A Normalization Model of Multisensory Integration

By Tomokazu Ohshiro, Dora E. Angelaki, Gregory C. DeAngelis. 2011

315 citations recorded by the source corpus.

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 shown by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which uses 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 important features of multisensory integration by neurons.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Sensory Systems

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Citations in the corpus

Looming Signals Reveal Synergistic Principles of Multisensory Integration (2012, result): “These results, in conjunction with the extant literature, highlight the challenge of directly transposing models of multisensory interactions from single-neuron to populationlevel responses and perception (Krueger et al., 2009; Ohshiro et al., 2011).”
Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes (2015, methodology): “Like the models proposed by Ohshiro et al. (2011) and Fetsch et al. (2013), our model uses divisive normalisation and the neurophysiological effects it reproduces depend in part on that.”
Multisensory integration: flexible use of general operations (2014, unknown): “Although speculative at this point, it seems plausible that the ‘‘temporal principle’’ of integration as explained by DN (Ohshiro et al., 2011) is fine-tuned through learning, e.g., by sharpening tuning of individual neurons that affects the population-level normalization process.”
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (2015, methodology): “Motivated by the multisensory integration process of primary neurons in biological systems [51], [52], we extend the basic DNN with structure regularization on an additional fusion layer to accommodate the deep fusion process of multiple types of features.”