Substrate-aware technical layer · neuromorphic-biocomputing/1.0
Compare computation without pretending every substrate is the same.
This system connects spiking models, silicon neuromorphic hardware, living neural cultures, molecular computing, and hybrid bioelectronics while keeping their measurements, maturity, reproducibility controls, and ethical obligations distinct.
Substrate first
Software, digital silicon, analog devices, cells, organoids, molecules, and hybrid loops retain separate evidence contracts.
Complete accounting
Energy and performance claims state the task, quality, chip, host, interface, preparation, instrumentation, and support boundary.
No sentience certification
Activity, adaptation, complexity, or task performance does not establish consciousness, sentience, intelligence, or moral status.
Opening comparison corpus
Preserve non-equivalence
Bounded comparison
Artificial and spiking neural networks
When is a spike-based model meaningfully different from a conventional neural network?
Bounded comparison
Digital and mixed-signal neuromorphic hardware
How can deterministic digital state and variable analog dynamics be compared fairly?
Bounded comparison
Simulation and physical substrate
What transfers from a model evaluated in software to a device or living implementation?
Bounded comparison
Dissociated neural culture and brain organoid
Which biological features and experimental constraints differ between two living neural substrates?
Computational models
5 concepts
Neuromorphic computing
Design computation around sparse events, local state, distributed memory, and adaptive dynamics inspired by nervous systems.
0 math bridges →
Spiking neural networks
Represent neurons as stateful dynamical units communicating discrete events through time.
1 math bridge →
Neural coding and time
Specify what information a spike train represents and which temporal precision the task actually requires.
1 math bridge →
Synaptic plasticity and learning
Separate an observed state change, a learning rule, and improved performance under a preregistered task.
1 math bridge →
Event-driven computation
Perform work when informative events occur rather than updating every component on a fixed global schedule.
1 math bridge →
Silicon and physical substrates
5 concepts
Mixed-signal neuromorphic hardware
Combine analog state dynamics with digital communication and control to execute neural models efficiently.
1 math bridge →
In-memory and memristive computing
Use physical memory state to perform selected computation near or within storage arrays.
1 math bridge →
Neuromorphic sensors
Emit asynchronous changes from a sensor rather than repeatedly transmitting complete sampled frames.
1 math bridge →
Hardware-software co-design
Design models, compilers, mappings, hardware constraints, and measurements as one versioned system.
1 math bridge →
Physical reservoir computing
Use a physical system’s nonlinear transient dynamics as a feature transformation for a trained readout.
1 math bridge →
Biological substrates
4 concepts
Biological neural cultures
Study dissociated or patterned neuronal networks as living, variable experimental substrates.
1 math bridge →
Brain organoids and organoid intelligence
Separate brain-organoid models from the proposed research program that seeks to use them for computation.
0 math bridges →
Molecular and DNA computing
Encode bounded computational states and operations in molecules and laboratory transformations.
1 math bridge →
Synthetic biological circuits
Engineer cellular components to implement bounded sensing, logic, memory, or control functions.
0 math bridges →
Interfaces and hybrid systems
3 concepts
Bioelectronic interfaces
Translate between biological activity and electronic measurement or stimulation under explicit coupling assumptions.
0 math bridges →
Multielectrode arrays and stimulation
Record extracellular activity and deliver electrical stimulation through spatially arranged electrodes.
1 math bridge →
Hybrid bioelectronic systems
Create closed loops in which living and engineered components measure, transform, and influence one another.
0 math bridges →
Evaluation and governance
3 concepts
Benchmarking, energy, and task equivalence
Compare systems only after aligning tasks, correctness, boundaries, amortization, and excluded costs.
1 math bridge →
Reproducibility, variability, and controls
Treat hardware variation, software versions, biological batches, and analysis choices as first-class experimental inputs.
1 math bridge →
Biosafety, ethics, and governance
Govern human-cell provenance, consent, welfare uncertainty, biosafety, dual use, claims, and disposal across the full lifecycle.
0 math bridges →
Machine-readable evidence contracts
The registry publishes all concepts, comparisons, source boundaries, mathematical bridges, and non-claims.