Tensor Weighted Interpolated Transfer (TWIT)
One function that is at the core of the SRS system is where the activation of a N-Dimensional map of neurons (a Tensor) should stimulate some part or all of another tensors activation. This has several parameters. Source and Target dimensions. In NumPy nomenclature this is the shape and slices of the source and target tensors. For example the source tensor A might be (4,5,6) in shape and the target B might be (7,8) in shape. They miss match on both count of dimensions and size of dimensions. Also along each axis of the larger dimension tensor (in this case A) there is a start and end weight usually between -1.0 and 1.0. We want to interpolate the weights along that dimension. One may also select a subset of either or both A and B as the connected part. The form of this is source, destination, and weight ranges for each dimension. If some part is missing it is assumed to be the whole range of the tensor dimension, or for weights is assumed to be 1.0. Here is a screen shot