Modeling of Multisensory Convergence with a Network of Spiking Neurons: A Reverse Engineering Approach

• Published in: IEEE transactions on biomedical engineering 2011-07, Vol.58 (7), p.1940-1949
• Main Authors: Lim, Hun Ki, Keniston, Leslie P., Cios, Krzysztof J.
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Analysis of Variance; Applied sciences; Artificial intelligence; Biological system modeling; Computational modeling; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Convergence; Exact sciences and technology; Materials; Models, Neurological; multisensory convergence; Nerve Net - physiology; network of spiking neurons; Neuronal Plasticity; Neurons; reverse engineering; Synapses - physiology; Synaptic Potentials; Training
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2011.2125962

Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are formed. This lack of knowledge is due to the difficulty for biological experiments to manipulate and test the parameters of multisensory convergence, the first and definitive step in the multisensory process. Therefore, by using a computational model of multisensory convergence, this study seeks to provide insight into the mechanisms of multisensory convergence. To reverse-engineer multisensory convergence, we used a biologically realistic neuron model and a biology-inspired plasticity rule, but did not make any a priori assumptions about multisensory properties of neurons in the network. The network consisted of two separate projection areas that converged upon neurons in a third area, and stimulation involved activation of one of the projection areas (or the other) or their combination. Experiments consisted of two parts: network training and multisensory simulation. Analyses were performed, first, to find multisensory properties in the simulated networks; second, to reveal properties of the network using graph theoretical approach; and third, to generate hypothesis related to the multisensory convergence. The results showed that the generation of multisensory neurons related to the topological properties of the network, in particular, the strengths of connections after training, was found to play an important role in forming and thus distinguishing multisensory neuron types.