Zhang Jiawei, Gu Yong, Chen Aihua, Yu Yuguo
State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Artificial Intelligence Laboratory, Research Institute of Intelligent and Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence, Human Phenome Institute, Shanghai 200433, China.
Key Laboratory of Primate Neurobiology, Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
Research (Wash D C). 2022 Aug 19;2022:9787040. doi: 10.34133/2022/9787040. eCollection 2022.
Multisensory processing is of vital importance for survival in the external world. Brain circuits can both integrate and separate visual and vestibular senses to infer self-motion and the motion of other objects. However, it is largely debated how multisensory brain regions process such multisensory information and whether they follow the Bayesian strategy in this process. Here, we combined macaque physiological recordings in the dorsal medial superior temporal area (MST-d) with modeling of synaptically coupled multilayer continuous attractor neural networks (CANNs) to study the underlying neuronal circuit mechanisms. In contrast to previous theoretical studies that focused on unisensory direction preference, our analysis showed that synaptic coupling induced cooperation and competition in the multisensory circuit and caused single MST-d neurons to switch between sensory integration or separation modes based on the fixed-criterion causal strategy, which is determined by the synaptic coupling strength. Furthermore, the prior of sensory reliability was represented by pooling diversified criteria at the MST-d population level, and the Bayesian strategy was achieved in downstream neurons whose causal inference flexibly changed with the prior. The CANN model also showed that synaptic input balance is the dynamic origin of neuronal direction preference formation and further explained the misalignment between direction preference and inference observed in previous studies. This work provides a computational framework for a new brain-inspired algorithm underlying multisensory computation.
多感官处理对于在外部世界中生存至关重要。脑回路既能整合也能分离视觉和前庭感觉，以推断自身运动和其他物体的运动。然而，多感官脑区如何处理此类多感官信息以及它们在此过程中是否遵循贝叶斯策略，在很大程度上存在争议。在这里，我们将猕猴背内侧颞上区（MST-d）的生理学记录与突触耦合多层连续吸引子神经网络（CANNs）模型相结合，以研究潜在的神经回路机制。与之前专注于单感官方向偏好的理论研究不同，我们的分析表明，突触耦合在多感官回路中诱导了合作与竞争，并导致单个MST-d神经元根据固定标准因果策略在感觉整合或分离模式之间切换，这一策略由突触耦合强度决定。此外，感觉可靠性的先验在MST-d群体水平上通过汇集多样化标准来表示，并且在下游神经元中实现了贝叶斯策略，其因果推理随先验而灵活变化。CANN模型还表明，突触输入平衡是神经元方向偏好形成的动态起源，并进一步解释了先前研究中观察到的方向偏好与推理之间的不一致。这项工作为多感官计算背后一种新的受脑启发算法提供了一个计算框架。
