Computing with neural synchrony.
Computing with neural synchrony.
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DOI：
10.1371/journal.pcbi.1002561
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发表时间：
2012
影响因子：
4.3
通讯作者：
Brette R
中科院分区：
文献类型：
--
作者：
Brette R
神经元主要通过尖峰信号进行通信，但大多数神经计算理论都是基于放电率。然而，许多实验观察表明，尖峰的时间协调在感觉加工中起作用。在潜在的尖峰为基础的代码，同步出现作为一个很好的候选人，因为神经放电和可塑性是敏感的精细输入相关性。然而，目前还不清楚同步在神经计算中扮演什么角色，以及它可以提供什么功能优势。理论上，我表明，神经同步的计算兴趣出现时，神经元具有异质性。在这种情况下，刺激和神经同步性之间的关系被同步性感受野的概念所捕获，同步性感受野是在一组神经元中诱导同步反应的刺激的集合。在异质神经群体中，似乎同步模式代表刺激中的结构或感觉不变量，然后可以由突触后神经元检测到。所需的神经回路可以自发地出现与尖峰时间依赖的可塑性。在不同的感官形式的例子，我表明，这允许简单的神经回路从现实的感官刺激提取相关信息，例如，以确定在干扰物的存在下波动的气味。这种基于同步的计算理论表明，相对尖峰时间可能确实具有计算相关性，并提出了具有吸引人的计算特性的感觉处理的新型神经网络模型。大脑是如何计算的？传统的神经计算理论用平均放电率来描述神经元的操作功能，尖峰脉冲的时间几乎没有信息。然而，许多研究表明，尖峰时间可以传递信息，神经元对输入的同步性非常敏感。在这里，我提出了一个简单的基于尖峰的计算框架，基于刺激诱导的同步可以用于提取感觉不变量（例如，声源的位置）的想法，这对于经典神经网络来说是一项艰巨的任务。它依赖于一个简单的评论，即一系列重复的巧合本身就是一个不变量。感知的许多方面依赖于提取不变的特征，例如时变声音的空间位置，具有波动强度的气味的身份，音符的音高。我证明了简单的基于同步的神经元模型可以提取这些有用的功能，通过使用尖峰模型在几种感觉方式。
Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. How does the brain compute? Traditional theories of neural computation describe the operating function of neurons in terms of average firing rates, with the timing of spikes bearing little information. However, numerous studies have shown that spike timing can convey information and that neurons are highly sensitive to synchrony in their inputs. Here I propose a simple spike-based computational framework, based on the idea that stimulus-induced synchrony can be used to extract sensory invariants (for example, the location of a sound source), which is a difficult task for classical neural networks. It relies on the simple remark that a series of repeated coincidences is in itself an invariant. Many aspects of perception rely on extracting invariant features, such as the spatial location of a time-varying sound, the identity of an odor with fluctuating intensity, the pitch of a musical note. I demonstrate that simple synchrony-based neuron models can extract these useful features, by using spiking models in several sensory modalities.
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影响因子：
1.9
作者：
Brette, R
通讯作者：
Brette, R
影响因子：
2.9
作者：
Brette, R;Guigon, E
通讯作者：
Guigon, E
影响因子：
4.3
作者：
Deco, Gustavo;Jirsa, Viktor K.;Robinson, Peter A.;Breakspear, Michael;Friston, Karl J.
通讯作者：
Friston, Karl J.
影响因子：
16.2
作者：
Brivanlou, IH;Warland, DK;Meister, M
通讯作者：
Meister, M
DOI：
10.1073/pnas.130200797
发表时间：
2000-07-05
影响因子：
11.1
作者：
Azouz, R;Gray, CM
通讯作者：
Gray, CM