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A type of CFSheet extended to support joint sum-based normalization.
For L1 normalization, joint normalization means normalizing the sum of (the absolute values of) all weights in a set of corresponding CFs in different Projections, rather than only considering weights in the same CF.
This class makes it possible for a model to use joint normalization, by providing a mechanism for grouping Projections (see _port_match), plus a learn() function that computes the joint sums. Joint normalization also requires having ConnectionField store and return a norm_total for each neuron, and having an TransferFn that will respect this norm_total rather than the strict total of the ConnectionField's weights. At present, CFPOF_DivisiveNormalizeL1 and CFPOF_DivisiveNormalizeL1_opt do use norm_total; others can be extended to do something similar if necessary.
To enable joint normalization, you can declare that all the incoming connections that should be normalized together each have a dest_port of:
dest_port=('Activity','JointNormalize', 'AfferentGroup1'),
Then all those that have this dest_port will be normalized together, as long as an appropriate TransferFn is being used.
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joint_norm_fn = param.Callable(default= compute_joint_norm_totFunction to use to compute the norm_total for each CF in each projection from a group to be normalized jointly. |
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name = <param.parameterized.String object at 0xa9f6e6c>String identifier for this object. |
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Called by the simulation when the EventProcessor is added to the simulation. If an EventProcessor needs to have any code run when it is added to the simulation, the code can be put into this method in the subclass.
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Apply the weights_output_fns for every group of Projections. If active_units_mask is True, only active units will have their weights normalized. |
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joint_norm_fnFunction to use to compute the norm_total for each CF in each projection from a group to be normalized jointly.
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