scholarly journals Cortico-Fugal Regulation of Predictive Coding

2021 ◽  
Author(s):  
Alexandria M.H. Lesicko ◽  
Christopher F. Angeloni ◽  
Jennifer M. Blackwell ◽  
Mariella De Biasi ◽  
Maria N. Geffen
Keyword(s):  
Auditory Cortex ◽  
Prediction Error ◽  
Predictive Coding ◽  
Brain Regions ◽  
Central Nucleus ◽  
Predictive Processing ◽  
Subcortical Structures ◽  
Cortical Input ◽  
Repetition Suppression ◽  
Error Suppression

ABSTRACTSensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the auditory cortex to subcortical structures. To assess the role of these projections in predictive coding, we optogenetically suppressed the auditory cortico-collicular feedback in awake mice while recording responses from IC neurons to stimuli designed to test prediction error and repetition suppression. Suppression of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC neurons that exhibit repetition enhancement, an increase in firing with stimulus repetition, and error suppression, a stronger response to a tone in a predictable rather than unpredictable context. Cortico-collicular suppression led to a decrease in repetition enhancement in the central nucleus and a reduction in error suppression in shell regions of IC. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that neurons in IC responded more similarly to each context in the absence of cortical input. Our results demonstrate that the auditory cortex provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

ERP Repetition Effects and Mismatch Negativity Generation

2007 ◽  
Vol 21 (3-4) ◽  
pp. 204-213 ◽  
Author(s):  
Torsten Baldeweg
Keyword(s):  
Auditory Cortex ◽  
Perceptual Learning ◽  
Empirical Evidence ◽  
Mismatch Negativity ◽  
Prediction Error ◽  
Predictive Coding ◽  
Repetition Effects ◽  
Specific Adaptation ◽  
Repetition Suppression ◽  
Trace Formation

Neuronal adaptation is a ubiquitous property of the cortex. This review presents evidence from MMN studies that show ERP components with similar adaptive properties. Specifically, I consider the empirical evidence from the perspective of a predictive coding model of perceptual learning and inference. Within this framework, ERP and neuronal repetition effects (repetition suppression) are seen as reductions in prediction error, a process that requires synaptic modifications. Repetition positivity is a human auditory ERP component, which shows similar properties to stimulus-specific adaptation of auditory cortex neurons; a candidate mechanism for auditory trace formation.

scholarly journals Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

scholarly journals Evaluating the Evidence for Expectation Suppression in the Visual System

2020 ◽  
Author(s):  
Daniel Feuerriegel ◽  
Rufin Vogels ◽  
Gyula Kovacs
Keyword(s):  
Visual System ◽  
Statistical Learning ◽  
Neural Activity ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Activity Measurement ◽  
Confounding Factors ◽  
Perceptual Decision Making ◽  
Repetition Suppression ◽  
Specific Subset

Expectation suppression is defined as a reduction in a measure of neural activity following an expected stimulus compared to a stimulus that is neither expected nor surprising. Reports of expectation suppression have shaped the development of several influential predictive coding-based theories of visual perception. However recent work has highlighted multiple confounding factors that may mimic or inflate observed expectation suppression effects. In this review, we describe four confounds that are prevalent across studies that have tested for expectation suppression: surprise-related response modulations, effects of attention, stimulus repetition and adaptation, and effects of stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We report that there is evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. However, across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was a lack of consistent evidence for genuine expectation suppression within the visual system that cannot be accounted for by confounding factors. To underline the importance of devising more appropriate tests for expectation suppression we discuss how an absence of this effect would inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for stimulus expectation effects in future work.

scholarly journals Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

AbstractAccording to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Disentangling Predictive Processing in the Brain: A Meta-analytic Study in Favour of a Predictive Network

2021 ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signal. Despite extensive research has investigated the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a task-based meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Prior precision modulates the minimisation of prediction error in human auditory cortex

2018 ◽  
Author(s):  
Yi-Fang Hsu ◽  
Florian Waszak ◽  
Jarmo A. Hämäläinen
Keyword(s):  
Auditory Cortex ◽  
High Precision ◽  
Prediction Error ◽  
Sensory Input ◽  
Predictive Coding ◽  
Random Point ◽  
Probe Tone ◽  
Source Power ◽  
Cortical Dynamics ◽  
Inverse Variance

