CaImAn Spike Extraction¶

This tool uses 0.5 compute credits per hour.

Overview¶

The CaImAn spike extraction tool infers and extracts neural spikes and biosensor kinetics (e.g. GCaMP activity) from noisy calcium imaging fluorescent time-series data using constrained deconvolution¹ as implemented in the open-source package CaImAn².

Inputs¶

The workflow supports individual cell sets and cell set series. When a cell set series is provided, each cell set will be processed independently and the number of output files will match the number of input files.

The input files and processing parameters are listed in the table below.

Parameters¶

Parameter	Required?	Default	Description
Cell Set(s)	True	N/A	Single cell set or cell set series
bl	False	auto	Fluorescence baseline value. If set to 'auto', it will be estimated from the data.
c1	False	auto	Value of calcium at time 0. If set to 'auto', it will be set based on the data.
g	False	auto	Parameters of the autoregressive process that models the fluorescence impulse response. The parameters should be specified as comma-separated value (e.g. 5,10). If set to 'auto', it will be estimated from the data.
sn	False	auto	Standard deviation of the noise distribution. If set to 'auto', it will be estimated from the data.
p	False	1	Order of the autoregressive model used to deconvolve the indicator temporal dynamics
method_deconvolution	False	oasis	Method for solving the constrained deconvolution of temporal traces
bas_nonneg	False	True	If True, a non-negative baseline will be used. If False, the baseline will be greater than or equal to the minimum value, which could be negative.
noise_method	False	logmexp	Power spectrum averaging method used for noise estimation
noise_range	False	0.25,0.5	Range of normalized frequencies over which to compute the power spectrum for noise estimation. The range should be specified as 'fmin,fmax', where fmin and fmax refer to the minimum and maximum of the normalized frequency range to use (e.g.: 0.25,0.5).
s_min	False	N/A	Minimum spike threshold amplitude. For negative values the threshold is abs(s_min) * sn * sqrt(1-g). If unspecified, the standard L1 penalty is used. If set to 0, the threshold is determined automatically such that RSS <= sn^2 T.
optimize_g	True	False	If True, the time constants will be optimized. This applies only to the 'oasis' deconvolution method.
fudge_factor	False	0.96	Bias correction factor for the discrete time constants
lags	False	5	Number of lags for estimating the time constants of the autoregressive model. This should be an integer between 1 and the number of timepoints in the data.
solvers	False	ECOS,SCS	Primary and secondary solvers to use with the 'cvxpy' deconvolution method. This should be specified as 'solver1,solver2', where solver1 and solver1 refer to the primary and secondary solvers (e.g.: ECOS,SCS). The solvers should be one of the following values: 'ECOS', 'SCS', and 'CVXOPT'.

Outputs¶

The workflow will produce the outputs listed below.

Denoised Cell Set(s)¶

isxd cell set(s) containing the spatial footprints and denoised temporal traces.

Denoised cell set. (left) Spatial footprints of cells. Up to 20 cells, chosen across a range of SNRs, are highlighted. The corresponding fluorescent activity traces for the highlighted cells can be seen in the traces preview. (right) Denoised temporal traces of cells. Temporal activity of selected individual cells, chosen pseudorandomly to represent the range of SNRs (and sorted highest to lowest).

Spike Event Set(s)¶

isxd event set(s) containing the neural events identified by CaImAn.

Detected neural events. (top) Raster plot of detected events for all neurons; each neuron is a row. (middle) Average population event rate over time. (bottom left) Histogram of event rate. (bottom right) Histogram of mean inter-event interval.