Evaluation
Neural Predictivity
2018ActivePublished: 29 September 2026Updated: 29 September 2026Published
Key
innovation
A NeuroAI metric: how well a model activations predict measured neural (brain) responses — usually via linear regression (an encoding model).
Category
Evaluation
Abstraction level
Primitive
Operation level
Evaluation (runtime)Model
Use cases
Rating models as "models of the brain" (Brain-Score)Selecting layers that best map cortexNeuroAI research on model–brain agreementAnalysing brain-guided methods (NARI/NARF)
How it works
For a shared stimulus set, one collects model activations and neural responses. An encoding model (ridge regression) is fit from activations to neural activity on part of the data, and on held-out data the correlation (e.g. Pearson) between prediction and measurement is computed. The result, often normalised by measurement noise (noise ceiling), is the neural predictivity of a layer/model.
Problem solved
One needs to quantitatively assess whether a model processes information like the brain. Neural predictivity gives a number: how well the model representations predict real neural activity.
Components
Encoding model (ridge regression)Prediction
A mapping from model activations to neural activity.
Cross-validation + noise ceilingScoring
Held-out evaluation, normalised by measurement noise.
Evolution
2014
Deep models as predictors of visual cortex (Yamins, Khaligh-Razavi)
Inflection point2018
Brain-Score formalises neural predictivity as a benchmark
Inflection point