Towards a mathematical theory of cortical micro-circuits.
Towards a mathematical theory of cortical micro-circuits.
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DOI：
10.1371/journal.pcbi.1000532
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发表时间：
2009-10
影响因子：
4.3
通讯作者：
Hawkins J
中科院分区：
文献类型：
--
作者：
George D;Hawkins J
分层贝叶斯推理的理论背景作为理解皮质计算的框架正在获得认可。在本文中，我们描述了时空分层模型（称为分层时间记忆（HTM））中的贝叶斯信念传播如何产生皮层回路的数学模型。 HTM 节点是使用重合检测器和马尔可夫链的混合来抽象的。这种 HTM 节点的贝叶斯置信传播方程为神经元实现定义了一组功能约束。解剖数据提供了一组对比鲜明的组织约束。这两个约束的结合提出了对许多解剖学和生理学特征的理论上的解释，并预测了其他一些特征。我们描述了 HTM 网络的模式识别能力，并演示了派生电路在主观轮廓效应建模中的应用。我们还讨论了如何扩展理论和电路来解释当前模型无法解释的皮质特征，并描述可以从模型中得出的可测试的预测。了解皮层回路的计算和信息处理作用是神经科学中的突出问题之一。在本文中，我们从新皮质理论出发，将其建模为时空层次系统，以推导出生物皮质回路。这是通过将时空层次结构的推理方程提供的计算约束与解剖数据相结合来实现的。其结果是一个数学上一致的生物回路，可以映射到皮质层，并与哺乳动物新皮质的许多显着特征相匹配。该数学模型可以作为构建像大脑一样工作的机器的起点。由此产生的生物电路可用于模拟生理现象并得出有关大脑的可测试预测。
The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model. Understanding the computational and information processing roles of cortical circuitry is one of the outstanding problems in neuroscience. In this paper, we work from a theory of neocortex that models it as a spatio-temporal hierarchical system to derive a biological cortical circuit. This is achieved by combining the computational constraints provided by the inference equations for this spatio-temporal hierarchy with anatomical data. The result is a mathematically consistent biological circuit that can be mapped to the cortical laminae and matches many prominent features of the mammalian neocortex. The mathematical model can serve as a starting point for the construction of machines that work like the brain. The resultant biological circuit can be used for modeling physiological phenomena and for deriving testable predictions about the brain.
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影响因子：
2.9
作者：
Foldiak, Peter
通讯作者：
Foldiak, Peter
影响因子：
7.8
作者：
Friston, KJ
通讯作者：
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影响因子：
5.3
作者：
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通讯作者：
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DOI：
10.1098/rstb.2002.1171
发表时间：
2002-12-29
影响因子：
6.3
作者：
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通讯作者：
Sherman, SM
影响因子：
5.5
作者：
Feldmeyer, Dirk;Luebke, Joachim;Sakmann, Bert
通讯作者：
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