NeuroGenesysGlossary
Last updated • By Will Schulz
Short, sourced definitions of the terms that come up most across NeuroGenesys research, each with the answer first and links to the papers, posts and topic hubs that use it.
Which terms are defined here?
- Glossary • ε (epsilon)Verifier exploitabilityVerifier exploitability (ε) is the rate at which a verifier, such as a reward model, judge or checker, gives a high score to a wrong candidate. In Best-of-N sampling it decides whether more samples help or hurt; accuracy alone does not.
- Glossary • CPOCo-packaged opticsCo-packaged optics (CPO) puts the optical transceiver, the engine that converts electrical signals to light, on the same package as, or right next to, a switch or compute chip instead of in a pluggable module at the front panel. Shortening the electrical path cuts interconnect power in AI clusters.
- GlossaryNeural dataNeural data is information generated by measuring the activity of a person's nervous system, for example from EEG headbands, implanted brain-computer interfaces or other neurotechnology. Several US states now treat it as sensitive personal data, but they disagree on whether it covers the peripheral nervous system and data inferred from other signals.
- Glossary • OIOrganoid intelligenceOrganoid intelligence (OI) is a research field, named in 2023, that aims to use lab-grown 3D brain organoids (stem-cell-derived neural tissue) interfaced with electrodes and machine learning as a form of biological computing. It also studies learning and memory in a dish under an embedded ethics program.
- GlossaryBrain foundation modelA brain foundation model is a large neural network pretrained, often self-supervised, on many hours of neural recordings across sessions, subjects or devices. It can then be adapted with little data to new decoding tasks or new people, instead of training a bespoke decoder for every patient and session.
- Glossary • AIMCAnalog in-memory computingAnalog in-memory computing (AIMC) performs matrix–vector multiplication inside the memory array that stores a neural network's weights. It uses device physics, Ohm's law for multiplication and Kirchhoff's current law for summation, instead of moving weights to a separate processor, which targets the energy cost of data movement in AI inference.
