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This paper targets the problem of encoding information into binary cellassemblies. Spiking neural networks and k-winners-take-all models are twocommon approaches, but the first is hard to use for information processing andthe second is too simple and lacks important features of the first. We presentan intermediate model that shares the computational ease of kWTA and has moreflexible and richer dynamics. It uses explicit inhibitory neurons to balanceand shape excitation through an iterative procedure. This leads to a recurrentinteraction between inhibitory and excitatory neurons that better adapts to theinput distribution and performs such computations as habituation,decorrelation, and clustering. To show these, we investigate Hebbian-likelearning rules and propose a new learning rule for binary weights with multiplestabilization mechanisms. Our source code is publicly available.

Formation of cell assemblies with iterative winners-take-all computation and excitation-inhibition balance