Replay-Content-Classification

Replay Content Classification is a python package for categorizing hippocampal replay events using multiunit spiking activity. Multiunit spiking activity can be more informative than sorted spikes because there is no need to distinguish between neurons, meaning that we can take advantage of partial information from neurons that are not well separated in terms of electrophysiological signal.

This package also provides:

• Metrics for the confidence of classification
• Convenient functions for diagnostic plotting

See the notebooks (#1, #2) for more information on how to use the package.

References

Deng, X., Liu, D.F., Karlsson, M.P., Frank, L.M., and Eden, U.T. (2016). Rapid classification of hippocampal replay content for real-time applications. Journal of Neurophysiology 116, 2221-2235.

Installation

1. Install miniconda (or anaconda) if it isn't already installed. Type into bash (or install from the anaconda website):

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r

2. Go to the local repository (.../Replay-Content-Classification) and install the anaconda environment for the repository. Type into bash:

conda update -q conda
conda info -a
conda env create -f environment.yml
source activate Replay-Content-Classification
python setup.py develop

Example Usage

1. Classify replays into Inbound-Forward, Inbound-Reverse, Outbound-Forward, Outboud-Reverse using the clusterless decoder:

from replay_classification import ClusterlessDecoder

decoder = ClusterlessDecoder(
    position=linear_distance,
    trajectory_direction=trajectory_direction,
    spike_marks=test_marks,
    replay_speedup_factor=16,
)

decoder.fit()

results = decoder.predict(ripple_marks)

2. Classify replays using sorted spikes:

from replay_classification import SortedSpikeDecoder

decoder = SortedSpikeDecoder(
    position=linear_distance,
    trajectory_direction=trajectory_direction,
    spikes=test_spikes,
    replay_speedup_factor=16,
)

decoder.fit()

results = decoder.predict(ripple_spikes)

3. Decode only Inbound and Outbound by specifying the observation and state transition order:

from replay_classification import ClusterlessDecoder

decoder = ClusterlessDecoder(
    position=linear_distance,
    trajectory_direction=trajectory_direction,
    spike_marks=test_marks,
    observation_state_order=['Inbound', 'Outbound'],
    state_transition_state_order=['Inbound', 'Outbound'],
    state_names=['Inbound', 'Outbound'],
    initial_conditions='Uniform',
)

decoder.fit()

results = decoder.predict(ripple_spikes)

4. Decode replay position:

from replay_classification import ClusterlessDecoder

decoder = ClusterlessDecoder(
    position=linear_distance,
    trajectory_direction=np.ones_like(position), spike_marks=test_marks,
    observation_state_order=[1],
    state_transition_state_order=[1],
    state_names=['replay_position'],
    initial_conditions='Uniform',
)

decoder.fit()

results = decoder.predict(ripple_spikes)