man.1

.TH gibbsDPA5data 
.SH NAME 
BINE \- Bayesian Inference of Neuronal Ensembles
.SH SYNOPSIS
.B gibbsDPA5data 
[\fB\-\-file\fR \fIFILE\fR]
[\fB\-\-niter\fR \fIITERATIONS\fR]...

.SH DESCRIPTION

Implementation of the Bayesian inference method for detecting neuronal ensembles developed by G. Diana, T. Sainsbury and M. Meyer (see https://doi.org/10.1101/452557 for details).  
The input is a text file containing a binary matrix representing the binary activity of recorded neurons arrranged by rows.

.SH OPTIONS
.TP
.BR \-\-niter =\fIITERATIONS\fR
Set the number of Markov Chain iteration.
number of iterations of the Markov Chain
.TP
.BR \-\-trim=\fIVALUE\fR
number of MCMC steps between recorded samples. Default 1. 
.TP
.BR \-\-burn_in=\fIVALUE\fR
number of initial MCMC steps excluded
.TP
.BR \-\-assemblies=\fIVALUE\fR
itial number of assemblies
.TP
.BR \-\-seed=\fIVALUE\fR
random seed
.TP
.BR \-\-file=\fIFILE\fR
input file in matrix format [neurons]x[times] where row \fIi\fR represents the binary activity of neuron \fIi\fR.
.TP
.BR \-\-min_neur=\fIVALUE\fR
minimum number of synchronously active neurons. Default 0.
.TP
.BR \-\-min_act=\fIVALUE\fR
minimum neuronal activity (row sums). Default 0.
.TP
.BR \-\-folder=\fIFOLDER\fR
output folder - being created if not already existing 
.TP
.BR \-\-continue
uses data from previous run stored in \fIFOLDER\fR
.TP
.BR \-\-verbose
show details of the Markov Chain.