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Sunday, July 19 • 9:00pm - 10:00pm
P134: A fluid hierarchy in multisensory integration properties of large-scale cortical networks

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Zoom link: https://uva-live.zoom.us/j/99497243655

Ronaldo Nunes, Marcelo Reyes, Raphael de Camargo, Jorge Mejias
A fundamental ingredient for perception is the integration of information from different sensory modalities. This process, known as multisensory integration (MSI), has been studied extensively using animal and computational models [1]. It is not yet clear, however, how different brain areas contribute to MSI, and identifying relevant areas remains challenging. Part of the reason is that simultaneous electrophysiological recordings from different brain areas has developed only recently [2], and the intensity, noise profile and delay responses are diverse for different sensory signals [1]. Furthermore, computational models have traditionally focused only on a few areas, a limitation imposed by the lack of reliable anatomical data on brain networks.

We present here a theoretical and computational study of the mechanisms underlying MSI in the mouse brain, by constraining our model with a recently acquired anatomical brain connectivity dataset [3]. Our simulations of the resulting large-scale cortical network reveal the existence of a hierarchy of crossmodal excitability properties, with areas at the top of the hierarchy being the best candidates for integrating information from multiple modalities. Furthermore, our model predicts that the position of a given area in such hierarchy is highly fluid and depends on the strength of the sensory input received by the network. For example, we observe that the particular set of areas integrating visuotactile stimuli changes depending on the level of visual contrast. By simulating a simplified network model and developing its corresponding mean-field approximation, we determine that the origin of such hierarchical dynamics is the structural heterogeneity of the network, which is a salient property of cortical networks [3, 4]. Finally, we extend our results to macaque cortical networks [5] to show that the hierarchy of crossmodal excitability is also present in other mammals, and we characterize how frequency-specific interactions are affected by hierarchical dynamics and define functional connectivity [6]. Our work provides a compelling explanation as of why is it not possible to identify unique MSI areas even for a well- defined multisensory task, and suggests that MSI circuits are highly context- dependent.


This study was financed in part by the University of Amsterdam and the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil (Capes) - Finance Code 001.


1) Chandrasekaran C. Computational principles and models of multisensory integration. Curr. Op. Neurobiol. 2017, 43, 25-34.

2) Hong G, Lieber CM. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 2019, 20, 330-45.

3) Gamanut R et al. The mouse cortical connectome, characterized by an ultra-dense cortical graph, maintains specificity by distinct connectivity profiles. Neuron 2018, 97, 698-715.

4) Mejias JF, Longtin A. Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks. Front. Comput. Neurosci. 2014, 8, 107.

5) Mejias JF, Murray JD, Kennedy H, Wang XJ. Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Sci. Adv. 2016, 2:e1601335.

6) Nunes RV, Reyes MB, De Camargo RY. Evaluation of connectivity estimates using spiking neuronal network models. Biol. Cybern. 2019, 113, 309-320.

avatar for Jorge Mejias

Jorge Mejias

Swammerdam Institute for Life Sciences, University of Amsterdam

Sunday July 19, 2020 9:00pm - 10:00pm CEST
Slot 19