Eukaryotic life cycles alternate between haploid and diploid phases and in phylogenetically diverse unicellular eukaryotes, expression of paralogous homeodomain genes in gametes primes the haploid-to-diploid transition. In the unicellular Chlorophyte alga Chlamydomonas KNOX and BELL TALE-homeodomain genes mediate this transition. We demonstrate that in the liverwort Marchantia polymorpha paternal (sperm) expression of three of five phylogenetically diverse BELL genes, MpBELL234, and maternal (egg) expression of both MpKNOX1 and MpBELL34 mediate the haploid-to-diploid transition. Loss-of-function alleles of MpKNOX1 result in zygotic arrest, whereas loss of either maternal or paternal MpBELL234 results in variable zygotic and early embryonic arrest. Expression of MpKNOX1 and MpBELL34 during diploid sporophyte development is consistent with a later role for these genes in patterning the sporophyte. These results indicate that the ancestral mechanism to activate diploid gene expression was retained in early diverging land plants and subsequently co-opted during evolution of the diploid sporophyte body.

The development of binocular vision is an active learning process comprising the development of disparity tuned neurons in visual cortex and the establishment of precise vergence control of the eyes. We present a computational model for the learning and self-calibration of active binocular vision based on the Active Efficient Coding framework, an extension of classic efficient coding ideas to active perception. Under normal rearing conditions with naturalistic input, the model develops disparity tuned neurons and precise vergence control, allowing it to correctly interpret random dot stereograms. Under altered rearing conditions modeled after neurophysiological experiments, the model qualitatively reproduces key experimental findings on changes in binocularity and disparity tuning. Furthermore, the model makes testable predictions regarding how altered rearing conditions impede the learning of precise vergence control. Finally, the model predicts a surprising new effect that impaired vergence control affects the statistics of orientation tuning in visual cortical neurons.