Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, Japan.
Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, Japan.
PLoS Comput Biol. 2024 Sep 3;20(9):e1012378. doi: 10.1371/journal.pcbi.1012378. eCollection 2024 Sep.
Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistency of neural responses to stimuli that are presented repeatedly is enhanced implicitly in an unsupervised way, and results in improved perceptual consistency. Here, we propose the term "selective consistency" to describe this input-dependent consistency and hypothesize that it will be acquired in a self-organizing manner by plasticity within the neural system. To test this, we investigated whether a reservoir-based plastic model could acquire selective consistency to repeated stimuli. We used white noise sequences randomly generated in each trial and referenced white noise sequences presented multiple times. The results showed that the plastic network was capable of acquiring selective consistency rapidly, with as little as five exposures to stimuli, even for white noise. The acquisition of selective consistency could occur independently of performance optimization, as the network's time-series prediction accuracy for referenced stimuli did not improve with repeated exposure and optimization. Furthermore, the network could only achieve selective consistency when in the region between order and chaos. These findings suggest that the neural system can acquire selective consistency in a self-organizing manner and that this may serve as a mechanism for certain types of learning.
理解大脑如何实现相对一致的信息处理，而其活动本身却存在固有不一致性，这是神经科学面临的主要挑战之一。最近有报道称，在无监督的情况下，大脑对重复呈现的刺激的神经反应一致性会隐含地增强，从而提高感知一致性。在这里，我们提出“选择性一致性”这一术语来描述这种输入依赖性的一致性，并假设它将通过神经网络内的可塑性以自组织的方式获得。为了验证这一点，我们研究了基于储层的塑料模型是否能够对重复刺激产生选择性一致性。我们在每次试验中使用随机生成的白噪声序列，并参考多次呈现的白噪声序列。结果表明，塑料网络能够快速获得选择性一致性，只需对刺激进行五次暴露，即使是白噪声也是如此。选择性一致性的获得可以独立于性能优化，因为网络对参考刺激的时间序列预测准确性不会随着重复暴露和优化而提高。此外，只有当网络处于秩序和混沌之间的区域时，网络才能实现选择性一致性。这些发现表明，神经网络可以以自组织的方式获得选择性一致性，这可能是某些类型学习的一种机制。
