
• Current neuromorphic hardware from Intel, IBM, and academic labs
• Event-based sensors that see the way eyes see
• Liquid neural networks and continuous-time computation
• Why neuromorphic architectures are inevitable given AI's energy crisis
• How neuromorphic hardware naturally implements active inference
• The path to powerful AI running on wearable devices

By the end of this series, you'll understand why the question "How should we build intelligent machines?" has an answer written in biology—and why that answer is finally being translated into silicon.

Articles in This Series

The Chips That Think Like Brains: Inside the Neuromorphic Computing Revolution

Introduction to neuromorphic computing—why brain-inspired hardware will be 1000x more efficient than GPUs for AI.

IdeasthesiaRyan Collison

Spikes Not Floats: How Biological Neurons Actually Compute

The computational principles of biological neurons—why timing and spikes matter more than continuous activations.

IdeasthesiaRyan Collison

Intel Loihi and the Race for Brain-Like Silicon

Deep dive into Intel's neuromorphic chips—architecture principles and performance benchmarks.

IdeasthesiaRyan Collison

Event-Based Sensing: Cameras That See Like Eyes

How event-based sensors complement neuromorphic processors—vision systems that respond to change not frames.

IdeasthesiaRyan Collison

Liquid Neural Networks: Computation That Flows Like Water

MIT's liquid neural networks and continuous-time computation—networks that never stop adapting.

IdeasthesiaRyan Collison

The Energy Crisis of AI: Why Neuromorphic Is Inevitable

The unsustainable energy trajectory of current AI and why neuromorphic offers the only viable path to ubiquitous intelligence.

IdeasthesiaRyan Collison

Neuromorphic Active Inference: Hardware for the Free Energy Principle

How neuromorphic architectures naturally implement active inference—spiking networks as free energy minimizers.

IdeasthesiaRyan Collison

Edge AGI: Intelligence on Your Wrist

The path to powerful AI running on minimal hardware—neuromorphic chips enabling ubiquitous intelligence.

IdeasthesiaRyan Collison

Synthesis: What Brain-Like Hardware Teaches About Brain-Like Computation

Integration showing how neuromorphic constraints illuminate biological computation and coherence maintenance.

IdeasthesiaRyan Collison