Long-term memory storage by the interaction of structural and synaptic plasticity in recurrent neuronal networks

为了在复杂和不断变化的环境中生存，人类和动物通过形成记忆从过去的经验中学习。因此，触发记忆形成的经历会引起大脑网络中的神经元激活，导致神经元之间的连接发生变化，这些神经元代表了对这种经历的记忆。这种记忆项目的保留被认为取决于这些变化的持续性。然而，最近的实验表明，突触每天都在产生和移除，并且大部分突触在数周的时间尺度上交换。这就提出了一个问题，可以持续数年甚至数十年的长期记忆可以在这样一个可变的基底上存储和维持多久。具体来说，它必须澄清，尽管突触周转，长期记忆可以从神经元活动和不同的可塑性过程之间的相互作用，涉及改变神经元的连接，即结构可塑性，描述的创建和删除的突触，和突触可塑性，描述的突触传递效率的变化出现。为了研究这种复杂的相互作用在网络层面上，我们提出了一个理论研究所涉及的过程的数学模型。我们假设活动和所描述的可塑性机制之间的相互作用导致细胞群体中的集体动力学，这可以通过传入刺激来控制：在高刺激水平下，神经元活动和连接之间的正反馈导致形成高度互连的细胞簇-所谓的细胞组装体-被认为是记忆项目。在低刺激水平下，这些细胞之间的连接将被修剪，从而记忆被遗忘。在中等刺激水平下，我们预计已经形成的组装体可以保持很长一段时间。更准确地说，我们假设这些组件中的活动和连接之间的正反馈使它们稳定下来，以防止突触周转，从而使它们可以保留在长期记忆的时间尺度上。为了验证这些假设，我们将使用网络模拟和分析数学方法，这将使我们能够确定组装稳定性的决定因素。这项研究的结果将是一个动态的记忆模型，它可以形成或删除记忆的输入依赖性的方式，并保持他们的长期记忆的时间尺度，尽管突触营业额。

In order to survive in complex and changing environments, humans and animals learn from past experiences by forming memories. Hereby, an experience, which triggers memory formation, causes neuronal activation in the brains networks leading to changes of the connections between neurons, which represent the memory of this experience. The retention of this memory item is believed to depend on the persistence of these changes. However, recent experiments show that synapses are created and removed on a daily basis and large fractions of synapses are exchanged on a timescale of weeks. This poses the question, how long term-memories, which can persist for years and decades, can be stored and maintained on such a variable substrate. Specifically, it has to be clarified how, in spite of synaptic turnover, long-term memory can emerge from the interaction between neuronal activities and different plasticity processes involved in changing the neuronal connectivity namely structural plasticity, describing the creation and removal of synapses, and synaptic plasticity, describing changes of the synaptic transmission efficacies. To investigate this complex interaction on the network level, we propose a theoretical study using mathematical models of the involved processes. We hypothesize that the interaction between activity and the described plasticity mechanisms leads to a collective dynamics in populations of cells, which can be controlled by the incoming stimulation: At high stimulation levels, a positive feedback between neuronal activities and connectivity leads to the formation of highly interconnected clusters of cells - so-called cell assemblies - which are considered as memory items. At low stimulation levels, the connections between these cells will be pruned, such that the memory is forgotten. At intermediate stimulation levels, we expect that already formed assemblies can be maintained for long period of time. More precisely, we hypothesize that the positive feedback between activity and connectivity in these assemblies stabilizes them against synaptic turnover, such that they can be retained on the timescale of long-term memory. To test these hypotheses, we will use both network simulations and analytical mathematical methods, which will allow us to identify the determinants of assembly stability. The outcome of this study will be a dynamic memory model which can form or remove memories in an input-dependent fashion and maintain them on the timescale of long-term memory despite synaptic turnover.