Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
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DOI：
10.1371/journal.pcbi.1002211
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发表时间：
2011-11
影响因子：
4.3
通讯作者：
Maass W
中科院分区：
文献类型：
--
作者：
Buesing L;Bill J;Nessler B;Maass W
大脑中尖峰神经元网络中的计算组织仍然在很大程度上是未知的，特别是考虑到它们放电活动的固有随机特征和实验观察到的大脑神经系统的试验到试验的可变性。原则上，存在一个强大的随机计算、抽样概率推理的计算框架，可以解释神经科学和认知科学中的大量宏观实验数据。但事实证明，要在这些用于随机计算的抽象模型和更详细的尖峰神经元网络动力学模型之间建立联系，是令人惊讶的困难。在这里，我们建立了这样一个联系，并证明了在一定条件下，放电神经元网络的随机放电活动可以解释为通过马尔可夫链蒙特卡罗(MCMC)抽样的概率推断。由于分布式系统中常用的MCMC采样方法，如Gibbs采样，与尖峰神经元的动态特性不一致，我们引入了一种基于不可逆马尔可夫链的不同方法，该方法能够通过适当选择随机变量来反映尖峰神经元活动的内在时间过程。我们提出了一个神经网络模型，并通过严格的理论分析表明，它的神经活动实现了给定分布的MCMC采样，无论是离散时间还是连续时间。这为弥合皮层计算的抽象功能模型和更详细的神经元网络模型之间的差距提供了一步。众所周知，神经元通过称为动作电位或棘波的短电脉冲进行交流。但是尖峰网络如何实现复杂的计算呢？试图将尖峰网络活动与确定性计算步骤的结果联系起来，就像数字计算机中处理器的输出比特，与认知科学和神经科学的发现相冲突，后者表明，相同实验中的神经尖峰输出在不同的试验中是不同的，即神经元是“不可靠的”。因此，最近有人提出，神经活动更应该被视为来自许多变量的潜在概率分布的样本，这些变量例如代表包含先验知识、记忆以及感觉输入的外部世界的模型。这一假设假设随机放电神经元网络能够在面对不确定性的情况下模拟强大的推理算法，即进行概率推理。在这项工作中，我们提出了一个确实满足这些计算要求的详细的神经网络模型，并将网络的尖峰动态与具体的概率计算联系起来。我们的模型表明，神经系统适合通过使用随机计算元素而不是确定性计算元素来进行概率推理。
The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. It is well-known that neurons communicate with short electric pulses, called action potentials or spikes. But how can spiking networks implement complex computations? Attempts to relate spiking network activity to results of deterministic computation steps, like the output bits of a processor in a digital computer, are conflicting with findings from cognitive science and neuroscience, the latter indicating the neural spike output in identical experiments changes from trial to trial, i.e., neurons are “unreliable”. Therefore, it has been recently proposed that neural activity should rather be regarded as samples from an underlying probability distribution over many variables which, e.g., represent a model of the external world incorporating prior knowledge, memories as well as sensory input. This hypothesis assumes that networks of stochastically spiking neurons are able to emulate powerful algorithms for reasoning in the face of uncertainty, i.e., to carry out probabilistic inference. In this work we propose a detailed neural network model that indeed fulfills these computational requirements and we relate the spiking dynamics of the network to concrete probabilistic computations. Our model suggests that neural systems are suitable to carry out probabilistic inference by using stochastic, rather than deterministic, computing elements.
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影响因子：
19.9
作者：
Fiser, Jozsef;Berkes, Pietro;Orban, Gergo;Lengyel, Mate
通讯作者：
Lengyel, Mate
影响因子：
4.2
作者：
Antic SD;Zhou WL;Moore AR;Short SM;Ikonomu KD
通讯作者：
Ikonomu KD
影响因子：
1.8
作者：
Bartels, Andreas;Logothetis, Nikos K.
通讯作者：
Logothetis, Nikos K.
DOI：
10.1126/science.1195870
发表时间：
2011-01-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Berkes P;Orbán G;Lengyel M;Fiser J
通讯作者：
Fiser J
影响因子：
64.8
作者：
Fiser, J;Chiu, CY;Weliky, M
通讯作者：
Weliky, M