` JETNET 3.0` offers the possibility to use shared weights for exploiting
translational symmetries. An example is when the input units consist of a
matrix of cells, e.g. cells in a calorimeter, where it is known
* a priori* that identical features can occur anywhere in the matrix with
translational symmetry. It is then profitable to configure the network
so that the hidden (feature) nodes cover several overlapping smaller
portions (large enough to cover the size of a subfeature) of the input
matrix. Weights connecting to corresponding parts of the different
receptive fields are then shared, i.e. assumed to be identical.

Such configurations can be achieved in ` JETNET 3.0` using the
switches ` MSTJN(23-27)`. The geometry of the input matrix
is specified with ` MSTJN(23)` and ` MSTJN(24)`. Periodic boundary
conditions are assumed if these values are negative. The geometry of the
receptive fields is specified with ` MSTJN(25)` and
` MSTJN(26)`, where . The number of hidden units
used for
each receptive field is specified by ` MSTJN(27)`. At initialization,
an array of receptive fields is generated with maximum overlap, i.e. the
fields are only shifted one (**x** or **y**) unit at a time, and new hidden
units are generated if the specified number of hidden units is less
than necessary. Any remaining hidden units are assumed to have full
connectivity from the inputs.

However, it is faster to train small network modules and later combining them into a larger network. The above solution is inefficient for large input matrices, since all weights are nevertheless updated.

Fri Feb 24 11:28:59 MET 1995