Working definition
Neuromorphic computing is a family of hardware and software approaches that borrow selected organizational principles from nervous systems, including event-driven communication, colocated state and computation, distributed parallelism, and local adaptation. “Brain-inspired” identifies design provenance; it does not establish that a system reproduces a brain, cognition, or subjective experience.
Mechanism
- Encode activity as events or locally evolving state.
- Route sparse signals among distributed processing elements.
- Update state or weights under declared dynamics.
Measurements
- Task correctness
- Latency and throughput
- Energy under a declared system boundary
Reproducibility controls
- Version hardware, software, firmware, and analysis code.
- Declare dataset, preprocessing, random seeds, and measurement boundary.
- Report repeated runs, variation, exclusions, and failed trials.
Limits and failure modes
- No single architecture defines the field.
- Biological inspiration is not biological equivalence.
Mathematical connection
Formal structure without substrate erasure
Related mathematical concepts are named, but no direct bridge is asserted in this release.
Technical and governance sources
- [1]Taking Neuromorphic Computing to the Next Level with Loihi 2 · Intel Labs
Establishes: An official description of the Loihi 2 research chip, its programmable neuron models, event-based communication, on-chip learning support, and the Lava software framework used to construct neuromorphic applications.
Boundary: This is a vendor technical brief about a research platform. Performance and efficiency results remain workload-, configuration-, measurement-boundary-, and comparison-dependent and do not establish equivalence to biological intelligence.
- [2]SpiNNaker2 Developer Portal and Hardware Documentation · SpiNNcloud Systems and SpiNNaker2 community
Establishes: Maintained technical documentation for a many-core, event-based neuromorphic platform, including chip topology, processing elements, communication, software interfaces, and supported computational workloads.
Boundary: Architecture documentation establishes available mechanisms, not universal speed, energy, learning, biological plausibility, or production readiness. Claims require a named board, software version, workload, and system boundary.
- [3]NeuroBench: Advancing Neuromorphic Computing Through Collaborative, Fair and Representative Benchmarking · National Institute of Standards and Technology
Establishes: A community framework separating algorithm and system tracks and defining task, correctness, efficiency, and reporting procedures intended to make neuromorphic results more comparable and reproducible.
Boundary: A benchmark ranks submitted systems on declared tasks and metrics. It does not prove general intelligence, biological equivalence, safety, usefulness outside the benchmark, or superiority under unreported host and data costs.