Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.

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发现大脑网络组织的计算相关性

了解神经认知计算不仅需要定位分布在整个大脑中的认知信息，还需要确定这些信息是如何到达那里的。我们回顾了将经验和模拟大脑网络组织与认知信息处理联系起来的最新进展。基于这些进展，我们提供了一个新的框架来理解连接在认知中的作用：网络编码（编码/解码）模型。这些模型利用连通性来指定通过神经活动流过程的信息传输，成功地预测经验神经数据中认知表征的形成。这些模型的成功支持了这样一种可能性：局部神经功能从主要由连接模式指定的分布式活动流过程中机械地出现（计算）。