Koechlin E, Anton J L, Burnod Y
INSERM-CREARE, Université Pierre et Marie Curie, Paris, France.
Biol Cybern. 1999 Jan;80(1):25-44. doi: 10.1007/s004220050502.
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.
皮质生理学和计算神经科学中的一个主要问题是理解来自前馈连接的外在信号与来自侧向连接的皮质内信号之间的相互作用。我们在此提出一种基于中颞区（MT区）局部皮质回路实现贝叶斯推理原理这一假设的运动感知计算模型。这种方法在前馈与侧向、兴奋性与抑制性输入之间建立了一种功能平衡。该模型再现了MT区神经元对移动刺激做出反应的大多数已知特性。它解释了包括运动透明度、空间和时间整合/分割在内的重要运动感知现象。在整合先前提出的模型的几个特性的同时，它做出了具体的可测试预测，特别是关于神经元的时间特性和MT区侧向连接的结构。此外，所提出的机制与V1区局部皮质回路的已知特性一致。这表明贝叶斯推理可能是皮质神经元群体信息处理的一个普遍特征。
