Spike-based population coding and working memory.
Spike-based population coding and working memory.
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DOI：
10.1371/journal.pcbi.1001080
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发表时间：
2011-02
影响因子：
4.3
通讯作者：
Denève S
中科院分区：
文献类型：
--
作者：
Boerlin M;Denève S
令人信服的行为证据表明，尽管知觉或运动任务中存在固有的不确定性，但人类可以做出最佳决策。神经科学中的一个关键问题是尖峰神经元群体如何实现这种概率计算。在本文中，我们开发了一个全面的框架，用于动态环境中最佳的、基于尖峰的感觉整合和工作记忆。我们提出，在整合和激发神经元的循环连接网络中，概率分布是逐峰推断的。因此，这些网络可以最佳地组合联合收割机感觉线索，跟踪随时间变化的刺激的状态，并在比单个神经元的时间常数长得多的时间内记住积累的证据。重要的是，我们提出，人口的反应和持久的工作记忆状态代表整个概率分布，而不仅仅是单一的刺激值。这些记忆通过持续的、异步的活动模式来反映，这些活动模式使下游神经元在其短的整合时间窗口内获得相关信息。模型神经元充当预测编码器，仅发射说明尚未发出信号的新信息的尖峰。因此，尖峰时间确定性地发信号通知预测误差，这与尖峰时间被认为是基础激发速率的随机样本的速率代码相反。作为这种编码方案的结果，多个尖峰模式可以可靠地编码相同的信息。这导致了弱相关的、类似泊松的尖峰序列，它们对初始条件敏感，但对甚至高水平的外部神经噪声也是鲁棒的。这种尖峰列车的变异性再现皮层感觉尖峰列车中观察到的，但不能等同于噪音。相反，它是基于最优尖峰的推理的结果。相比之下，我们表明，基于速率的模型执行时，随机尖峰神经元。我们的大多数日常活动都受到不确定性的影响。行为研究已经证实，人类以统计学上最优的方式处理这种不确定性。一个关键的问题是这种最优性背后的神经机制，即神经元如何用概率分布表示和计算。以前的方法提出，概率编码在神经群体的放电率。然而，这样的速率代码似乎不太适合在不断变化的环境中理解感知。特别是，目前还不清楚概率计算如何通过生物学上合理的尖峰神经元来实现。在这里，我们提出了一个网络的尖峰神经元，可以最佳地结合联合收割机不确定的信息，从不同的感觉方式，并保持这些信息可供很长一段时间。这意味着神经记忆不仅代表刺激的最可能值，而且代表整个概率分布。此外，我们的模型表明，每个尖峰信号都传递新的重要信息。因此，观察到的神经反应的变化不能简单地理解为噪音，而是作为一个必要的结果，最佳的感觉整合。因此，我们的研究结果质疑了人们对神经“信号”和“噪声”本质的强烈信念。
Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons. Most of our daily actions are subject to uncertainty. Behavioral studies have confirmed that humans handle this uncertainty in a statistically optimal manner. A key question then is what neural mechanisms underlie this optimality, i.e. how can neurons represent and compute with probability distributions. Previous approaches have proposed that probabilities are encoded in the firing rates of neural populations. However, such rate codes appear poorly suited to understand perception in a constantly changing environment. In particular, it is unclear how probabilistic computations could be implemented by biologically plausible spiking neurons. Here, we propose a network of spiking neurons that can optimally combine uncertain information from different sensory modalities and keep this information available for a long time. This implies that neural memories not only represent the most likely value of a stimulus but rather a whole probability distribution over it. Furthermore, our model suggests that each spike conveys new, essential information. Consequently, the observed variability of neural responses cannot simply be understood as noise but rather as a necessary consequence of optimal sensory integration. Our results therefore question strongly held beliefs about the nature of neural “signal” and “noise”.
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影响因子：
2.5
作者：
Compte, A;Constantinidis, C;Wang, WJ
通讯作者：
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影响因子：
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作者：
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影响因子：
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