Department of Electrical, Electronic and Information Engineering, University of Bologna, Viale Risorgimento 2, I40136, Bologna, Italy.
Department of Psychology, Department of BioEngineering, Interdepartmental Neuroscience Program, University of California, Los Angeles, CA, USA.
Eur J Neurosci. 2017 Nov;46(9):2481-2498. doi: 10.1111/ejn.13725.
Recently, experimental and theoretical research has focused on the brain's abilities to extract information from a noisy sensory environment and how cross-modal inputs are processed to solve the causal inference problem to provide the best estimate of external events. Despite the empirical evidence suggesting that the nervous system uses a statistically optimal and probabilistic approach in addressing these problems, little is known about the brain's architecture needed to implement these computations. The aim of this work was to realize a mathematical model, based on physiologically plausible hypotheses, to analyze the neural mechanisms underlying multisensory perception and causal inference. The model consists of three layers topologically organized: two encode auditory and visual stimuli, separately, and are reciprocally connected via excitatory synapses and send excitatory connections to the third downstream layer. This synaptic organization realizes two mechanisms of cross-modal interactions: the first is responsible for the sensory representation of the external stimuli, while the second solves the causal inference problem. We tested the network by comparing its results to behavioral data reported in the literature. Among others, the network can account for the ventriloquism illusion, the pattern of sensory bias and the percept of unity as a function of the spatial auditory-visual distance, and the dependence of the auditory error on the causal inference. Finally, simulations results are consistent with probability matching as the perceptual strategy used in auditory-visual spatial localization tasks, agreeing with the behavioral data. The model makes untested predictions that can be investigated in future behavioral experiments.
最近，实验和理论研究集中在大脑从嘈杂的感觉环境中提取信息的能力，以及如何处理跨模态输入以解决因果推断问题，从而提供对外界事件的最佳估计。尽管有经验证据表明，神经系统在解决这些问题时使用了一种统计上最优和概率的方法，但对于实现这些计算所需的大脑结构知之甚少。这项工作的目的是基于生理上合理的假设，实现一个数学模型，以分析多感觉感知和因果推断的神经机制。该模型由三个拓扑组织的层组成：两个层分别编码听觉和视觉刺激，并通过兴奋性突触相互连接，并向第三层下游层发送兴奋性连接。这种突触组织实现了两种跨模态相互作用的机制：第一种机制负责外部刺激的感觉表示，而第二种机制则解决因果推断问题。我们通过将网络的结果与文献中报道的行为数据进行比较来测试该网络。除其他外，该网络可以解释语音错觉、感觉偏差模式以及作为听觉-视觉距离函数的统一性感知，以及听觉误差对因果推断的依赖性。最后，模拟结果与听觉-视觉空间定位任务中使用的概率匹配一致，与行为数据一致。该模型做出了未经测试的预测，可以在未来的行为实验中进行研究。
