Ursino Mauro, Cuppini Cristiano, Magosso Elisa
Department of Electrical, Electronic and Information Engineering University of Bologna, I 40136 Bologna, Italy
Neural Comput. 2017 Mar;29(3):735-782. doi: 10.1162/NECO_a_00935. Epub 2017 Jan 17.
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations. The work includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In cross-modal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues.
最近的理论和实验研究表明，在多感官条件下，大脑对外界事件进行近似最优的贝叶斯估计，更重视更可靠的刺激。然而，负责这种行为的神经机制及其在多感官环境中的逐步成熟，仍未得到充分理解。这封信的目的是用一个视听整合的神经网络模型来分析这个问题，该模型基于概率群体编码——即一群神经元可以编码概率函数以进行贝叶斯推理的观点。该模型由两条拓扑组织的单感官神经元链（听觉和视觉）组成。它们通过可塑性感受野接收相应的输入，并相互交换可塑性跨模态突触，这些突触编码视听输入的空间共现。第三条多感官神经元链对听觉和视觉兴奋进行简单求和。这项工作包括一个理论部分和一项计算机模拟研究。我们展示了在训练过程中，如何使用一个简单的突触学习规则（由赫布强化和一个衰减项组成）来缩小感受野并编码单感官似然函数。因此，训练后，每个单感官区域都能实现对刺激位置（听觉或视觉）的最大似然估计。在跨模态条件下，相同的学习规则可以将先验概率信息编码到跨模态突触中。计算机模拟证实了理论结果，并表明所提出的网络在单模态条件下可以实现对听觉（或视觉）位置的最大似然估计，在跨模态条件下可以实现贝叶斯估计，与最优估计有适度偏差。此外，该模型解释了口技错觉，并且通过观察多模态神经元的活动，根据单个线索的可靠性，逐次解释了听觉和视觉输入的自动重新加权。
