Keeler J D, Pichler E E, Ross J
Chemistry Department, Stanford University, CA 94305.
Proc Natl Acad Sci U S A. 1989 Mar;86(5):1712-6. doi: 10.1073/pnas.86.5.1712.
We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may "quench" the neural patterns of activity to enhance the ability to learn details.
我们研究了一个包含高斯噪声、高阶神经元相互作用和神经调节的神经网络模型。对于一阶网络，在噪声水平上存在一个阈值（相变），高于该阈值时，网络仅表现出无序行为，并且在噪声阈值附近出现临界减慢。如果网络具有高阶反馈相互作用，它可以容忍更多噪声，这也会导致网络动力学中的滞后和多稳态。生物神经网络中的去甲肾上腺素等神经调质可以调节信噪比。我们将结果与实验结果进行了比较，并建议进一步研究以测试滞后和神经调节在模式识别和学习中的作用。我们提出，去甲肾上腺素可能会“抑制”神经活动模式，以增强学习细节的能力。
