Results in the recent literature suggest that multisensory integration in the brain follows the rules of Bayesian inference. However, how neural circuits can realize such inference and how it can be learned from experience is still the subject of active research. The aim of this work is to use a recent neurocomputational model to investigate how the likelihood and prior can be encoded in synapses, and how they affect audio-visual perception, in a variety of conditions characterized by different experience, different cue reliabilities and temporal asynchrony. The model considers two unisensory networks (auditory and visual) with plastic receptive fields and plastic crossmodal synapses, trained during a learning period. During training visual and auditory stimuli are more frequent and more tuned close to the fovea. Model simulations after training have been performed in crossmodal conditions to assess the auditory and visual perception bias: visual stimuli were positioned at different azimuth (±10° from the fovea) coupled with an auditory stimulus at various audio-visual distances (±20°). The cue reliability has been altered by using visual stimuli with two different contrast levels. Model predictions are compared with behavioral data. Results show that model predictions agree with behavioral data, in a variety of conditions characterized by a different role of prior and likelihood. Finally, the effect of a different unimodal or crossmodal prior, re-learning, temporal correlation among input stimuli, and visual damage (hemianopia) are tested, to reveal the possible use of the model in the clarification of important multisensory problems.

中文翻译：

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通过空间视听感知的神经网络解释似然操纵和先验的影响。

最新文献的结果表明，大脑中的多感觉整合遵循贝叶斯推理的规则。但是，神经回路如何实现这种推理以及如何从经验中学习仍然是积极研究的主题。这项工作的目的是使用最新的神经计算模型来研究在以不同体验，不同提示可靠性和时间异步为特征的各种情况下，突触中的可能性和先验如何编码，以及它们如何影响视听感知。该模型考虑了两个单感觉网络（听觉和视觉），它们在学习期间经过训练，具有可塑性感受场和可塑性跨峰突触。在训练过程中，视觉和听觉刺激更加靠近中央凹，并且更加靠近中央凹。在交叉模态条件下进行训练后的模型仿真，以评估听觉和视觉感知偏差：视觉刺激位于不同的方位角（距中央凹±10°），并且听觉刺激位于各种视听距离（±20°） 。通过使用具有两种不同对比度级别的视觉刺激，提示的可靠性已经改变。将模型预测与行为数据进行比较。结果表明，在以先验和可能性的不同角色为特征的各种条件下，模型预测与行为数据一致。最后，测试了不同的单峰或交叉峰的先验，重新学习，输入刺激之间的时间相关性以及视觉损伤（偏盲）的影响，以揭示该模型在阐明重要的多感觉问题中的可能用途。