Neurocomputational approaches to modelling multisensory integration in the brain: A review

Ursino, Mauro; Cuppini, Cristiano; Magosso, Elisa

doi:10.1016/j.neunet.2014.08.003

Cited by 64 publications

(57 citation statements)

References 193 publications

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“…The brain process of weighing sensory information from the environment follows the principle of Bayesian integration (Ernst, 2006; Bates and Wolbers, 2014; Ursino et al, 2014). This process aims to increase the accuracy of the percept by reducing its uncertainty (Bates and Wolbers, 2014).…”

Section: Discussion Of Findingsmentioning

confidence: 99%

“…Stimulus information comes to a person through different modalities, for instance, the size of an object can be estimated through vision and haptics. The Bayesian model assumes that the brain weighs each signal optimally with respect to its variance and combines them into one estimate with a smaller variance than the variance of the individual estimates (Ernst, 2006; Ursino et al, 2014). According to the maximum likelihood estimation, the reliability of the combined estimate is the sum of the individual estimates (Ernst, 2006), i.e., it is generally valuable to integrate stimuli from different modalities as OA seem to do.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

“…Computational studies have been done considering different areas of the brain (Anastasio et al, 2000; Colonius and Diederich, 2004b; Gu et al, 2008; Ursino et al, 2014). The authors found that the neurons present in the SC and the dorsal medial superior temporal area use the Bayesian rule to integrate and weigh multisensory information to arrive at a least variable percept.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Aging in Multisensory Integration: A Systematic Review

Dieuleveult

Siemonsma

Erp

et al. 2017

Front. Aging Neurosci.

147

149

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Multisensory integration (MSI) is the integration by the brain of environmental information acquired through more than one sense. Accurate MSI has been shown to be a key component of successful aging and to be crucial for processes underlying activities of daily living (ADLs). Problems in MSI could prevent older adults (OA) to age in place and live independently. However, there is a need to know how to assess changes in MSI in individuals. This systematic review provides an overview of tests assessing the effect of age on MSI in the healthy elderly population (aged 60 years and older). A literature search was done in Scopus. Articles from the earliest records available to January 20, 2016, were eligible for inclusion if assessing effects of aging on MSI in the healthy elderly population compared to younger adults (YA). These articles were rated for risk of bias with the Newcastle-Ottawa quality assessment. Out of 307 identified research articles, 49 articles were included for final review, describing 69 tests. The review indicated that OA maximize the use of multiple sources of information in comparison to YA (20 studies). In tasks that require more cognitive function, or when participants need to adapt rapidly to a situation, or when a dual task is added to the experiment, OA have problems selecting and integrating information properly as compared to YA (19 studies). Additionally, irrelevant or wrong information (i.e., distractors) has a greater impact on OA than on YA (21 studies). OA failing to weigh sensory information properly, has not been described in previous reviews. Anatomical changes (i.e., reduction of brain volume and differences of brain areas’ recruitment) and information processing changes (i.e., general cognitive slowing, inverse effectiveness, larger time window of integration, deficits in attentional control and increased noise at baseline) can only partly explain the differences between OA and YA regarding MSI. Since we have an interest in successful aging and early detection of MSI issues in the elderly population, the identified tests form a good starting point to develop a clinically useful toolkit to assess MSI in healthy OA.

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Section: Discussion Of Findingsmentioning

confidence: 99%

Section: Discussion Of Findingsmentioning

confidence: 99%

Section: Discussion Of Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Aging in Multisensory Integration: A Systematic Review

Dieuleveult

Siemonsma

Erp

et al. 2017

Front. Aging Neurosci.

147

149

View full text Add to dashboard Cite

show abstract

“…The phenomenological aspects of inverse effectiveness and other properties of MSI are well described in the vertebrate optic tectum or superior colliculus (Stein and Stanford, 2008; Stein et al, 2014; Meredith and Stein, 1983, 1986), but less is known about the cellular mechanisms underlying these processes or how these responses give rise to behavior (Stein et al, 1988, 1989). While several cellular models have been put forth to explain inverse effectiveness (Cuppini et al, 2012; Ursino et al, 2014; Stein et al, 2009), one stumbling block toward testing these has been the lack of a robust, experimentally tractable model system that is easily assessable at multiple levels of analysis, from synapses to behavior. The Xenopus laevis tadpole optic tectum has emerged as a preparation in which we can study MSI at the single cell, network and behavioral levels (Deeg et al, 2009; Felch et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A cellular mechanism for inverse effectiveness in multisensory integration

Truszkowski

Carrillo

Bleier

et al. 2017

eLife

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To build a coherent view of the external world, an organism needs to integrate multiple types of sensory information from different sources, a process known as multisensory integration (MSI). Previously, we showed that the temporal dependence of MSI in the optic tectum of Xenopus laevis tadpoles is mediated by the network dynamics of the recruitment of local inhibition by sensory input (Felch et al., 2016). This was one of the first cellular-level mechanisms described for MSI. Here, we expand this cellular level view of MSI by focusing on the principle of inverse effectiveness, another central feature of MSI stating that the amount of multisensory enhancement observed inversely depends on the size of unisensory responses. We show that non-linear summation of crossmodal synaptic responses, mediated by NMDA-type glutamate receptor (NMDARs) activation, form the cellular basis for inverse effectiveness, both at the cellular and behavioral levels.DOI: http://dx.doi.org/10.7554/eLife.25392.001

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“…Artificial Neural Networks, fuzzy logic, genetic algorithms and immunity system are being widely used in industrial applications [5]- [9]. Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune function.…”

Section: Developed Mppt Controllermentioning

confidence: 99%

Tracking and Controlling Maximum Power Point Utilizing Artificial Intelligent System

Hamied¹

2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

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Neurocomputational approaches to modelling multisensory integration in the brain: A review

Cited by 64 publications

References 193 publications

Effects of Aging in Multisensory Integration: A Systematic Review

Effects of Aging in Multisensory Integration: A Systematic Review

A cellular mechanism for inverse effectiveness in multisensory integration

Tracking and Controlling Maximum Power Point Utilizing Artificial Intelligent System

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