General anaconda environment for all setups and data analyses
Install the latest anaconda version for your operating system (https://www.anaconda.com/products/individual).
Open the anaconda prompt and create a virtual environment via conda
conda create --name ephys --channel conda-forge python=3.10
conda activate ephys
conda update -n base -c defaults conda
conda config --add channels conda-forge
conda config --set channel_priority strict
Then installing spikeinterface. There was time when I was desperate for their bug fix so I installed spikeinterface from the source. However, it should be totally fine to install it from pypl If you want to install it from the source, then the following command will be useful. For further info, check out their github page or documentation webpage.
cd Documents\GitHub
git clone https://github.com/SpikeInterface/spikeinterface.git
cd spikeinterface
pip install -e .
cd ..
pip install git+https://github.com/NeuralEnsemble/python-neo.git
pip install git+https://github.com/SpikeInterface/probeinterface.git
[optional] ibllib has many plotting functions You can either install it via pip or to install it from source. Below is the version I forked from the source
Note: ibllib use scipy 1.12 but installing one of these packages open-ephys-python-tools zarr docker cuda-python numcodecs hdbscan needs scipy 1.13 so I hopes there is no conflict between them
pip install git+https://github.com/chiyu1203/ibllib.git
Then installing other dependencies. (open ephys python tool is for loading timestamp; zarr and numcodesc for compressing data; ipympl is for interactive plots on Jupyter notebook. Pyside 6 is for spikeinterface-gui )
pip install open-ephys-python-tools zarr docker cuda-python numcodecs hdbscan ipympl spikeinterface-gui PySide6
If you have a good GPU and wants to install kilosort. Here is the instruction under python 3.11.
python -m pip install kilosort[gui]=4.1.1
pip uninstall torch
pip3 install torch --index-url https://download.pytorch.org/whl/cu118
#conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia
Note: kilosort updates frequently so that spikeinterface can not always catch up with that. Therefore, I kept a seperate virtual environment dedicated for kilosort in case I want to use it standalone or use its GUI.
Probably dont need the packages below anymore
conda install --yes -c conda-forge -v ipython jupyter pytest
If you want to use phy for manual curation of spike sorting, create a seperate virtual environment and install it via this command
conda create -n phy2 -y python=3.11 cython dask h5py joblib matplotlib numpy pillow pip pyopengl pyqt pyqtwebengine pytest python qtconsole requests responses scikit-learn scipy traitlets
When using phy for curation, go to the directory where phy mata file is stored and type the following:
conda activate phy2
cd path/to/my/spikesorting/output
phy template-gui params.py
e.g. D:\Open Ephys\2025-02-23_20-39-04\phy_KS4>phy template-gui params.py
data collection (doing a 3 hour recording with a 32-channel probe generates data at around 25 GB, double check if neuroPC has enough capacity before starting a session, a 80-min recording with 128 channels results in 40 GB)
Surround displays are used to create panorama view. To set up surround displays, go to NVIDIA Control Panel with all 4 displays on and click Configure Surround, PhysX. Then click Configure, where you can specify which display to use and their relative position. In the case of 5840 * 1080 bezel corrected resolution, click on 5760 *1080 first resolution first and then add 40 Bezels value to each border (V1, V2). Note: it is possible to set the Refresh Rate to 240 Hz, however, we dont have/need that much fast camera and our computer has the closed-loop VR system to work on already so why bother. After that, enable Surround to let the effect kick in. Usually this will result the 4th display to turn off. To reconnect the 4th display, go to Set up multiple displays on the menus and click up the 4th display. Lastly, go to Change resolution to double check the resolution is 5840 * 1080 bezel corrected and 144 Hz refresh rate. (if ever needed, go to Set up G-SYNC to enable G-SYNC, G-SYNC Compatible). In general, this only needs to be setup once. However, the surround displays can break when turning on the computer with the surround displays stays "off" or any other reason. As long as you see Bonsai open the windows in the 4th display. That is usually the case when the surround display is off.
We use probes from CambridgeNeurotech. The mapping can be found via probeinterface, for more details, or customised adjusted json or prb file in the repository. For example, 'H10_RHD2164.prb' or 'H10_RHD2164_rev.prb'. rev means the intan chip is inserted in the opposite direction.
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Turn on air conditioning and PC to heat up the room in the morning [Optional] Use dehumilifier to reduce the humidity overnight.
