Graduate School of Frontier Sciences, The University of Tokyo Kashiwa, Chiba, Japan.
Front Comput Neurosci. 2013 Jul 25;7:101. doi: 10.3389/fncom.2013.00101. eCollection 2013.
We perceive our surrounding environment by using different sense organs. However, it is not clear how the brain estimates information from our surroundings from the multisensory stimuli it receives. While Bayesian inference provides a normative account of the computational principle at work in the brain, it does not provide information on how the nervous system actually implements the computation. To provide an insight into how the neural dynamics are related to multisensory integration, we constructed a recurrent network model that can implement computations related to multisensory integration. Our model not only extracts information from noisy neural activity patterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli came from the same source or different sources. We show that our model can reproduce the results of psychophysical experiments on spatial unity and localization bias which indicate that a shift occurs in the perceived position of a stimulus through the effect of another simultaneous stimulus. The experimental data have been reproduced in previous studies using Bayesian models. By comparing the Bayesian model and our neural network model, we investigated how the Bayesian prior is represented in neural circuits.
我们通过不同的感觉器官来感知周围的环境。然而，目前尚不清楚大脑如何从接收到的多感官刺激中估计周围环境的信息。虽然贝叶斯推理为大脑中工作的计算原理提供了一个规范解释，但它并没有提供有关神经系统实际如何实现计算的信息。为了深入了解神经动力学如何与多感觉整合相关，我们构建了一个可以实现与多感觉整合相关计算的递归网络模型。我们的模型不仅可以从嘈杂的神经活动模式中提取信息，还可以估计因果结构；也就是说，它可以推断出不同的刺激是来自同一个还是不同的来源。我们表明，我们的模型可以再现关于空间统一性和定位偏差的心理物理学实验的结果，这些结果表明，通过另一个同时刺激的影响，刺激的感知位置会发生转移。先前的研究使用贝叶斯模型再现了这些实验数据。通过比较贝叶斯模型和我们的神经网络模型，我们研究了贝叶斯先验如何在神经回路中表示。
