The connectivity structure of neuronal networks in cortex is highly dynamic. This ongoing cortical rewiring is assumed to serve important functions for learning and memory. We analyze in this article a model for the self-organization of synaptic inputs onto dendritic branches of pyramidal cells. The model combines a generic stochastic rewiring principle with a simple synaptic plasticity rule that depends on local dendritic activity. In computer simulations, we find that this synaptic rewiring model leads to synaptic clustering, that is, temporally correlated inputs become locally clustered on dendritic branches. This empirical finding is backed up by a theoretical analysis which shows that rewiring in our model favors network configurations with synaptic clustering. We propose that synaptic clustering plays an important role in the organization of computation and memory in cortical circuits: we find that synaptic clustering through the proposed rewiring mechanism can serve as a mechanism to protect memories from subsequent modifications on a medium time scale. Rewiring of synaptic connections onto specific dendritic branches may thus counteract the general problem of catastrophic forgetting in neural networks.

中文翻译：

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通过突触重新布线在树突上出现稳定的突触簇

皮层神经元网络的连接结构是高度动态的。这种正在进行的皮质重新布线被认为对学习和记忆有重要作用。我们在本文中分析了锥体细胞树突分支上突触输入的自组织模型。该模型结合了通用随机重连原理和取决于局部树突活动的简单突触可塑性规则。在计算机模拟中，我们发现这种突触重新布线模型会导致突触聚类，即时间相关的输入在树突分支上局部聚类。这一实证发现得到了理论分析的支持，该分析表明我们模型中的重新布线有利于具有突触聚类的网络配置。我们提出突触聚类在皮层回路中的计算和记忆的组织中发挥着重要作用：我们发现通过所提出的重新布线机制进行的突触聚类可以作为一种机制来保护记忆免受中等时间尺度的后续修改。因此，将突触连接重新连接到特定的树突分支上可能会抵消神经网络中灾难性遗忘的普遍问题。