Substrate Computing

Built the way
the brain works.

Not simulation. Not approximation. Physical circuits that encode the operational logic of neural tissue directly into silicon.

The architecture

Fluorescence microscopy — mouse cortex

The architecture of substrate computing

Not simulation. Not approximation. Physical circuits that behave the way neurons do.

01 — Architecture

Spiking neural circuits

Our chips encode neural logic at the substrate level. Each circuit fires only when input exceeds threshold — exactly as biological neurons do. No clock. No polling. No wasted cycles.

Fluorescence microscopy — neural network
02 — Computation

Event-driven by design

Power is consumed only when something changes. In sparse-signal environments, draw approaches zero. This is not an optimisation — it is the operating principle.

Interferometry pattern
03 — Adaptation

Local weight adaptation

On-chip learning without a training loop. Weights adapt locally in response to input — the same Hebbian principle that governs synaptic plasticity. No gradient. No server. No round-trip.

Neurons — fluorescence microscopy
04 — Power

20 W. Full-network inference.

The human brain runs on 20 watts. Our chips match that envelope at cortical scale. The physics of sparse, asynchronous computation make it possible.

20 Wsustained, full-network
vs. 500W+ GPU inference
~10¹¹
Neurons in the human cortex
1 ms
Synaptic transmission latency
½B yrs
Time this architecture has been proven

Every decade, we solve computation the same way. Not this one.

Be the first to know what comes next. We write when there is something worth reading. That is the only frequency we can promise.

SEM — microfluidic silicon chip
IC die photograph — LF444
SEM — crystalline nanostructures
STM — single molecule
Chladni vibrational pattern
Abstract filament structure
NeuroShell

Substrate computing chips built the way the brain works. Stockholm, Sweden.

© 2026 NeuroShell AB