AbstractThe predictive coding model of perception proposes that successful representation of the perceptual world depends upon cancelling out the discrepancy between prediction and sensory input (i.e., prediction error). Recent studies further suggest a distinction between prediction error associated with non-predicted stimuli of different prior precision (i.e., inverse variance). However, it is not fully understood how prediction error from different precision levels is minimised in the predictive process. The current research used magnetoencephalography (MEG) to examine whether prior precision modulates the cortical dynamics of the making of perceptual inferences. We presented participants with cycles of repeated tone quartets which consisted of three prime tones and one probe tone. Within each cycle, the three prime tones remained identical while the probe tones changed at some random point (e.g., from repetition of 123X to repetition of 123Y). Therefore, the repetition of probe tones can reveal the development of perceptual inferences in low and high precision contexts depending on its position within the cycle. We found that the two conditions resemble each other in terms of N1m modulation (as both were associated with N1m suppression) but differ in terms of N2m modulation. While repeated probe tones in low precision context did not exhibit any modulatory effect, repeated probe tones in high precision context elicited a suppression and rebound of the N2m source power. The differentiation suggested that the minimisation of prediction error in low and high precision contexts likely involves distinct mechanisms.

scholarly journals Predictive Processing in Cognitive Robotics: A Review

2021 ◽  
Vol 33 (5) ◽  
pp. 1402-1432
Author(s):  
Alejandra Ciria ◽  
Guido Schillaci ◽  
Giovanni Pezzulo ◽  
Verena V. Hafner ◽  
Bruno Lara
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Cognitive Robotics ◽  
Predictive Processing ◽  
Limited Attention ◽  
Active Inference ◽  
Perceptual Inference ◽  
Energy Principle ◽  
Clear Cut ◽  
Processing Framework

Abstract Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.

scholarly journals Cerebral hierarchies: predictive processing, precision and the pulvinar

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140169 ◽  
Author(s):  
Ryota Kanai ◽  
Yutaka Komura ◽  
Stewart Shipp ◽  
Karl Friston
Keyword(s):  
Prediction Error ◽  
Message Passing ◽  
Predictive Coding ◽  
Gain Control ◽  
Predictive Processing ◽  
Special Focus ◽  
First Order ◽  
Neuronal Populations ◽  
Neurobiological Substrates ◽  
The Brain

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

scholarly journals Understanding Emotions: Origins and Roles of the Amygdala

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 823
Author(s):  
Goran Šimić ◽  
Mladenka Tkalčić ◽  
Vana Vukić ◽  
Damir Mulc ◽  
Ena Španić ◽  
...  
Keyword(s):  
Sensory Information ◽  
Anterior Cingulate ◽  
Central Nucleus ◽  
Subcortical Structures ◽  
Neuronal Populations ◽  
Efferent Projections ◽  
Short And Long Term ◽  
Understanding Emotions ◽  
Fight Or Flight Response ◽  
And Behavior

Emotions arise from activations of specialized neuronal populations in several parts of the cerebral cortex, notably the anterior cingulate, insula, ventromedial prefrontal, and subcortical structures, such as the amygdala, ventral striatum, putamen, caudate nucleus, and ventral tegmental area. Feelings are conscious, emotional experiences of these activations that contribute to neuronal networks mediating thoughts, language, and behavior, thus enhancing the ability to predict, learn, and reappraise stimuli and situations in the environment based on previous experiences. Contemporary theories of emotion converge around the key role of the amygdala as the central subcortical emotional brain structure that constantly evaluates and integrates a variety of sensory information from the surroundings and assigns them appropriate values of emotional dimensions, such as valence, intensity, and approachability. The amygdala participates in the regulation of autonomic and endocrine functions, decision-making and adaptations of instinctive and motivational behaviors to changes in the environment through implicit associative learning, changes in short- and long-term synaptic plasticity, and activation of the fight-or-flight response via efferent projections from its central nucleus to cortical and subcortical structures.

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