Temporal competitive learning induced in neural networks by spike timing-dependent plasticity

In this paper, we introduce a novel computational model of spike timing-dependent plasticity (STDP) that can induce competitive learning in neural networks, and hence can work as an efficient coding mechanism for temporal correlated neural activities. Most computational STDP models use either additive or multiplicative learning rules. Usually additive rules induce competition in many-to-one networks, yet they not only suffer from instability and slow converging speed, but also cannot be extended properly to many-to-many networks. Multiplicative rules on the other hand can reach stable results in a shorter time, but they do not cause competition in any kind of networks. So these models cannot readily explain complex phenomena in neural processing. Here we attack this problem by introducing a modified multiplicative STDP model with a mechanism called dasiaglobal depressionpsila, which induces competitive learning in many-to-many networks while preserves the virtues of original multiplicative models. Moreover, this model tends to group presynaptic neurons according to their firing patterns. Specifically, an ensemble of presynaptic neurons with correlated activities may collectively form strong connections with one postsynaptic neuron, while a different ensemble may connect with another postsynaptic neuron. Overall this model performs pattern grouping according to the input neural activities. We prove this point theoretically and experimentally in this paper.