Multisensory integration in the superior colliculus: a neural network model

Ursino, Mauro; Cuppini, Cristiano; Magosso, Elisa; Serino, Andrea; Pellegrino, Giuseppe di

doi:10.1007/s10827-008-0096-4

Cited by 37 publications

(40 citation statements)

References 43 publications

(101 reference statements)

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“…Ursino et al (2009) proposed an alternative model of multi-sensory integration that accounts for the principle of inverse-effectiveness and other key properties of multisensory integration in the superior colliculus (Cuppini et al, 2010). This alternative model, which is based on subtractive inhibition, makes a distinct prediction from the normalization model with regard to cross-modal suppression (Ohshiro et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Ohshiro

Angelaki

DeAngelis

2017

Neuron

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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.

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Section: Resultsmentioning

confidence: 99%

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Ohshiro

Angelaki

DeAngelis

2017

Neuron

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“…Neural network models can provide some neuronal explanations for it (Ursino, Cuppini, Magosso, Serino, & di Pellegrino, 2009;Cuppini, Ursino, Magosso, Rowland, & Stein, 2010). Our neuronal modeling study may provide new insight into the understanding of multisensory integration for subthreshold stimuli.…”

Section: Discussionmentioning

confidence: 96%

Neuronal Responses Below Firing Threshold for Subthreshold Cross-Modal Enhancement

Hoshino

2011

Neural Computation

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Multisensory integration (such as somatosensation-vision, gustation-olfaction) could occur even between subthreshold stimuli that in isolation do not reach perceptual awareness. For example, when a somatosensory (subthreshold) stimulus is delivered within a close spatiotemporal congruency, a visual (subthreshold) stimulus evokes a visual percept. Cross-modal enhancement of visual perception is maximal when the somatosensory stimulation precedes the visual one by tens of milliseconds. This rapid modulatory response would not be consistent with a top-down mechanism acting through higher-order multimodal cortical areas, but rather a direct interaction between lower-order unimodal areas. To elucidate the neuronal mechanisms of subthreshold cross-modal enhancement, we simulated a neural network model. In the model, lower unimodal (X, Y) and higher multimodal (M) networks are reciprocally connected by bottom-up and top-down axonal projections. The lower networks are laterally connected with each other. A pair of stimuli was presented to the lower networks, whose respective intensities were too weak to induce salient neuronal activity (population response) when presented alone. Neurons of the Y network were slightly depolarized below firing threshold when a cross-modal stimulus was presented alone to the X network. This allowed the Y network to make a rapid (within tens of milliseconds) population response when presented with a subsequent congruent stimulus. The reaction speed of the Y network was accelerated, provided that the top-down projections were strengthened. We suggest that a subthreshold (nonpopulation) response to a cross-modal stimulus, acting through interaction between lower (primary unisensory) areas, may be essential for a rapid suprathreshold (population) response to a congruent stimulus that follows. Top-down influences on cross-modal enhancement may be faster than expected, accelerating reaction speed to input, in which ongoing-spontaneous subthreshold excitation of lower-order unimodal cells by higher-order multimodal cells may play an active role.

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“…Multisensory information could, for example, be processed in a centralized or decentralized manner. In a centralized system information received through all sensory systems would be fed into a single integration center, where the multiple inputs would be integrated (47,48). In contrast, in a decentralized system the integration would be achieved through the interconnection of many multisensory areas (49).…”

Section: Discussionmentioning

confidence: 99%

Cross-modal object recognition and dynamic weighting of sensory inputs in a fish

Schumacher

Perera

Thenert

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal object recognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an objectdiscrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal object recognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that crossmodal object recognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal object recognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate. multisensory integration | perception | sensory transfer | sensory reliability | sensory conflict

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Multisensory integration in the superior colliculus: a neural network model

Cited by 37 publications

References 43 publications

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Neuronal Responses Below Firing Threshold for Subthreshold Cross-Modal Enhancement

Cross-modal object recognition and dynamic weighting of sensory inputs in a fish

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