A theoretical study of multisensory integration in the superior colliculus by a neural network model.
Neurons in the superior colliculus (SC) integrate stimuli of different modalities. In this work, a mathematical model of the integrative response of SC neurons is presented, to gain a deeper insight into the possible mechanisms involved, and on individual differences in integrative abilities. The model includes two unimodal areas (auditory and visual), which communicate via feedforward and feedback synapses with a third multisensory area. Each neuron is represented via a sigmoidal relationship and a first-order dynamic. Neurons in the same area interact via lateral synapses. Simulations show that the model, with a basal parameter set, can mimic various responses described in the literature: (i) multimodal enhancement in response to two cross-modal stimuli within the receptive field, according to the inverse-effectiveness principle; (ii) within-modality suppression and cross-modality suppression by a stimulus (of the same or other modality) placed outside the receptive field. Sensitivity analysis on model parameters demonstrate that different classes of neurons observed in the literature (such as, neurons which exhibit within modality suppression without cross-modality suppression, or neurons with asymmetrical cross-modality suppression) can be reproduced by simply modifying synaptic strengths in the multimodal area. Finally, exempla of the possible role of feedback mechanisms in ambiguous conditions (such as reinforcement of a poor perception by a second cross-modal stimulus, or ventriloquism) are shown and critically discussed. The model may be of value to assess the different mechanisms responsible for multisensory integration in the SC, and, in future, to study neural plasticity in multisensory systems during development or rehabilitation.
Magosso
Elisa
E
Department of Electronics, Computer Science and Systems, University of Bologna, Italy.
Cuppini
Cristiano
C
Serino
Andrea
A
Di Pellegrino
Giuseppe
G
Ursino
Mauro
M
D000818:Animals; D001931:Brain Mapping; D003198:Computer Simulation; D008433:Mathematics; D008959:Models, Neurological; D009415:Nerve Net; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010812:Physical Stimulation / Q000379:methods; D013477:Superior Colliculi / Q000166:cytology / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology