Redundancy in synaptic connections enables neurons to learn optimally
Redundancy in synaptic connections enables neurons to learn optimally
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DOI：
10.1073/pnas.1803274115
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发表时间：
2018-07-17
影响因子：
11.1
通讯作者：
Fukai, Tomoki
中科院分区：
文献类型：
--
作者：
Hiratani, Naoki;Fukai, Tomoki
最近的实验研究表明，在哺乳动物大脑的皮质微回路中，大多数神经元到神经元的连接是通过多个突触实现的。然而，目前还不知道这种冗余的突触连接是否提供了任何功能上的好处。在这里，我们展示了冗余的突触连接能够在突触重新连接的情况下实现近乎最佳的学习。通过构建一个简单的树突神经元模型，我们证明了在多突触连接的情况下，突触可塑性近似于一种称为粒子滤波的基于样本的贝叶斯滤波算法，而配线可塑性实现了其重采样过程。将提出的框架扩展到一个详细的初级视觉皮质知觉学习的单神经元模型，我们表明该模型解释了许多实验观察。特别是，该模型基于突触前神经元的刺激选择性，再现了棘波时序依赖可塑性的树突位置依赖性和树突树上的功能性突触组织。我们的研究为突触的可塑性和重新连接提供了一个概念性框架。
Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring.