![]() | Spike Train Analysis Toolkit |
Options and parameters are passed to the toolkit functions by way of a specialized data structure. The members of this data structure are described below. Items that only apply to multineuron analysis are in pink.
Default selection in blue.
Name | Description | Options | Method | ||||
---|---|---|---|---|---|---|---|
direct | metric | binless | ctwmcmc | ||||
entropy_estimation_method | Array of entropy estimation methods | plugin | Plug-in | × | × | × | × |
tpmc | Treves-Panzeri-Miller-Carlton | ||||||
jack | Jackknife | ||||||
ma | Debiased Ma bound | ||||||
bub | Best upper bound | ||||||
chaoshen | Chao-Shen | ||||||
ww | Wolpert-Wolf | ||||||
nsb | Nemenman-Shafee-Bialek | ||||||
variance_estimation_method | Array of variance estimation methods | nsb_var | Variance in the NSB estimate | × | × | × | × |
jack | Jackknife | ||||||
boot | Bootstrap | ||||||
unoccupied_bins_strategy See matrix2hist2d for description of options. | Strategy for dealing with unoccupied bins | -1 | Ignore unoccupied bins | × | × | ||
0 | Use an unoccupied bin only if its row and column are occupied | ||||||
1 | Use all bins | ||||||
sum_spike_trains See directbin for description of options. | Should simultaneous spike trains be summed? | 0 | Do not sum across trials | × | |||
1 | Sum across trials | ||||||
permute_spike_trains See directbin for description of options. | Should permuted versions of simultaneous spike trains be considered identical? | 0 | Take into account spike train origin | × | |||
1 | Disregard spike train origin | ||||||
metric_family See metricdist for description of options. | Which family of metrics to use | 0 | Dspike | × | |||
1 | Dinterval | ||||||
parallel See metricdist for description of options. | Whether or not to use the "all-parameter" method | 0 | Single parameter (default if shift_cost only has one element) | × | |||
1 | All parameter (default if shift_cost has multiple elements) | ||||||
warping_strategy See binlesswarp for description of options. | Warping strategy | 0 | Linear scaling | × | |||
1 | Uniform spacing | ||||||
stratification_strategy See binlessinfo for description of options. | Stratification strategy | 0 | Single stratum | × | |||
1 | Stratum for each spike count | ||||||
2 | Stratum for each spike count; all spike trains with more than max_embed_dim -min_embed_dim spikes go into a single stratum | ||||||
singleton_strategy See binlessinfo for description of options. | Singleton counting strategy | 0 | Ignore | × | |||
1 | Include | ||||||
legacy_binning See directbin for description of options. | Legacy binning flag | 0 | Use the current binning method. | × | |||
1 | Use the legacy binning method. | ||||||
h_zero See ctwmcmctree for description of options. | Deterministic node flag | 0 | Weight deterministic nodes. | × | |||
1 | Do not weight deterministic nodes. | ||||||
tree_format See ctwmcmctree for description of options. | CTW tree format | none | Do not output tree(s) (see ctwmcmcbridge for details). | × | |||
cell | Output tree(s) as cell array. | ||||||
struct | Output tree(s) as struct array. | ||||||
recording_tag See binlessembed for description of options. You do not need to set this option if you use the top level function binless . | Recording Tag | episodic | Data is episodic (e.g., spike trains). | × | |||
continuous | Data is continuous (e.g. LFP). |
Name | Description | Type | Range | Default | Method | |||
---|---|---|---|---|---|---|---|---|
direct | metric | binless | ctwmcmc | |||||
start_time | Starting time for analysis in seconds | double | < end_time | max of all spike train start times | × | × | × | × |
end_time | Ending time for analysis in seconds | double | > start_time | min of all spike train end times | × | × | × | × |
counting_bin_size | Counting bin size in seconds | double | > 0 | end_time -start_time | × | × | ||
words_per_train | Number of words per spike train in each trial | integer | > 0 | 1 | × | |||
letter_cap | Cap the maximum letter allowed in a bin | integer | > 0 | ∞ | × | × | ||
shift_cost | Cost metric in 1/seconds (may be a vector of such values) | double | ≥ 0 | 1/(end_time -start_time ) | × | |||
label_cost | Cost of changing a spike label (may be a vector of such values) | double | ≥ 0 and ≤ 2 | 0 | × | |||
clustering_exponent | Clustering exponent | double | — | -2 | × | |||
start_warp | Starting time for warped spike trains | double | < end_warp | -1 | × | |||
end_warp | Ending time for warped spike trains | double | > start_warp | 1 | × | |||
min_embed_dim | Minimal embedding dimension for episodic data | integer | ≤ max_embed_dim | 1 | × | |||
max_embed_dim | Maximal embedding dimension for episodic data | integer | ≥ max_embed_dim | 2 | × | |||
cont_min_embed_dim | Minimal embedding dimension for continuous data | integer | ≤ cont_max_embed_dim | 0 | × | |||
cont_max_embed_dim | Maximal embedding dimension for continuous data | integer | ≥ cont_max_embed_dim | 2 | × | |||
beta | KT ballast parameter | double | > 0 | 1/(largest value in binned data plus one) | × | |||
gamma | Weighting between tree node and its children | double | > 0 and < 1 | 0.5 | × | |||
max_tree_depth | Maximum tree depth | integer | > 0 | 1000 | × | |||
memory_expansion | Ratio by which to reallocate tree memory | double | ≥ 1 | 1.61 | × | |||
nmc | Number of Monte Carlo samples | integer | ≥ 0 | 199 | × | |||
mcmc_iterations | Absolute number of MCMC iterations | integer | > 0 | 100 | × | |||
mcmc_max_iterations | Maximum number of MCMC iterations | integer | ≥ mcmc_iterations | 105 | × | |||
mcmc_min_acceptances | Minimum number of MCMC acceptances | integer | > 0 | 20 | × |