Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China.
Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA.
Phys Rev E. 2023 Jun;107(6-1):064302. doi: 10.1103/PhysRevE.107.064302.
We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli.
我们研究了双模连续吸引子神经网络的动态行为，每个网络处理一种感觉输入模态，并相互作用。我们发现，当两个模块中都存在驼峰时，当模块间的相互作用是兴奋性的时，每个驼峰的位置会向另一个输入移动，而当抑制性时则会远离。当一个模块间的相互作用是兴奋性的，而另一个是适度抑制性的时，可以产生时变调制的群体峰。当进一步增加抑制性耦合时，会出现瞬间峰。在驼峰共存的区域，驼峰高度主要通过兴奋性的模块间耦合得到增强，但由于驼峰从直接输入中移位，存在较小的削弱效应。当双模网络作为多感觉整合的解码器时，我们扩展了贝叶斯框架，以表明兴奋性和抑制性耦合分别编码吸引和排斥的先验。在低差异时，在贝叶斯框架中，驼峰位置解码后验均值，而在高差异时，存在多个稳定状态。在多个稳定状态的区域中，如果导致更稳定状态的输入在足够长的延迟后到达，则可以访问不稳定的状态。当一个输入在移动时，如果移动刺激较弱，对应的模块中的驼峰会被固定，在中间刺激强度下，驼峰会被解锁，在强刺激强度下，驼峰会跟踪输入，并且这些转换的刺激强度随着移动刺激的速度增加而增加。这些结果对于理解静态和动态刺激的多感觉整合非常重要。
