Saidi Maryam, Towhidkhah Farzad, Gharibzadeh Shahriar, Lari Abdolaziz Azizi
Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, 15875-4413, Iran.
J Integr Neurosci. 2013 Dec;12(4):491-511. doi: 10.1142/S0219635213500301. Epub 2013 Dec 6.
Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model simulation.
人类通过整合来自不同感官模态的信息来感知周围世界。早期的多感官整合模型分别主要依赖传统贝叶斯推理和因果贝叶斯推理，用于单一因果（源）以及两个因果（针对视觉和听觉系统等两种感官）情况。本文提出了一种新的循环神经模型，用于整合视觉和本体感觉信息。该模型基于群体编码，能够模拟人类大脑中神经中枢的多感官整合。模拟结果与因果贝叶斯推理所取得的结果一致。该模型还可以模拟人类视觉和本体感觉信息的感官训练过程。多感官整合中的训练过程是此前文献中较少关注的一点。在我们之前的研究中，通过一系列实验研究了本体感觉训练对多感官感知的影响。当前的研究评估了视觉和本体感觉这两种模态训练的效果，并通过一系列新实验将它们相互比较。在这些实验中，要求受试者将手绕圈移动并估计其位置。实验在八名接受本体感觉训练的受试者和八名接受视觉训练的受试者身上进行。实验结果显示了三个要点：（1）视觉学习率明显高于本体感觉学习率；（2）通过训练，视觉和本体感觉误差的均值都有所降低，但统计分析表明，这种降低对于本体感觉误差是显著的，而对于视觉误差则不显著；（3）训练阶段甚至在开始时的视觉误差，都远小于主要测试阶段的误差，因为在主要测试中，受试者必须同时关注两种感官。本文的实验结果与神经模型模拟结果一致。
