Institute of Evolution, Centre for Ecological Research, Tihany, Hungary.
Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Budapest, Hungary.
PLoS Comput Biol. 2020 Nov 30;16(11):e1008425. doi: 10.1371/journal.pcbi.1008425. eCollection 2020 Nov.
There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental "devo-engrams" are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms.
人们越来越意识到进化学习可能产生发展记忆。遗传调控和神经网络可以通过类似的形式主义来建模,这就提出了一个重要的问题,即原则、过程和性能中的富有成效的类比。我们研究了过去表型的各种发展记忆的形成和持久性,探讨了记住的过去表型的数量如何随网络规模而变化,以及通过类似赫布规则存储的记忆的程度,以及这些发展“devo-engrams”对网络干扰(优雅降级)的稳健性。神经和遗传调控网络之间的类比并不是表面上的,因为它允许在过去彼此独立开发的领域之间进行知识转移。众所周知的壮观表型辐射的例子可以部分用这种方式来解释。