Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons
Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons
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DOI：
10.1523/jneurosci.5684-10.2011
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发表时间：
2011-07-27
影响因子：
5.3
通讯作者：
Maass, Wolfgang
中科院分区：
文献类型：
--
作者：
Legenstein, Robert;Maass, Wolfgang
据推测，树突分支中的非线性处理赋予单个神经元执行复杂计算操作的能力，这些计算操作需要解决例如绑定问题。然而，单个神经元如何以自组织的方式获得这种功能尚不清楚，因为大多数关于突触可塑性和学习的理论研究都集中在没有非线性树突特性的神经元模型上。与此同时，在实验中，树突突的信息处理和单个神经元的各种可塑性机制的复杂图景已经出现。特别是，关于大鼠海马树突分支强度增强的新实验数据尚未纳入该模型。在本文中，我们研究了如何将实验观察到的可塑性机制，如去极化依赖的峰值时间依赖的可塑性和分支强度增强，整合到具有树突峰值的自组织非线性神经计算中。我们提供了一个数学证明，在一个简化的设置中，这些可塑性机制诱导树突分支之间的竞争，这是分析单个神经元适应性的一个新概念。我们通过计算机模拟表明，这种树突竞争使单个神经元成为几个神经元集合的成员，并获得非线性计算能力，例如绑定多个输入特征的能力。因此，我们的研究结果表明，非线性神经计算可能通过局部突触和树突可塑性机制的相互作用在单个神经元中自组织。
It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, because most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent spike-timing-dependent plasticity and branch-strength potentiation, could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that, in a simplified setup, these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles and to acquire nonlinear computational capabilities, such as the capability to bind multiple input features. Hence, our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms.