Gamma response of the brain: a multifunctional oscillation that represents bottom-up with top-down processing

2001 ◽  
Vol 39 (2-3) ◽  
pp. 137-150 ◽  
Author(s):  
S Karakaş ◽  
C Başar-Eroğlu ◽  
Ç Özesmi ◽  
H Kafadar ◽  
Ö.Ü Erzengin
Keyword(s):  
Top Down ◽  
Bottom Up ◽  
The Brain

Top-Down Predictions Determine Perceptions

2017 ◽  
Author(s):  
Martin V. Butz ◽  
Esther F. Kutter
Keyword(s):  
Graphical Models ◽  
Information Integration ◽  
Visual Processing ◽  
Visual Information ◽  
Generative Models ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Top Down ◽  
Bottom Up ◽  
The Brain

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

ARTISTIC COMPLEXITY AND SALIENCY: TWO FACES OF THE SAME COIN?

2013 ◽  
Vol 09 (02) ◽  
pp. 1350010 ◽  
Author(s):  
MATTEO CACCIOLA ◽  
GIANLUIGI OCCHIUTO ◽  
FRANCESCO CARLO MORABITO
Keyword(s):  
Information Content ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Observer ◽  
Top Down ◽  
Complexity Measures ◽  
Bottom Up ◽  
Content Description ◽  
Brain Processing ◽  
The Brain

Many computer vision problems consist of making a suitable content description of images usually aiming to extract the relevant information content. In case of images representing paintings or artworks, the information extracted is rather subject-dependent, thus escaping any universal quantification. However, we proposed a measure of complexity of such kinds of oeuvres which is related to brain processing. The artistic complexity measures the brain inability to categorize complex nonsense forms represented in modern art, in a dynamic process of acquisition that most involves top-down mechanisms. Here, we compare the quantitative results of our analysis on a wide set of paintings of various artists to the cues extracted from a standard bottom-up approach based on visual saliency concept. In every painting inspection, the brain searches for more informative areas at different scales, then connecting them in an attempt to capture the full impact of information content. Artistic complexity is able to quantify information which might have been individually lost in the fruition of a human observer thus identifying the artistic hand. Visual saliency highlights the most salient areas of the paintings standing out from their neighbours and grabbing our attention. Nevertheless, we will show that a comparison on the ways the two algorithms act, may manifest some interesting links, finally indicating an interplay between bottom-up and top-down modalities.

scholarly journals The cutaneous borders of interoception: Active and social inference of pain and pleasure on the skin

2018 ◽  
Author(s):  
Mariana von Mohr ◽  
Aikaterini Fotopoulou
Keyword(s):  
Social Factors ◽  
Predictive Coding ◽  
Active Inference ◽  
Top Down ◽  
Bottom Up ◽  
Social Inference ◽  
Brain Levels ◽  
The Brain

Pain and pleasant touch have been recently classified as interoceptive modalities. This reclassification lies at the heart of long-standing debates questioning whether these modalities should be defined as sensations on their basis of neurophysiological specificity at the periphery or as homeostatic emotions on the basis of top-down convergence and modulation at the spinal and brain levels. Here, we outline the literature on the peripheral and central neurophysiology of pain and pleasant touch. We next recast this literature within a recent Bayesian predictive coding framework, namely active inference. This recasting puts forward a unifying model of bottom-up and top-down determinants of pain and pleasant touch and the role of social factors in modulating the salience of peripheral signals reaching the brain.

scholarly journals Circular inference in bistable perception

2019 ◽  
Author(s):  
Pantelis Leptourgos ◽  
Charles-Edouard Notredame ◽  
Marion Eck ◽  
Renaud Jardri ◽  
Sophie Denève
Keyword(s):  
Prior Knowledge ◽  
Bayesian Approach ◽  
Necker Cube ◽  
Top Down ◽  
Inference Model ◽  
Bottom Up ◽  
Bistable Perception ◽  
Sensory Evidence ◽  
The Brain ◽  
Healthy Participants

AbstractWhen facing fully ambiguous images, the brain cannot commit to a single percept and instead switches between mutually exclusive interpretations every few seconds, a phenomenon known as bistable perception. Despite years of research, there is still no consensus on whether bistability, and perception in general, is driven primarily by bottom-up or top-down mechanisms. Here, we adopted a Bayesian approach in an effort to reconcile these two theories. Fifty-five healthy participants were exposed to an adaptation of the Necker cube paradigm, in which we manipulated sensory evidence (by shadowing the cube) and prior knowledge (e.g., by varying instructions about what participants should expect to see). We found that manipulations of both sensory evidence and priors significantly affected the way participants perceived the Necker cube. However, we observed an interaction between the effect of the cue and the effect of the instructions, a finding incompatible with Bayes-optimal integration. In contrast, the data were well predicted by a circular inference model. In this model, ambiguous sensory evidence is systematically biased in the direction of current expectations, ultimately resulting in a bistable percept.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Benjamin Schuman ◽  
Shlomo Dellal ◽  
Alvar Prönneke ◽  
Robert Machold ◽  
Bernardo Rudy
Keyword(s):  
Pyramidal Cells ◽  
Apical Dendrite ◽  
Annual Review ◽  
Publication Date ◽  
Top Down ◽  
Bottom Up ◽  
Violin Concerto ◽  
States Of Consciousness ◽  
The Brain ◽  
Tennis Match

