Ursino Mauro, Cuppini Cristiano, Magosso Elisa
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy.
Neural Netw. 2014 Dec;60:141-65. doi: 10.1016/j.neunet.2014.08.003. Epub 2014 Aug 23.
The Brain's ability to integrate information from different modalities (multisensory integration) is fundamental for accurate sensory experience and efficient interaction with the environment: it enhances detection of external stimuli, disambiguates conflict situations, speeds up responsiveness, facilitates processes of memory retrieval and object recognition. Multisensory integration operates at several brain levels: in subcortical structures (especially the Superior Colliculus), in higher-level associative cortices (e.g., posterior parietal regions), and even in early cortical areas (such as primary cortices) traditionally considered to be purely unisensory. Because of complex non-linear mechanisms of brain integrative phenomena, a key tool for their understanding is represented by neurocomputational models. This review examines different modelling principles and architectures, distinguishing the models on the basis of their aims: (i) Bayesian models based on probabilities and realizing optimal estimator of external cues; (ii) biologically inspired models of multisensory integration in the Superior Colliculus and in the Cortex, both at level of single neuron and network of neurons, with emphasis on physiological mechanisms and architectural schemes; among the latter, some models exhibit synaptic plasticity and reproduce development of integrative capabilities via Hebbian-learning rules or self-organizing maps; (iii) models of semantic memory that implement object meaning as a fusion between sensory-motor features (embodied cognition). This overview paves the way to future challenges, such as reconciling neurophysiological and Bayesian models into a unifying theory, and stimulates upcoming research in both theoretical and applicative domains.
大脑整合来自不同模态信息（多感官整合）的能力，对于准确的感官体验以及与环境进行高效互动至关重要：它能增强对外界刺激的检测，消除冲突情境的歧义，加快反应速度，促进记忆检索和物体识别过程。多感官整合在多个脑水平上发挥作用：在皮层下结构（尤其是上丘）、高级联合皮层（如后顶叶区域），甚至在传统上被认为是纯粹单感官的早期皮层区域（如初级皮层）。由于大脑整合现象的复杂非线性机制，神经计算模型是理解这些现象的关键工具。本综述考察了不同的建模原理和架构，根据其目标对模型进行了区分：（i）基于概率并实现外部线索最优估计器的贝叶斯模型；（ii）上丘和皮层中多感官整合的生物启发模型，包括单个神经元和神经元网络层面，重点关注生理机制和架构方案；在后者中，一些模型表现出突触可塑性，并通过赫布学习规则或自组织映射来重现整合能力的发展；（iii）将物体意义实现为感觉运动特征融合（具身认知）的语义记忆模型。这一概述为未来的挑战（如将神经生理学模型和贝叶斯模型统一为一个理论）铺平了道路，并激发了理论和应用领域的后续研究。
