Connectional parameters determine multisensory processing in a spiking network model of multisensory convergence

• Published in: Experimental brain research Vol. 213; no. 2-3; pp. 329 - 339
• Main Authors: Lim, H. K., Keniston, L. P., Shin, J. H., Allman, B. L., Meredith, M. A., Cios, K. J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01-09-2011; Springer; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Biomedicine; Brain; Brain - physiology; Brain Mapping; Computational neuroscience; Computer Simulation; Convergence; Firing pattern; Humans; Models, Neurological; Neural Pathways - physiology; Neurogenesis; Neurology; Neurons; Neurosciences; Perception - physiology; Perceptual-motor processes; Physical Stimulation; Physiological aspects; Research Article; Sensory integration; Sensory systems; Somatosensory system; Poland; United States; United States > US; Virginia; Multisensory integration; Bimodal; Association cortex; Simulation; Computation; Cross-modal
• ISSN: 0014-4819; 1432-1106
• DOI: 10.1007/s00221-011-2671-6

For the brain to synthesize information from different sensory modalities, connections from different sensory systems must converge onto individual neurons. However, despite being the definitive, first step in the multisensory process, little is known about multisensory convergence at the neuronal level. This lack of knowledge may be due to the difficulty for biological experiments to manipulate and test the connectional parameters that define convergence. Therefore, the present study used a computational network of spiking neurons to measure the influence of convergence from two separate projection areas on the responses of neurons in a convergent area. Systematic changes in the proportion of extrinsic projections, the proportion of intrinsic connections, or the amount of local inhibitory contacts affected the multisensory properties of neurons in the convergent area by influencing (1) the proportion of multisensory neurons generated, (2) the proportion of neurons that generate integrated multisensory responses, and (3) the magnitude of multisensory integration. These simulations provide insight into the connectional parameters of convergence that contribute to the generation of populations of multisensory neurons in different neural regions as well as indicate that the simple effect of multisensory convergence is sufficient to generate multisensory properties like those of biological multisensory neurons.