Cell assemblies in neural network are often assumed as overlapping, i.e. a neuron may belong to several of them simultaneously. We argue that network structures with overlapping cell assemblies can exhibit faster learning comparing to non-overlapping ones. In such structures newly trained assemblies take advantage of their overlaps with the already trained neighbors. The assemblies learned in such manner nevertheless preserve the ability for subsequent separate firing. We discuss the implications it may have for intensification of neural network training methods and we also propose to view this learning speed-up in a broader context of inter-assembly cooperation useful for modeling concept formation in human thinking.
['Andrei Kursin', 'Dušan Húsek', 'Roman Neruda']
Faster learning with overlapping neural assemblies