Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli
Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli
复制标题
DOI：
10.3389/fncom.2013.00101
复制
发表时间：
2013-07-25
影响因子：
3.2
通讯作者：
Okada, Masato
中科院分区：
文献类型：
--
作者：
Yamashita, Itsuki;Katahira, Kentaro;Okada, Masato
我们通过不同的感觉器官来感知周围的环境。然而，目前还不清楚大脑是如何从我们周围的多感官刺激中估计信息的。虽然贝叶斯推理提供了在大脑中工作的计算原理的规范描述，但它并没有提供关于神经系统如何实际实现计算的信息。为了深入了解神经动力学与多感觉整合的关系，我们构建了一个循环网络模型，可以实现与多感觉整合相关的计算。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，它可以推断不同的刺激是来自同一来源还是不同的来源。我们的模型可以再现空间统一性和定位偏差的心理物理实验结果，这些结果表明，通过另一个同步刺激的影响，刺激的感知位置发生了变化。实验数据已经在以前的研究中使用贝叶斯模型进行了复制。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。
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.