Due to their reduced morphology, non-photosynthetic plants have been one of the most challenging groups to delimit to species level. The mycoheterotrophic genus Monotropastrum, with the monotypic species M. humile, has been a particularly taxonomically challenging group, owing to its highly reduced vegetative and root morphology. Using integrative species delimitation, we have focused on Japanese Monotropastrum, with a special focus on an unknown taxon with rosy pink petals and sepals. We investigated its flowering phenology, morphology, molecular identity, and associated fungi. Detailed morphological investigation has indicated that it can be distinguished from M. humile by its rosy pink tepals and sepals that are generally more numerous, elliptic, and constantly appressed to the petals throughout its flowering period, and by its obscure root balls that are unified with the surrounding soil, with root tips that hardly protrude. Based on genome-wide single-nucleotide polymorphisms, molecular data has provided clear genetic differentiation between this unknown taxon and M. humile. Monotropastrum humile and this taxon are associated with different Russula lineages, even when they are sympatric. Based on this multifaceted evidence, we describe this unknown taxon as the new species M. kirishimense. Assortative mating resulting from phenological differences has likely contributed to the persistent sympatry between these two species, with distinct mycorrhizal specificity.

A model of neural network to recognize spatiotemporal patterns is presented. The network consists of two kinds of neural cells: P-cells and B-cells. A P-cell generates an impulse responding to more than one impulse and embodies two special functions: short term storage (STS) and heterosynaptic facilitation (HSF). A B-cell generates several impulses with high frequency as soon as it receives an impulse. In recognizing process, an impulse generated by a P-cell represents a recognition of stimulus pattern, and triggers the generation of impulses of a B-cell. Inhibitory impulses with high frequency generated by a B-cell reset the activities of all P-cells in the network. Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns. The selectivities to directional and non-directional patterns are realized by only adjusting excitatory synaptic weights of P-cells. In the other example, the network recognizes time series of spatial patterns, where the lengths of the series are not necessarily the same and the transitional speeds of spatial patterns are not always the same. In both examples, the HSF signal controls the total activity of the network, which contributes to exact recognition and error recovery. In the latter example, it plays a role to trigger and execute the recognizing process. Finally, we discuss the correspondence between the model and physiological findings.

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