Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information represented by polychronous neuronal groups under ongoing activity. Here, in computational models, we show that the frequently activated polychronous neuronal groups can be learned by readout neurons with joint weight-delay spike-timing-dependent plasticity. The identity of neurons in the group and their expected spike timing at millisecond scale can be recovered from the incoming weights and delays of the readout neurons. The detection performance can be further improved by two layers of readout neurons. In this way, the detection of polychronous neuronal groups becomes an intrinsic part of the network, and the readout neurons become differentiated members in the group to indicate whether subsets of the group have been activated according to their spike timing. The readout spikes representing this information can be used to analyze how PNGs interact with each other or propagate to downstream networks for higher-level processing.

It has been proved that in one-dimensional cases, the weights of Kohonen's self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difficult to analyze Kohonen's SOMs in multidimensional cases; however, it has been conjectured that similar results seem to be obtainable in multidimensional cases. In this note, we show that in multidimensional cases, even though the weights are ordered at some time, it is possible that they become disordered in the future.