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Prepare locust saline, dyes, tin foil (to wrap the dye), parafilm and a beaker with trypsin for electrode, a beaker for locust saline, 1 pipettes (for loading dye) and 1 pipetteman, distill water and isopropyl alcohol. Place those reagents on ice in a box except for locust saline. Then rinse the probe with isopropyl alcohol to clear the remaining dye (followed by rinsing with distilled water).
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place the locust on ice for 5 mins and then dissect the locust in the tube-holder. Make an insertion site on the exoskeleton. The site should be in between the antenna (and somewhere ventral to the antenna). Then slice it (either 1. in between the antenna if you want to preserve them or 2. cut off the antenna nerve and then remove most of the exoskeleton) all the way to black part (save the black part).
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Remove airsacs, cuticle, and fat body until you can see the brain. [Optional] cut off the gut: to do that, it is actually better to cut the face all the way until mouth so that there are space to find the gut (have not decided whether I should remove the gut or not). Install one or two metal bars (coated with wax) to stablise the brain (wax get removed easily if the coating was not done properly. Or the problem is due to the wax). Fix the metal tube with wax (the ventral part of the head; for the dorsal part, just use insersion site to stablise them) and then remove the neural sheet.
Note: The downside of removing the gut is that the head would become a big dry hole so I was not sure if putting the ground pin at the dorsal side of the head would work. One idea is to stuff moist kimwipe beneath the brain. Or maybe buy dura-gel would be a good idea. However, keep in mind during the surgery that if the brain does not move too much due to animal's breath, it should be possible to do the recording without removing the gut
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When removing the neural sheet, focus on dorsal to the central complex and ripe the neural sheet along DV axis (with two fine forceps). This is because the probe is inserted along along DV axis. And place parafilm on the brain
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glue the head with the headstage and insert the ground wire into the dorsal side of the head and then tape the ground pin with the head-fix bar
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Place the locust on the airball and then prepare to stain the electrode. (Remember to connect the SPL wire with the intan chip before putting them on the stereotaxis)
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Once the probe is in place (Remember to get the ground wire stick well to the metal bar. Otherwise, it will block field of view of the camera), turn on LinLab2, micromanipulators and place the microscope inside the rig to identify potential location (AP axis = x, + means anterior; LM axis = y, + means lateral, DV axis = z, + mean ventral)
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Make sure the brain is dry enough so that the dye does not diffuse when touching the surface of the brain. Once the probe touch the surface of the target area, rezero LinLab.
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Start to lower the probe with creeper function in LinLab, every 50 um (1um/s). Use Stimulus_toolkit to search for visual related neurons.
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Once a good spot is found, remove the microscope and position the third monitor. Turn off and unplug the micromanipulator and LinLab to remove additional electrical noise.
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The procedure of the recording is (1) start bonsai workflow (2) start recording on OpenEphys (3) Press C to start the camera and then R to start record film (4) start the stimulus with keypress S
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get PFA, PBS and trypsin ready when the recording is finishing or when pulling out of the electrode back (no need to dilute PFA but trypsin is stored in 10x)
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Once the recording is done, try to pull the electrode back (2um/s) (if the brian is too dry, trying saline-soaked kimiwipe to keep the brain moist before pulling the probe out)
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Add PBS into dissection stage and then move the head to the stage. Note: it is actually fine to just cut off the labula complex and save dissection time. Finally place the brain into the 4% PFA and store it at the cold room overnight.
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Use bonVision based visual paradigm: open the designed workflow and use arduino to trigger the cameras (and then turn on fictrac if this is a closed-loop experiment). Note: there is a difference feature file for camera 1557 for fictrac or for pose estimation. The camera config files are stored at C:\src\fictrac\pose_estimate\setup2025
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Use Unity based visual paradigm: Open pylon viewer and turn on camera 1557 so that we can load feature into the camera (somehow fictrac automatically use this camera so we load a particular feature file:432x480_hw in this camera for fictrac) and then close Pylon Viewer.
2-1. turn on Bonsai workflow multicamera_hw, and use the same steps to start the arduino and press R for recording. Then run fictrac and then run python_script fictrac\VR_array\socket_zmq_republisher.py. And then start OpenEphys recording >>> connect the barcode arduino >>> run Unity files (Here is a problem with which monitors Unity to target. Belows is the details).
Press Esc to stop the Unity file (this seems to take around 10 mins to save and compress the data; Unity is buggy on this pc so needs to use task manager to shut down this software). Then turn off fictrac and then turn off bonsai workflow.
