Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits
Fig 5
Top, general workflow in FilterNet. In case of a visual stimulus, a movie is processed by an array of filters distributed in the visual space. Each filter convolves the frames of the movie with the spatial and temporal kernels, performs rectification, and outputs a time dependent firing rate representing the response of the filter to the movie, which can be also converted to instantiations of spike trains. Bottom, illustration of inputs and outputs of FilterNet. Inputs include specifications of parameters such as duration, frame rate, and file locations, as well as contents of the files describing the input patterns and filter properties and distributions. The “run_filternet.py” script is used to carry out the calculations. The output may contain the time series of time-dependent firing rates for each filter and spike trains (illustrated) generated from these time series.