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We investigated the dynamical behaviors of bimodular continuous attractorneural networks, each processing a modality of sensory input and interactingwith each other. We found that when bumps coexist in both modules, the positionof each bump is shifted towards the other input when the intermodular couplingsare excitatory and is shifted away when inhibitory. When one intermodularcoupling is excitatory while another is moderately inhibitory, temporallymodulated population spikes can be generated. On further increase of theinhibitory coupling, momentary spikes will emerge. In the regime of bumpcoexistence, bump heights are primarily strengthened by excitatory intermodularcouplings, but there is a lesser weakening effect due to a bump being displacedfrom the direct input. When bimodular networks serve as decoders ofmultisensory integration, we extend the Bayesian framework to show thatexcitatory and inhibitory couplings encode attractive and repulsive priors,respectively. At low disparity, the bump positions decode the posterior meansin the Bayesian framework, whereas at high disparity, multiple steady statesexist. In the regime of multiple steady states, the less stable state can beaccessed if the input causing the more stable state arrives after asufficiently long delay. When one input is moving, the bump in thecorresponding module is pinned when the moving stimulus is weak, unpinned atintermediate stimulus strength, and tracks the input at strong stimulusstrength, and the stimulus strengths for these transitions increase with thevelocity of the moving stimulus. These results are important to understandingmultisensory integration of static and dynamic stimuli.

Bimodular continuous attractor neural networks with static and moving stimuli