Balanced crossmodal excitation and inhibition essential for maximizing multisensory gain.
We examined whether and how the balancing of crossmodal excitation and inhibition affects intersensory facilitation. A neural network model, comprising lower-order unimodal networks (X, Y) and a higher-order multimodal network (M), was simulated. Crossmodal excitation was made by direct activation of principal cells of the X network by the Y network. Crossmodal inhibition was made in an indirect manner: the Y network activated glial cells of the X network. This let glial plasma membrane transporters export GABA molecules into the extracellular space and increased the level of ambient GABA. The ambient GABA molecules were accepted by extrasynaptic GABAa receptors and tonically inhibited principal cells of the X network. Namely, crossmodal inhibition was made through GABAergic gliotransmission. Intersensory facilitation was assessed in terms of multisensory gain: the difference between the numbers of spikes evoked by multisensory (XY) stimulation and unisensory (X-alone) stimulation. The maximal multisensory gain (XY-X) could be achieved at an intermediate noise level by balancing crossmodal excitation and inhibition. This result supports an experimentally derived conclusion: intersensory facilitation under noisy environmental conditions is not necessarily in accord with the principle of inverse effectiveness; rather, multisensory gain is maximal at intermediate signal-to-noise ratio (SNR) levels. The maximal multisensory gain was available at the weakest signal if noise was not present, indicating that the principle of inverse effectiveness is a special case of the intersensory facilitation model proposed here. We suggest that the balancing of crossmodal excitation and inhibition may be crucial for intersensory facilitation. The GABAergic glio-transmission-mediated crossmodal inhibitory mechanism effectively works for intersensory facilitation and on determining the maximal multisensory gain in the entire SNR range between the two extremes: low and high SNRs.
Department of Intelligent Systems Engineering, Ibaraki University, Hitachi, Ibaraki, 316-8511, Japan hoshino@mx.ibaraki.ac.jp.
Osamu
O
Hoshino
D008564:Membrane Potentials / Q000502:physiology; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D009457:Neuroglia / Q000502:physiology; D009474:Neurons / Q000502:physiology; D011963:Receptors, GABA-A / Q000378:metabolism; D005680:gamma-Aminobutyric Acid / Q000378:metabolism
D011963:Receptors, GABA-A; D005680:gamma-Aminobutyric Acid