Quickstart

Run the analysis using the Jupyter notebook at notebook/workflow_2D.ipynb.

Required Files

Neural data directory (e.g., minian/ output):

• {trace_name}.zarr: calcium traces (e.g., C.zarr or C_lp.zarr)

• A.zarr: spatial footprints for cell overlay (optional)

• max_proj.zarr: max projection image for visualization (optional)

Timestamp and position files:

• neural_timestamp.csv: neural frame timestamps

• behavior_position.csv: animal position with bodypart columns (DeepLabCut format)

• behavior_timestamp.csv: behavior frame timestamps

Configuration files:

• config.yaml: analysis parameters

• data_paths.yaml: paths to your data files

Setup

1. Create data paths config

Create data_paths.yaml with paths relative to this file:

data_paths.yaml

id: your_data
mio_model: placecell.config.DataPathsConfig
mio_version: 0.8.1
# Directory containing zarr files (C.zarr, A.zarr, max_proj.zarr)
neural_path: path/to/minian_output
neural_timestamp: path/to/neural_timestamp.csv
behavior_position: path/to/behavior_position.csv
behavior_timestamp: path/to/behavior_timestamp.csv

2. Create analysis config

Create config.yaml with analysis parameters:

config.yaml

id: your_config
mio_model: placecell.config.AnalysisConfig
mio_version: 0.8.1
neural:
  id: neural
  fps: 20.0
  trace_name: C
  oasis:
    id: oasis
    g: [1.60, -0.63]
    baseline: p10
    penalty: 0
behavior:
  id: behavior
  behavior_fps: 20.0
  bodypart: LED
  speed_threshold: 10.0
  speed_window_frames: 5
  spatial_map_2d:
    id: spatial_map_2d
    bins: 50
    min_occupancy: 0.05
    occupancy_sigma: 3
    activity_sigma: 3
    n_shuffles: 500
    p_value_threshold: 0.05

3. Run the notebook

Open notebook/workflow_2D.ipynb in Jupyter Lab, set CONFIG_PATH and DATA_PATH to your config files, and run all cells.

Output

The workflow displays an occupancy preview:

Then displays the stability vs significance plot:

And finally launches the interactive place cell viewer:

Next Steps

• See Pipeline Details for how the analysis works