scholarly journals Attention Promotes the Neural Encoding of Prediction Errors

2019 ◽  
Author(s):  
Cooper A. Smout ◽  
Matthew F. Tang ◽  
Marta I. Garrido ◽  
Jason B. Mattingley
Keyword(s):  
Information Processing ◽  
Coding Theory ◽  
Predictive Coding ◽  
Sensory Information ◽  
Neural Response ◽  
Prediction Errors ◽  
Feature Information ◽  
The Difference ◽  
Stimulus Features ◽  
The Brain

AbstractThe human brain is thought to optimise the encoding of incoming sensory information through two principal mechanisms: prediction uses stored information to guide the interpretation of forthcoming sensory events, and attention prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Since prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models proposes that attention increases the activity of prediction (or ‘representation’) neurons, and would therefore suggest that attention and prediction synergistically modulate selectivity for feature information in the brain. Here we applied multivariate forward encoding techniques to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task, to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, and support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.

scholarly journals Prediction Error and Repetition Suppression Have Distinct Effects on Neural Representations of Visual Information

2017 ◽  
Author(s):  
Matthew F. Tang ◽  
Cooper A. Smout ◽  
Ehsan Arabzadeh ◽  
Jason B. Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

AbstractPredictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

scholarly journals Prediction error and repetition suppression have distinct effects on neural representations of visual information

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Matthew F Tang ◽  
Cooper A Smout ◽  
Ehsan Arabzadeh ◽  
Jason B Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

Predictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here, we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

scholarly journals Backwards is the way forward: Feedback in the cortical hierarchy predicts the expected future

2013 ◽  
Vol 36 (3) ◽  
pp. 221-221 ◽  
Author(s):  
Lars Muckli ◽  
Lucy S. Petro ◽  
Fraser W. Smith
Keyword(s):  
Predictive Coding ◽  
Internal Models ◽  
Prediction Errors ◽  
Conceptual Level ◽  
Cortical Hierarchy ◽  
The Brain ◽  
Descriptive Level ◽  
Perception Action ◽  
The Way

AbstractClark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models).

scholarly journals Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Hyojin Bae ◽  
Sang Jeong Kim ◽  
Chang-Eop Kim
Keyword(s):  
Neural Networks ◽  
Neural Coding ◽  
Deep Neural Networks ◽  
Neural Response ◽  
Feature Representation ◽  
Network Feature ◽  
Biological Neural Networks ◽  
Stimulus Features ◽  
The Comparative Study ◽  
The Brain

One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

scholarly journals Dynamic compartmentalization in neurons enables branch-specific learning

2018 ◽  
Author(s):  
Willem A.M. Wybo ◽  
Benjamin Torben-Nielsen ◽  
Marc-Oliver Gewaltig
Keyword(s):  
Information Processing ◽  
Electrical Properties ◽  
Mathematical Formalism ◽  
Tree Graph ◽  
Dendritic Trees ◽  
Shunting Inhibition ◽  
Structural And Electrical Properties ◽  
Stimulus Features ◽  
The Brain ◽  
Specific Learning

AbstractThe dendritic trees of neurons play an important role in the information processing in the brain. While it is tacitly assumed that dendrites require independent compartments to perform most of their computational functions, it is still not understood how they compartmentalize into functional subunits. Here we show how these subunits can be deduced from the structural and electrical properties of dendrites. We devised a mathematical formalism that links the dendritic arborization to an impedance-based tree-graph and show how the topology of this tree-graph reveals independent dendritic compartments. This analysis reveals that coopera-tivity between synapses decreases less than depolarization with increasing electrical separation, and thus that surprisingly few independent subunits coexist on dendritic trees. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of compartments in a context-dependent, temporal manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features.

scholarly journals Information theoretic evidence for predictive coding in the face processing system

2016 ◽  
Author(s):  
Alla Brodski-Guerniero ◽  
Georg-Friedrich Paasch ◽  
Patricia Wollstadt ◽  
Ipek Özdemir ◽  
Joseph T. Lizier ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Human Subjects ◽  
Brain Area ◽  
Predictive Coding ◽  
Sensory Information ◽  
Low Frequency ◽  
Processing System ◽  
Information Storage ◽  
Beta Band ◽  
The Brain

AbstractPredictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre-)activated prior knowledge serving these predictions are still unknown. Based on the idea that such pre-activated prior knowledge must be maintained until needed we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time-courses from magnetoencephalography (MEG) recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Pre-activation of prior knowledge for faces showed as alpha- and beta-band related AIS increases in content specific areas; these AIS increases were behaviourally relevant in brain area FFA. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Moreover, top-down transfer of predictions estimated by transfer entropy was associated with beta frequencies. Our results support accounts that activated prior knowledge and the corresponding predictions are signalled in low-frequency activity (<30 Hz).Significance statementOur perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains like prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare – mostly because this kind of evidence requires making strong a-priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach we find that face-related prior knowledge and the derived predictions are represented and transferred in low-frequency brain activity.

scholarly journals Can expectation suppression be explained by reduced attention to predictable stimuli?

2020 ◽  
Author(s):  
Arjen Alink ◽  
Helen Blank
Keyword(s):  
Predictive Coding ◽  
Additional Research ◽  
Prediction Errors ◽  
Evoked Responses ◽  
Suppression Effect ◽  
Attention Effect ◽  
Neural Representations ◽  
Stimulus Features

AbstractThe expectation-suppression effect – reduced stimulus-evoked responses to expected stimuli – is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that this phenomenon can also be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. To resolve whether expectation suppression is best explained by attention or predictive coding, additional research is needed to determine whether attention effects precede the encoding of expectation violations (or vice versa) and to reveal how expectations change neural representations of stimulus features.

scholarly journals Principles for models of neural information processing

2017 ◽  
Author(s):  
Kendrick N. Kay ◽  
Kevin S. Weiner
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Brain Function ◽  
Network Models ◽  
Neural Network Models ◽  
Specific Effects ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Difference ◽  
The Brain

AbstractThe goal of cognitive neuroscience is to understand how mental operations are performed by the brain. Given the complexity of the brain, this is a challenging endeavor that requires the development of formal models. Here, we provide a perspective on models of neural information processing in cognitive neuroscience. We define what these models are, explain why they are useful, and specify criteria for evaluating models. We also highlight the difference between functional and mechanistic models, and call attention to the value that neuroanatomy has for understanding brain function. Based on the principles we propose, we proceed to evaluate the merit of recently touted deep neural network models. We contend that these models are promising, but substantial work is necessary to (i) clarify what type of explanation these models provide, (ii) determine what specific effects they accurately explain, and (iii) improve our understanding of how they work.

scholarly journals Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect

2020 ◽  
Vol 30 (10) ◽  
pp. 5204-5217
Author(s):  
Adrien Witon ◽  
Amirali Shirazibehehsti ◽  
Jennifer Cooke ◽  
Alberto Aviles ◽  
Ram Adapa ◽  
...  
Keyword(s):  
Cognitive Neuroscience ◽  
Predictive Coding ◽  
Local Effect ◽  
Second Phase ◽  
Prediction Errors ◽  
Global Effect ◽  
Third Phase ◽  
Research Task ◽  
The Brain ◽  
Short Timeframe

Abstract Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task—an experimental paradigm that engages a predictive hierarchy, which encompasses the GW. The results of our source reconstruction suggest three phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions, consistent with a global neuronal workspace (GNW; as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a PC explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the GNW.

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