Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.
Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.
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DOI：
10.1038/s41598-023-31365-6
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发表时间：
2023-03-16
影响因子：
4.6
通讯作者：
Frank, Lawrence R.
中科院分区：
文献类型：
--
作者：
Galinsky, Vitaly L.;Frank, Lawrence R.
记忆和学习的有效性、健壮性和灵活性构成了人类自然智能、认知和意识的本质。然而，迄今为止，关于这些主题的当前公认的观点是在没有任何关于大脑如何通过其电信号进行内部通信的真正物理理论基础上提出的。这种缺乏坚实的理论框架不仅影响了我们对大脑如何工作的理解，而且还影响了从大脑神经元组织的标准正统观点和基于Hodgkin-Huxley ad-hoc电路类比的大脑网络衍生功能开发的广泛的计算模型，这些电路类比产生了大量的人工，递归，卷积，尖峰等，神经网络（ARCSe NN）反过来又导致了标准算法，这些算法构成了人工智能（AI）和机器学习（ML）方法的基础。我们的假设，基于我们最近开发的弱瞬逝脑电波传播（WETCOW）的物理模型，与当前正统的脑神经元只是在缓慢泄漏的伴随下整合和激发的模型相反，它们可以执行由传播非线性近临界脑电波的集体影响引导的有效相干同步/去相干化的更复杂的任务，这些波目前被认为只是无关紧要的阈下噪声。在本文中，我们强调了WETCOW框架的学习和记忆能力，然后将其应用于AI/ML和神经网络的特定应用。我们证明，由这些严格同步的脑电波激发的学习是浅的，但其时间和准确性优于标准测试数据集上的深度ARCSe对应物。这些结果对我们理解大脑功能和广泛的AI/ML应用都有影响。
The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin–Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.
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