Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
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DOI：
10.1371/journal.pcbi.1003037
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发表时间：
2013-04
影响因子：
4.3
通讯作者：
Maass W
中科院分区：
文献类型：
--
作者：
Nessler B;Pfeiffer M;Buesing L;Maass W
神经元网络计算的原理，以及突触权重的尖峰定时依赖可塑性（STDP）如何生成和维持其计算功能，都是未知的。先前的工作已经表明，软赢家通吃（WTA）电路，其中锥体神经元通过中间神经元相互抑制，是皮质微电路的常见图案。我们通过理论分析和计算机模拟表明，贝叶斯计算诱导在这些网络图案通过STDP结合活动依赖性的神经元的兴奋性的变化。这种紧急贝叶斯计算的基本组成部分是先验，其结果来自神经元兴奋性的适应和隐式生成模型，用于通过STDP在突触权重中创建的隐藏原因。事实上，一个令人惊讶的结果是，STDP能够近似一个强大的原则，用于将这种隐式生成模型拟合到高维尖峰输入：期望最大化。我们的研究结果表明，实验观察到的自发活动和试验到试验的变化皮层神经元的信息处理能力的基本特征，因为他们的功能作用是代表概率分布，而不是静态的神经代码。此外，它建议将贝叶斯计算模块网络作为皮质分布式信息处理的新模型。神经元如何学习从输入中提取信息，并执行有意义的计算？神经元接收的输入信号是连续的动作电位流或“尖峰”，到达数千个突触。这些突触的强度--突触的重量--不断地发生变化。在许多实验中已经证明，这种修改取决于突触前和突触后神经元中尖峰的时间顺序，这一规则被称为STDP，但仍然不清楚这如何有助于神经网络架构中的更高级别功能。在本文中，我们表明，STDP诱导在一个常见的连接图案在皮层-赢家通吃（WTA）网络-自主，自组织学习的概率模型的输入。神经回路的最终功能是对输入尖峰序列进行贝叶斯计算。这种无监督学习以前已经在抽象的算法层面上进行了广泛的研究。我们表明，STDP近似于机器学习中最强大的学习方法之一，期望最大化（EM）。在一系列的计算机模拟中，我们证明了这使得WTA电路中的STDP能够解决复杂的学习任务，达到了超越以前使用的尖峰神经网络的性能水平。
The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. How do neurons learn to extract information from their inputs, and perform meaningful computations? Neurons receive inputs as continuous streams of action potentials or “spikes” that arrive at thousands of synapses. The strength of these synapses - the synaptic weight - undergoes constant modification. It has been demonstrated in numerous experiments that this modification depends on the temporal order of spikes in the pre- and postsynaptic neuron, a rule known as STDP, but it has remained unclear, how this contributes to higher level functions in neural network architectures. In this paper we show that STDP induces in a commonly found connectivity motif in the cortex - a winner-take-all (WTA) network - autonomous, self-organized learning of probabilistic models of the input. The resulting function of the neural circuit is Bayesian computation on the input spike trains. Such unsupervised learning has previously been studied extensively on an abstract, algorithmical level. We show that STDP approximates one of the most powerful learning methods in machine learning, Expectation-Maximization (EM). In a series of computer simulations we demonstrate that this enables STDP in WTA circuits to solve complex learning tasks, reaching a performance level that surpasses previous uses of spiking neural networks.
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影响因子：
25
作者：
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