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A framework for evaluating models on integrative brain measurements
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brainscore standardizes the interface between neuroscience metrics and the data they operate on. Brain recordings (termed "assemblies", e.g. neural or behavioral) are packaged in a standard format. This allows metrics (e.g. neural predictivity, RDMs) to operate on many assemblies without having to be re-written. Together with, brainscore allows scoring candidate models of the brain on a range of assemblies and metrics.

Quick setup

Recommended for most users. Use Brain-Score as a library. You will need Python >= 3.6 and pip >= 18.1.

pip install git+

To contribute to Brain-Score, please send in a pull request.

Basic Usage

import brainscore
data = brainscore.get_assembly("dicarlo.Majaj2015")
> <xarray.NeuronRecordingAssembly 'dicarlo.Majaj2015' (neuroid: 296, presentation: 268800, time_bin: 1)>
> array([[[ 0.060929],
>         [-0.686162],
>         ...,
> Coordinates:
>   * neuroid          (neuroid) MultiIndex
>   - neuroid_id       (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...
>   ...

from brainscore.metrics.rdm import RDM
metric = RDM()
score = metric(assembly1=data, assembly2=data)
> Score(aggregation: 2)>
> array([1., 0.])
> Coordinates:
>   * aggregation    'center' 'error'

Some steps may take minutes because data has to be downloaded during first-time use.

More examples can be found in the examples directory.

Environment Variables

Variable Description
RESULTCACHING_HOME directory to cache results (benchmark ceilings) in, ~/.result_caching by default (see


MIT license


`ValueError: did not find HDF5 headers` during netcdf4 installation pip seems to fail properly setting up the HDF5_DIR required by netcdf4. Use conda: `conda install netcdf4`
repeated runs of a benchmark / model do not change the outcome even though code was changed results (scores, activations) are cached on disk using Delete the corresponding file or directory to clear the cache.
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