Minimal Neural Network Conditions for Encoding Future Interactions.

International journal of neural systems Pub Date : 2025-04-01 Epub Date: 2025-02-28 DOI:10.1142/S0129065725500169
Sergio Diez-Hermano, Gonzalo Aparicio-Rodriguez, Paloma Manubens, Abel Sanchez-Jimenez, Carlos Calvo-Tapia, David Levcik, José Antonio Villacorta-Atienza
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Abstract

Space and time are fundamental attributes of the external world. Deciphering the brain mechanisms involved in processing the surrounding environment is one of the main challenges in neuroscience. This is particularly defiant when situations change rapidly over time because of the intertwining of spatial and temporal information. However, understanding the cognitive processes that allow coping with dynamic environments is critical, as the nervous system evolved in them due to the pressure for survival. Recent experiments have revealed a new cognitive mechanism called time compaction. According to it, a dynamic situation is represented internally by a static map of the future interactions between the perceived elements (including the subject itself). The salience of predicted interactions (e.g. collisions) over other spatiotemporal and dynamic attributes during the processing of time-changing situations has been shown in humans, rats, and bats. Motivated by this ubiquity, we study an artificial neural network to explore its minimal conditions necessary to represent a dynamic stimulus through the future interactions present in it. We show that, under general and simple conditions, the neural activity linked to the predicted interactions emerges to encode the perceived dynamic stimulus. Our results show that this encoding improves learning, memorization and decision making when dealing with stimuli with impending interactions compared to no-interaction stimuli. These findings are in agreement with theoretical and experimental results that have supported time compaction as a novel and ubiquitous cognitive process.

空间和时间是外部世界的基本属性。破译大脑处理周围环境的机制是神经科学的主要挑战之一。由于空间和时间信息交织在一起,当情况随着时间的推移而迅速变化时,这一点尤其具有挑战性。然而,了解应对动态环境的认知过程至关重要,因为神经系统就是在这种环境中迫于生存压力进化而来的。最近的实验发现了一种新的认知机制--时间压缩。根据这种机制,动态环境在内部由感知元素(包括主体本身)之间未来互动的静态地图来表示。在人类、大鼠和蝙蝠身上都已证明,在处理时间变化的情况时,预测的相互作用(如碰撞)比其他时空和动态属性更突出。在这种普遍性的激励下,我们研究了一种人工神经网络,以探索通过其中存在的未来互动来表示动态刺激所需的最低条件。我们的研究表明,在一般和简单的条件下,与预测的相互作用相关联的神经活动会出现,从而对感知到的动态刺激进行编码。我们的研究结果表明,与无相互作用的刺激相比,这种编码能提高学习、记忆和决策能力。这些发现与理论和实验结果一致,都支持时间压缩是一种新颖且无处不在的认知过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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