PerichMillerPopulation2018#

class torch_brain.datasets.PerichMillerPopulation2018(root=None, recording_ids=None, transform=None, dirname='perich_miller_population_2018', **kwargs)[source]#

Bases: torch_brain.datasets.mixins.SpikingDatasetMixin, torch_brain.datasets.dataset.Dataset

Motor cortex (M1 and PMd) spiking activity and reaching kinematics from four macaques performing center-out and random target reaching tasks. The monkeys were trained to move a cursor from a central target to one of eight peripheral targets arranged in a circle.

Preprocessing

To download and prepare this dataset, run

brainsets prepare perich_miller_population_2018

Tasks: Center-Out and Random Target

Brain Regions: M1, PMd

Dataset Statistics

  • Subjects: 4

  • Total Sessions: 111 (84 Center-Out, 27 Random Target)

  • Total Units: 10,410

  • Events: ~11.1M spikes, ~15.5M behavioral timestamps

References

Perich, M. G., Miller, L. E., Azabou, M., & Dyer, E. L. Long-term recordings of motor and premotor cortical spiking activity during reaching in monkeys. Neuron. Dataset: Dandiset 000688.

Parameters:

  • root (Optional[str]) – Root directory for the dataset. Defaults to processed_dir from brainsets config.

  • recording_ids (Optional[list[str]]) – List of recording IDs to load.

  • transform (Optional[Callable]) – Data transformation to apply.

  • dirname (str) – Subdirectory for the dataset. Defaults to “perich_miller_population_2018”.

get_sampling_intervals(split=None)[source]#

Returns a dictionary of sampling intervals for each recording. This represents the intervals that can be sampled from each session.

This dictionary will be used by torch_brain’s Samplers to know where to sample from.

The default method returns intervals containing the entire domain of each recording. This behavior can be overridden by subclasses to give out custom sampling intervals.

Returns:

Dictionary mapping recording IDs to their time domain intervals.