Li Kang, Bundesen Claus, Ditlevsen Susanne
Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark.
Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, Copenhagen, 1353, Denmark.
J Math Neurosci. 2016 Dec;6(1):8. doi: 10.1186/s13408-016-0040-2. Epub 2016 May 23.
A fundamental question concerning the way the visual world is represented in our brain is how a cortical cell responds when its classical receptive field contains a plurality of stimuli. Two opposing models have been proposed. In the response-averaging model, the neuron responds with a weighted average of all individual stimuli. By contrast, in the probability-mixing model, the cell responds to a plurality of stimuli as if only one of the stimuli were present. Here we apply the probability-mixing and the response-averaging model to leaky integrate-and-fire neurons, to describe neuronal behavior based on observed spike trains. We first estimate the parameters of either model using numerical methods, and then test which model is most likely to have generated the observed data. Results show that the parameters can be successfully estimated and the two models are distinguishable using model selection.
一个关于视觉世界在我们大脑中呈现方式的基本问题是，当皮层细胞的经典感受野包含多个刺激时，该细胞会如何做出反应。对此提出了两种相反的模型。在反应平均模型中，神经元以所有单个刺激的加权平均值做出反应。相比之下，在概率混合模型中，细胞对多个刺激的反应就好像只有其中一个刺激存在一样。在这里，我们将概率混合模型和反应平均模型应用于泄漏积分发放神经元，以根据观察到的尖峰序列来描述神经元行为。我们首先使用数值方法估计任一模型的参数，然后测试哪个模型最有可能产生了观察到的数据。结果表明，可以成功估计参数，并且使用模型选择可以区分这两种模型。
