Rule Michael E, Sorbaro Martino, Hennig Matthias H
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
Institute of Neuroinformatics, University of Zürich and ETH, 8057 Zürich, Switzerland.
Entropy (Basel). 2020 Jun 28;22(7):714. doi: 10.3390/e22070714.
In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can be viewed as encoding channels in the information-theoretic sense. However, neural populations face constraints not commonly considered in communications theory. Using restricted Boltzmann machines as a model of sensory encoding, we find that networks with sufficient capacity learn to balance precision and noise-robustness in order to adaptively communicate stimuli with varying information content. Mirroring variability suppression observed in sensory systems, informative stimuli are encoded with high precision, at the cost of more variable responses to frequent, hence less informative stimuli. Curiously, we also find that statistical criticality in the neural population code emerges at model sizes where the input statistics are well captured. These phenomena have well-defined thermodynamic interpretations, and we discuss their connection to prevailing theories of coding and statistical criticality in neural populations.
在这项工作中，我们探索了噪声脉冲网络中感官编码统计模型所学习到的编码策略。神经系统中感官通信的早期阶段在信息论意义上可被视为编码通道。然而，神经群体面临着通信理论中通常未考虑的限制。使用受限玻尔兹曼机作为感官编码模型，我们发现具有足够容量的网络会学习平衡精度和噪声鲁棒性，以便自适应地传达具有不同信息内容的刺激。与在感官系统中观察到的变异性抑制现象类似，信息丰富的刺激以高精度进行编码，代价是对频繁出现、因而信息较少的刺激产生更多变异性的反应。奇怪的是，我们还发现，在输入统计信息被很好捕捉的模型规模下，神经群体编码中的统计临界性会出现。这些现象具有明确的热力学解释，并且我们讨论了它们与神经群体中现行编码理论和统计临界性的联系。
