Responses of neurons that integrate multiple sensory inputs are traditionally characterized in terms of a set of empirical principles. However, a simple computational framework that accounts for these empirical features of multisensory integration has not been established. We propose that divisive normalization, acting at the stage of multisensory integration, can account for many of the empirical principles of multisensory integration exhibited by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which employs a simple functional operation (normalization) for which there is considerable experimental support, also accounts for the recent observation that the mathematical rule by which multisensory neurons combine their inputs changes with cue reliability. The normalization model, which makes a strong testable prediction regarding cross-modal suppression, may therefore provide a simple unifying computational account of the key features of multisensory integration by neurons.

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

GABAergic dysfunctions have been implicated in the pathogenesis of schizophrenia, especially the associated cognitive impairments. The GABA synthetic enzyme glutamate decarboxylase 67-kDa isoform (GAD67) encoded by the GAD1 gene is downregulated in the brains of patients with schizophrenia. Furthermore, a patient with schizophrenia harboring a homozygous mutation of GAD1 has recently been discovered. However, it remains unclear whether loss of function of GAD1 leads to the symptoms observed in schizophrenia, including cognitive impairment. One of the obstacles faced in experimental studies to address this issue is the perinatal lethality of Gad1 knockout (KO) mice, which precluded characterization at the adult stage. In the present study, we successfully generated Gad1 KO rats using CRISPR/Cas9 genome editing technology. Surprisingly, 33% of Gad1 KO rats survived to adulthood and could be subjected to further characterization. The GABA concentration in the Gad1 KO cerebrum was reduced to ~52% of the level in wild-type rats. Gad1 KO rats exhibited impairments in both spatial reference and working memory without affecting adult neurogenesis in the hippocampus. In addition, Gad1 KO rats showed a wide range of behavioral alterations, such as enhanced sensitivity to an NMDA receptor antagonist, hypoactivity in a novel environment, and decreased preference for social novelty. Taken together, the results suggest that Gad1 KO rats could provide a novel model covering not only cognitive deficits but also other aspects of the disorder. Furthermore, the present study teaches an important lesson: differences between species should be considered when developing animal models of human diseases.

No abstract