Cortical Labs Pty Ltd, Melbourne, 3056, VIC, Australia.
Biomedical Engineering Department, University of Melbourne, Parkville, 3010, VIC, Australia.
Nat Commun. 2023 Aug 30;14(1):5287. doi: 10.1038/s41467-023-41020-3.
Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.
理解大脑如何处理信息是一项极其困难的任务。在描述大脑信息处理的各种指标中，对动态近临界状态的观察引起了极大的兴趣。然而，由于人类和动物模型的理论和实验限制，尚无法确定神经临界状态何时以及为何会出现，以及与注意力、认知，甚至意识的联系。为了探索这个问题，我们使用了一种体外皮质神经元神经网络，该网络经过训练可以玩简化版的“乒乓球”游戏，以展示合成生物智能（SBI）。我们证明，当神经网络接收到与任务相关的结构化感觉输入时，临界动力学就会出现，从而使系统重新组织到近临界状态。此外，更好的任务表现与接近临界动力学相关。然而，仅仅是临界性本身不足以使神经网络在没有关于先前动作后果的其他信息的情况下表现出学习能力。这些发现有力地支持了这样一种观点，即神经临界性是作为传入结构化信息处理的基本特征出现的，而不需要更高阶的认知。