In addition, I shall not think about running unity and ephys before I add this feature
when running Unity programme on Ephys setup. One big problem is that stimuli are presented when the monitors are closed up so
either I editted the unity programme to make the control scence to present at the second monitors and VR scene at the main monitorsnot easy. I shall completely remove the control scence and directly load parameters in the Swarm scene. A quick way to shut down the second monitor is just to unplug it. how to implement stimulus alignment with Ephys without using sync pulse is not clear.
The fundamental difference between mult-camera filming or not is whether to use arduino to trigger the camera. Thus, if only using one camera to capture the behaviour or when synchronising camera shutter is not important, then use feature file that does not come with _hw. The rest of the step should be more or less the same.
The analysis methods are organised into a json file, which is created from a python file. This json file tells the notebook and python script how to analysis the data (with some boolean options) and include meta information of the experiment that is not included during data collection phase.
Therefore, each project (or each experiment) has its own json file. Below explains what those analysis methods are about.
"experimenter": "chiyu",
"probe_type": "H10_stacked", # currently only supports P2 and H10_stacked
"motion_corrector": "testing", # either choose a algorithm to correct drift/motion or using "testing" to go through each method
"sorter_name": "kilosort4", # currently only supports kilosort 3 and 4 and default sorters
"analyse_good_channels_only": true, # whether to remove bad and noisy channels from analysis
"load_raw_traces": false,
"analyse_entire_recording": true, # whether to chop the recording into a portion for analysis
"save_prepocessed_file": false, # whether to compress and save dataset after it goes through bandpass filter, common median reference, remove bad channels, and slice into a portion
"load_prepocessed_file": false,
"save_sorting_file": true,
"load_sorting_file": false,
"save_analyser_to_disc": true,
"load_analyser_from_disc": false,
"extract_waveform_sparse": false,
"extract_waveform_sparse_explicit": false,
"export_to_phy": true,
"overwrite_existing_phy": true,
"load_curated_spikes": true,
"export_report": false,
"include_MUA": true, # whether to include MUA when using the script decode_spikes.py
"event_of_interest": "stim_onset", # chooose an event to align spikes with
"analyse_stim_evoked_activity": true, # if false, then entire data will be analysed. Work in progress
"experiment_name": "coherence",
"overwrite_curated_dataset": true, whether to overwrite the existing pickle file or not, which is generated from fictrac. If True, then the programme will overwrite the pickle file
"save_output": whether to save any output during data analysis. If True, then save any output
"fictrac_posthoc_analysis": whether the fictrac data comes from online tracking or posthoc analysis. If True, then entering the analysis pipeline for posthoc analysis.
"use_led_to_align_stimulus_timing": whether to use independent light source to track stimulus onset and offset. If True, then entering the analysis pipeline for that. If False, the programme will try to use timestamp coming from trials and from cameras to isolate the onset (this can only be achieved when posthoc analysis is not applied)
"align_with_isi_onset": whether to use align the stimulus-evoked response based on stimulus onset or ISI onset. If True, then aligning the data based on ISI onset (this only works with Bonsai projects where ISI is logged)
"mark_jump_as_nan": whether to use fft to identify jumping event in fictrac data and mark the event as nan
"active_trials_only": work in progress (to include walking trials)
"filtering_method": what kind of filter to apply for tracking animals
"plotting_tbt_overview": whether to plot a heat map of animal's velocity trial by trial and two histograms of animals travel distance and optomotor index.
"plotting_trajectory": whether to plot the trajectory of the animals in the experiment.
"plotting_event_related_trajectory": whether to plot a heat map of animal's trajectory after the stimulus onset
"plotting_deceleration_accerleration": whether to plot the average deceleration and accerleration onset of the animals (this is still under construction)
"plotting_position_dependant_fixation": whether to plot animal's angular velocity in response time. This is used in conflict experiment
"plotting_optomotor_response": whether to plot the animal's mean optomotor index across stimulus type and the trajectory of yaw rotation vector trial by trial.
"load_experiment_condition_from_database": this is used in the jypter notebook to see whether you want to access google-sheet-based database. If False, all animals stored in the server will be included.
"select_animals_by_condition": this is used in the jypter notebook to extract specific animals for analysis. If True, you need to specify what condition in a dictionary. If False, all animals in the database (google sheet) will be included.
"analysis_by_stimulus_type": this is used in the python script to see whether you want to plot the velocity heatmap based on stimlus type or based on time. If True, the heatmap will be stored based on stimulus type.