Edmond and Lily Safra Center for Brain Sciences and the Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, Israel.
PLoS Comput Biol. 2013;9(8):e1003179. doi: 10.1371/journal.pcbi.1003179. Epub 2013 Aug 22.
It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.
人们普遍认为，大脑中的联想记忆依赖于多稳态突触动力学，这种动力学使突触能够在较长时间内保持其值。然而，多稳态动力学不仅限于突触。特别是，一些遗传调控网络的动力学是多稳态的，这增加了一种可能性，即即使没有神经系统，单个细胞也能够学习关联。在这里，我们研究了一个具有双稳态元件和随机动力学的标准遗传调控网络模型。我们证明了这样的遗传调控网络模型能够学习多个、通用、重叠的关联。网络的容量（定义为可以同时存储和检索的关联数量）与遗传调控网络中双稳态元件的数量的平方根成正比。此外，我们计算了克隆细胞群体（如细菌菌落或组织中的细胞）存储关联的能力。我们表明，即使细胞不相互作用，群体存储关联的能力也大大超过单个细胞，并且与双稳态元件的数量成正比。因此，我们表明，即使是单个细胞也具有学习关联的计算能力，而当这些细胞形成群体时，这种能力会大大增强。
