Bayesian inference in populations of cortical neurons: a model of motion integration and segmentation in area MT
Bayesian inference in populations of cortical neurons: a model of motion integration and segmentation in area MT
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DOI：
10.1007/s004220050502
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发表时间：
1999-01-01
影响因子：
1.9
通讯作者：
Burnod, Y
中科院分区：
文献类型：
--
作者：
Koechlin, E;Anton, JL;Burnod, Y
皮质生理学和计算神经科学的一个主要问题是理解前馈连接的外部信号和横向连接的皮质内信号之间的相互作用。我们在这里提出了一个计算模型的运动感知的基础上的假设，即在中颞区（MT区）的局部皮层电路实现贝叶斯推理原则。这种方法建立了前馈和横向，兴奋性和抑制性，输入之间的功能平衡。该模型再现了MT区神经元对运动刺激的响应的大部分已知特性。它解释了重要的运动感知现象，包括运动透明度，空间和时间的整合/分割。在整合先前提出的模型的几个属性的同时，它做出了具体的可测试的预测，特别是关于神经元的时间属性和MT区域中的横向连接的架构。此外，所提出的机制是一致的V1区的局部皮层电路的已知属性。这表明贝叶斯推理可能是皮层神经元群体信息处理的一般特征。
A major issue in cortical physiology and computational neuroscience is understanding the interaction between extrinsic signals from feedforward connections and intracortical signals from lateral connections. We propose here a computational model for motion perception based on the assumption that the local cortical circuits in the medio-temporal area (area MT) implement a Bayesian inference principle. This approach establishes a functional balance between feedforward and lateral, excitatory and inhibitory, inputs. The model reproduces most of the known properties of the neurons in area MT in response to moving stimuli. It accounts for important motion perception phenomena including motion transparency, spatial and temporal integration/segmentation. While integrating several properties of previously proposed models, it makes specific testable predictions concerning, in particular, temporal properties of neurons and the architecture of lateral connections in area MT. In addition, the proposed mechanism is consistent with the known properties of local cortical circuits in area V1. This suggests that Bayesian inference may be a general feature of information processing in cortical neuron populations.