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

循环神经网络中结构和突触可塑性相互作用的长期记忆存储

基本信息

批准号：
336760888
负责人：
Dr. Michael Fauth
金额：
--
依托单位：
III. Physikalisches Institut - Biophysik
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/336760888?language=en
关键词：
Long term memory storage interaction

项目摘要

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.

为了在复杂多变的环境中生存，人类和动物通过形成记忆从过去的经验中学习。因此，一种触发记忆形成的经历，会在大脑网络中引起神经元的激活，从而导致神经元之间的连接发生变化，这些神经元代表着对这一经历的记忆。该记忆项的保留被认为取决于这些变化的持久性。然而，最近的实验表明，突触每天都在产生和移除，而且大部分突触在几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，能在如此多变的基质上储存和维持多久？具体来说，需要澄清的是，在突触更替的情况下，长期记忆是如何从神经元活动和不同可塑性过程之间的相互作用中产生的，这些可塑性过程涉及改变神经元连通性，即描述突触产生和移除的结构可塑性和描述突触传递效率变化的突触可塑性。为了在网络层面上研究这种复杂的相互作用，我们提出了一种使用相关过程的数学模型的理论研究。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了细胞群体中的集体动态，这可以通过传入的刺激来控制：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相互连接的细胞簇-所谓的细胞集合-这被认为是记忆项目。在较低的刺激水平下，这些细胞之间的联系将被修剪，因此记忆被遗忘。在中等增产水平下，我们预计已经形成的组合可以维持很长一段时间。更准确地说，我们假设这些组合中的活动和连接之间的正反馈稳定了它们对突触更替的影响，这样它们就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装配稳定性的决定因素。本研究的结果将是一个动态记忆模型，该模型可以以输入依赖的方式形成或去除记忆，并在突触转换的情况下在长期记忆的时间尺度上保持记忆。