PredictiveCancellation_CerebellumLikeMON/DataScripts/SpykingCircus.params

[data]
file_format    = raw_binary            # Can be raw_binary, openephys, hdf5, ... See >> spyking-circus help -i for more info
data_dtype     = float32
nb_channels    = 1
sampling_rate  = 16666.666666666668
stream_mode    = None                  # None by default. Can be multi-files, or anything depending to the file format
mapping        = mon.prb  # Mapping of the electrode (see http://spyking-circus.rtfd.ord)
suffix         = done                      # Suffix to add to generated files
global_tmp     = True                  # should be False if local /tmp/ has enough space (better for clusters)
overwrite      = True                  # If you want to filter or remove artefacts on site. Data are duplicated otherwise
blosc_compress = False                 # For clusters only, to compress data shared among nodes

[detection]
radius         = auto       # Radius [in um] (if auto, read from the prb file)
N_t            = 5          # Width of the templates [in ms]
spike_thresh   = 5              #!! AUTOMATICALLY EDITED: DO NOT MODIFY !!
peaks          = both    # Can be negative (default), positive or both
matched-filter = False      # If True, we perform spike detection with matched filters
matched_thresh = 5          # Threshold for detection if matched filter is True
alignment      = True       # Realign the waveforms by oversampling
isolation      = False      # Enforce individual snippets to be isolated [experimental]

[filtering]
cut_off        = 300, auto # Min and Max (auto=nyquist) cut off frequencies for the band pass butterworth filter [Hz]
filter         = True      # If True, then a low-pass filtering is performed
remove_median  = False     # If True, median over all channels is substracted to each channels (movement artefacts)

[whitening]
chunk_size     = 60        # Size of the data chunks [in s]
safety_time    = 1         # Temporal zone around which templates are isolated [in ms]
temporal       = False     # Perform temporal whitening
spatial        = True      # Perform spatial whitening
max_elts       = 50000     # Max number of events per electrode (should be compatible with nb_elts)
nb_elts        = 1       # Fraction of max_elts that should be obtained per electrode [0-1]
output_dim     = 5         # Can be in percent of variance explain, or num of dimensions for PCA on waveforms

[clustering]
extraction     = median-raw # Can be either median-raw (default), median-pca, mean-pca, mean-raw, or quadratic
safety_space   = True       # If True, we exclude spikes in the vicinity of a selected spikes
safety_time    = 1          # Temporal zone around which templates are isolated [in ms]
max_elts       = 50000      # Max number of events per electrode (should be compatible with nb_elts)
nb_elts        = 1         # Fraction of max_elts that should be obtained per electrode [0-1]
nclus_min      = 0.002      # Min number of elements in a cluster (given in percentage)
max_clusters   = 10         # Maximal number of clusters for every electrodes
nb_repeats     = 3          # Number of passes used for the clustering
make_plots     = png          # Generate sanity plots of the clustering
sim_same_elec  = 3          # Distance within clusters under which they are re-merged
cc_merge       = 1      # If CC between two templates is higher, they are merged
dispersion     = (5, 5)     # Min and Max dispersion allowed for amplitudes [in MAD]
smart_search   = True       # Parameter to activate the smart search mode
smart_select   = False      # Experimental: activate the smart selection of centroids (max_clusters is ignored)
noise_thr      = 0.8        # Minimal amplitudes are such than amp*min(templates) < noise_thr*threshold
remove_mixture = True       # At the end of the clustering, we remove mixtures of templates
cc_mixtures    = 0.75       # If CC between a sum of two templates and a template is higher, it is removed

[fitting]
chunk          = 1         # Size of chunks used during fitting [in second]
gpu_only       = True      # Use GPU for computation of b's AND fitting
amp_limits     = (0.3, 30) # Amplitudes for the templates during spike detection
amp_auto       = True      # True if amplitudes are adjusted automatically for every templates
max_chunk      = inf       # Fit only up to max_chunk
collect_all    = False      # If True, one garbage template per electrode is created, to store unfitted spikes

[merging]
cc_overlap     = 0.75      # Only templates with CC higher than cc_overlap may be merged
cc_bin         = 1         # Bin size for computing CC [in ms]
correct_lag    = False     # If spikes are aligned when merging. May be better for phy usage
auto_mode      = 0         # If >0, merging will be automatic (see doc, 0.1 is a good value)

[converting]
erase_all      = True      # If False, a prompt will ask you to export if export has already been done
sparse_export  = False     # If True, data for phy are exported in a sparse format. Need recent version of phy
export_pcs     = prompt    # Can be prompt [default] or in none, all, some
export_all     = False     # If True, unfitted spikes will be exported as the last Ne templates

[extracting]
safety_time    = 1         # Temporal zone around which spikes are isolated [in ms]
max_elts       = 10000     # Max number of events per templates (should be compatible with nb_elts)
nb_elts        = 0.8       # Fraction of max_elts that should be obtained per electrode [0-1]
output_dim     = 5         # Percentage of variance explained while performing PCA
cc_merge       = 0.975     # If CC between two templates is higher, they are merged
noise_thr      = 0.8       # Minimal amplitudes are such than amp*min(templates) < noise_thr*threshold

[validating]
nearest_elec   = auto      # Validation channel (e.g. electrode closest to the ground truth cell)
max_iter       = 200       # Maximum number of iterations of the stochastic gradient descent (SGD)
learning_rate  = 1.0e-3    # Initial learning rate which controls the step-size of the SGD
roc_sampling   = 10        # Number of points to estimate the ROC curve of the BEER estimate
test_size      = 0.3       # Portion of the dataset to include in the test split
radius_factor  = 0.5       # Radius factor to modulate physical radius during validation
juxta_dtype    = uint16    # Type of the juxtacellular data
juxta_thresh   = 6         # Threshold for juxtacellular detection
juxta_valley   = False     # True if juxta-cellular spikes are negative peaks
juxta_spikes   =           # If none, spikes are automatically detected based on juxta_thresh
filter         = True      # If the juxta channel need to be filtered or not
make_plots     = png       # Generate sanity plots of the validation [Nothing or None if no plots]

[noedits]
filter_done    = True              #!! AUTOMATICALLY EDITED: DO NOT MODIFY !!