使用内部代理编码关系表示中门控矩阵的分层交互作用的皮层认知功能模型
A model of cortical cognitive function using hierarchical interactions of gating matrices in internal agents coding relational representations
摘要：
灵活的认知需要能够快速检测变量的系统功能，并根据预测指导未来的行为。这里描述的模型提出了一个潜在的框架模式的神经活动，以检测系统的功能和感官输入的组件之间的关系，并应用它们在一个预测的方式。该模型包括在神经活动的状态空间内操作的多个内部门控代理，类似于在外部环境中行为的外部代理。多个内部门控代理代表神经活动的模式，其检测和门控代表不同神经元群体之间的关系的矩阵连接的模式。门控矩阵连接的模式表示可用于预测一系列感觉输入的未来分量或静态感觉刺激的不同特征之间的关系的函数。该模型被应用到动态轨迹的预测，不同的感官刺激的特征之间的内在关系，并预测仿射变换，这可能是有用的解决认知任务，如乌鸦渐进矩阵任务。
英文摘要：
Flexible cognition requires the ability to rapidly detect systematic functions of variables and guide future behavior based on predictions. The model described here proposes a potential framework for patterns of neural activity to detect systematic functions and relations between components of sensory input and apply them in a predictive manner. This model includes multiple internal gating agents that operate within the state space of neural activity, in analogy to external agents behaving in the external environment. The multiple internal gating agents represent patterns of neural activity that detect and gate patterns of matrix connectivity representing the relations between different neural populations. The patterns of gating matrix connectivity represent functions that can be used to predict future components of a series of sensory inputs or the relationship between different features of a static sensory stimulus. The model is applied to the prediction of dynamical trajectories, the internal relationship between features of different sensory stimuli and to the prediction of affine transformations that could be useful for solving cognitive tasks such as the Ravens progressive matrices task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
