Decentralized Multisensory Information Integration in Neural Systems.
Decentralized Multisensory Information Integration in Neural Systems.
复制标题
神经系统中的分散式多感官信息集成。
DOI：
10.1523/jneurosci.0578-15.2016
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Wu Si
中科院分区：
文献类型：
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作者：
Zhang Wen-Hao;Chen Aihua;Rasch Malte J;Wu Si
如何将多种感官线索整合到神经回路中仍然是一个挑战。常见的假设是，信息整合可能是在一个专门的多感觉整合区接收前馈输入的方式。然而，最近的实验证据表明，它不是一个单一的多感觉脑区，而是许多多感觉脑区同时参与信息的整合。为什么信息集成需要许多相互连接的领域，这令人困惑。在这里，我们从理论上研究了如何在一个相互连接的多感官区域网络中以分布式方式实现信息整合。使用生物现实的神经网络模型，我们开发了一个分散的信息集成系统，包括多个相互连接的集成区域。研究一个结合视觉和前庭线索来推断航向的例子，我们发现这样一个分散的系统与解剖学证据和实验观察吻合得很好。特别是，我们表明，这种分散的系统可以最佳地整合信息。去中心化系统预测，最佳整合的信息应该从大脑区域之间的通信动态中局部出现，并为解释多感觉大脑区域之间的连接性提供了新的思路。为了从模糊的环境中可靠地提取信息，大脑整合了多个感官线索，这些线索提供了关于同一感兴趣实体的不同方面的信息。在这里，我们提出了一个分散的架构多感官整合。在这样的系统中，没有处理器处于网络拓扑的中心，并且通过互连的本地处理器以分布式方式实现信息集成。通过研究视觉和前庭线索对航向方向的推断，我们发现分散系统可以最佳地整合信息，处理器之间的相互连接决定了线索整合的程度。我们的模型再现了在实验中观察到的已知多感觉整合行为，并为我们理解信息如何在大脑中整合提供了新的思路。
How multiple sensory cues are integrated in neural circuitry remains a challenge. The common hypothesis is that information integration might be accomplished in a dedicated multisensory integration area receiving feedforward inputs from the modalities. However, recent experimental evidence suggests that it is not a single multisensory brain area, but rather many multisensory brain areas that are simultaneously involved in the integration of information. Why many mutually connected areas should be needed for information integration is puzzling. Here, we investigated theoretically how information integration could be achieved in a distributed fashion within a network of interconnected multisensory areas. Using biologically realistic neural network models, we developed a decentralized information integration system that comprises multiple interconnected integration areas. Studying an example of combining visual and vestibular cues to infer heading direction, we show that such a decentralized system is in good agreement with anatomical evidence and experimental observations. In particular, we show that this decentralized system can integrate information optimally. The decentralized system predicts that optimally integrated information should emerge locally from the dynamics of the communication between brain areas and sheds new light on the interpretation of the connectivity between multisensory brain areas. SIGNIFICANCE STATEMENT To extract information reliably from ambiguous environments, the brain integrates multiple sensory cues, which provide different aspects of information about the same entity of interest. Here, we propose a decentralized architecture for multisensory integration. In such a system, no processor is in the center of the network topology and information integration is achieved in a distributed manner through reciprocally connected local processors. Through studying the inference of heading direction with visual and vestibular cues, we show that the decentralized system can integrate information optimally, with the reciprocal connections between processers determining the extent of cue integration. Our model reproduces known multisensory integration behaviors observed in experiments and sheds new light on our understanding of how information is integrated in the brain.