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals An ALE meta-analytic review of top-down and bottom-up processing of music in the brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Victor Pando-Naude ◽  
Agata Patyczek ◽  
Leonardo Bonetti ◽  
Peter Vuust
Keyword(s):  
Music Perception ◽  
Likelihood Estimation ◽  
Music Listening ◽  
Top Down ◽  
Bottom Up ◽  
Remarkable Feature ◽  
Analytic Review ◽  
Auditory Cortices ◽  
Meta Analyses ◽  
The Brain

AbstractA remarkable feature of the human brain is its ability to integrate information from the environment with internally generated content. The integration of top-down and bottom-up processes during complex multi-modal human activities, however, is yet to be fully understood. Music provides an excellent model for understanding this since music listening leads to the urge to move, and music making entails both playing and listening at the same time (i.e., audio-motor coupling). Here, we conducted activation likelihood estimation (ALE) meta-analyses of 130 neuroimaging studies of music perception, production and imagery, with 2660 foci, 139 experiments, and 2516 participants. We found that music perception and production rely on auditory cortices and sensorimotor cortices, while music imagery recruits distinct parietal regions. This indicates that the brain requires different structures to process similar information which is made available either by an interaction with the environment (i.e., bottom-up) or by internally generated content (i.e., top-down).

scholarly journals Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

2018 ◽  
Author(s):  
Noam Gordon ◽  
Naotsugu Tsuchiya ◽  
Roger Koenig-Robert ◽  
Jakob Hohwy
Keyword(s):  
Predictive Coding ◽  
Sensory Information ◽  
Phase Coherence ◽  
Signal Integration ◽  
Physiological Measures ◽  
Top Down ◽  
Bottom Up ◽  
Visual Hierarchy ◽  
Linear Signal ◽  
The Brain

AbstractPerception results from the integration of incoming sensory information with pre-existing information available in the brain. In this EEG (electroencephalography) study we utilised the Hierarchical Frequency Tagging method to examine how such integration is modulated by expectation and attention. Using intermodulation (IM) components as a measure of non-linear signal integration, we show in three different experiments that both expectation and attention enhance integration between top-down and bottom-up signals. Based on multispectral phase coherence, we present two direct physiological measures to demonstrate the distinct yet related mechanisms of expectation and attention. Specifically, our results link expectation to the modulation of prediction signals and the integration of top-down and bottom-up information at lower levels of the visual hierarchy. Meanwhile, they link attention to the propagation of ascending signals and the integration of information at higher levels of the visual hierarchy. These results are consistent with the predictive coding account of perception.

scholarly journals Time scale separation of information processing between sensory and associative regions

2020 ◽  
Author(s):  
P Sorrentino ◽  
G Rabuffo ◽  
R Rucco ◽  
F Baselice ◽  
E Troisi Lopez ◽  
...  
Keyword(s):  
Information Processing ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Speed Of Information Processing ◽  
The Brain

AbstractStimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Here we used high temporal-resolution Magnetoencephalography (MEG) signals to estimate the persistence of the information in functional links across the brain. We show that short- and long-lasting retention of the information, entailing different speeds in the update rate, naturally split the brain into two anatomically distinct subnetworks. The “fast updating network” (FUN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the “slow updating network” (SUN) hinges classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belongs to both subnetworks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.Significance statementThe human brain constantly scans the environment in search of relevant incoming stimuli, and appropriately reconfigures its large-scale activation according to environmental requests. The functional organization substanding these bottom-up and top-down processes, however, is not understood. Studying the speed of information processing between brain regions during resting state, we show the existence of two spatially segregated subnetworks processing information at fast- and slow-rates. Notably, these networks involve the regions that typically belong to the perception stream and the associative regions, respectively. Therefore, we provide evidence that, regardless of the presence of a stimulus, the bottom-up and top-down perceptive pathways are inherent to the resting state dynamics.

Conclusions

2020 ◽  
pp. 140-142
Author(s):  
Thomas Boraud
Keyword(s):  
Decision Making ◽  
Conceptual Framework ◽  
Neural Correlates ◽  
Top Down ◽  
Bottom Up ◽  
Decision Making Processes ◽  
Set Up ◽  
The Brain ◽  
Rational Process

This concluding chapter summarizes the book’s content. Twenty-five years since Schultz’s, Newsome’s, and Movshon’s experiments, neuroscientists joined the community of psychologists and economists who studied rationality. Together, they were able to set up experimental paradigms to try to better understand the neurobiological foundations of the learning and decision-making processes. The conceptual framework defined by all of these paradigms has been called neuroeconomics. Neuroeconomics is therefore the study of the neural correlates of rationality. As such, this book can be considered as an essay on neuroeconomics. In contrast to the classical top-down approach, the text proposes a bottom-up approach on decision-making. By addressing the problem of rationality from a dual connectionist and evolutionist perspective, the book highlights that the brain is not capable of producing a purely rational process.

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