The Feature Generation Mechanism
نویسندگان
چکیده
The feature generation mechanism (FGM) is presented, a method for growing partially connected Boolean artificial neural nets (ANNs). The FGM takes inspiration from the biological relationship between the outgrowth of neural processes and the activity-dependent calcium concentrations of the nerve cells themselves. FGM networks create a re-representation of Pattern sets presented to them during development. The FGM attempts to stabilise network growth at a point when the pattern set is represented by an efficient any-of-N encoding on its output or ’primary layer’. Simulations suggest that within a hybrid architecture the FGM approach can match the performance of an adjustable threshold competitive network (ATC), but do so with approximately a third of the free parameters required to implement the ATC.
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