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 http://github.com/brain-score/candidate_models,
allows scoring candidate models of the brain on a range of assemblies and metrics.
Recommended for most users. Use Brain-Score as a library. You will need Python >= 3.6 and pip >= 18.1.
pip install git+https://github.com/brain-score/brain-score
To contribute to Brain-Score, please send in a pull request.
import brainscore data = brainscore.get_assembly("dicarlo.Majaj2015") data > <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.
|RESULTCACHING_HOME||directory to cache results (benchmark ceilings) in,