Multiple Timescale Online Learning Rules for Information Maximization with Energetic Constraints
Multiple Timescale Online Learning Rules for Information Maximization with Energetic Constraints
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DOI：
10.1162/neco_a_01182
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发表时间：
2019-05-01
影响因子：
2.9
通讯作者：
Ching，ShiNung
中科院分区：
文献类型：
--
作者：
Yi，Peng;Ching，ShiNung
神经编码问题的一个关键方面是理解传入刺激的表征是如何通过神经网络内的学习和适应动态来建立的。infomax范式是建立在这样的前提下，这种学习试图最大限度地提高输入刺激和神经活动之间的互信息。在这封信中，我们着眼于两个概念障碍来解决这种基于信息的神经编码问题。具体来说，我们检查，然后显示这种形式的编码可以实现在线输入处理。因此，我们的框架避免了依赖于全局网络感知和批量处理感觉信号的优化方法的生物不相容性。我们的研究结果的核心是使用变分界限作为替代目标函数，一个既定的技术，以前没有被证明可以产生在线政策。我们获得学习动态线性高斯解码器的保护伞下的线性连续和离散尖峰神经编码模型。这一结果是通过成对反馈机制在神经元活动方面近似某些信息量来实现的。此外，我们解决了这样的学习动态如何可以实现严格的能量约束的问题。我们发现，赋予网络以在较慢的时间尺度上演化的辅助变量可以允许在神经动力学中实现鞍点优化，从而产生在信息和能量方面具有良好特性的神经代码。
A key aspect of the neural coding problem is understanding how representations of afferent stimuli are built through the dynamics of learning and adaptation within neural networks. The infomax paradigm is built on the premise that such learning attempts to maximize the mutual information between input stimuli and neural activities. In this letter, we tackle the problem of such information-based neural coding with an eye toward two conceptual hurdles. Specifically, we examine and then show how this form of coding can be achieved with online input processing. Our framework thus obviates the biological incompatibility of optimization methods that rely on global network awareness and batch processing of sensory signals. Central to our result is the use of variational bounds as a surrogate objective function, an established technique that has not previously been shown to yield online policies. We obtain learning dynamics for both linear-continuous and discrete spiking neural encoding models under the umbrella of linear gaussian decoders. This result is enabled by approximating certain information quantities in terms of neuronal activity via pairwise feedback mechanisms. Furthermore, we tackle the problem of how such learning dynamics can be realized with strict energetic constraints. We show that endowing networks with auxiliary variables that evolve on a slower timescale can allow for the realization of saddle-point optimization within the neural dynamics, leading to neural codes with favorable properties in terms of both information and energy.