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Predictive coding

Predictive coding is a theoretical framework in computational neuroscience that posits the brain functions as a predictive machine, generating top-down predictions about sensory inputs from higher cortical levels and comparing them with bottom-up sensory data to compute and minimize prediction errors, thereby enabling efficient perception and learning.^[1] This hierarchical process, rooted in Bayesian inference, allows the brain to model the causes of sensory signals rather than passively encoding raw data, optimizing for statistical regularities in the environment.^[2] The concept traces its modern origins to early ideas of unconscious perceptual inference proposed by Hermann von Helmholtz in the 19th century, but it was formalized in the late 20th century through computational models.^[3] A seminal contribution came from Rajesh P. N. Rao and Dana H. Ballard in 1999, who developed a hierarchical neural network model for the visual cortex where feedback connections transmit predictions of lower-level activity, while feedforward pathways carry residual errors, explaining phenomena like extra-classical receptive field effects and endstopping in visual neurons.^[1] Karl Friston extended this framework in the 2000s by integrating it with the free-energy principle, framing prediction error minimization as a strategy to reduce surprise or free energy in generative models of the world, applicable across sensory modalities and cognitive functions.^[2]^[3] At its core, predictive coding operates through a multi-level hierarchy of cortical areas, where each level represents increasingly abstract features of the sensory world using latent variables.^[4] Predictions flow downward to anticipate activity at lower levels, and discrepancies—termed prediction errors—are propagated upward to update the internal model, with precision weighting modulating the influence of errors based on expected reliability.^[3] This mechanism aligns with empirical observations, such as repetition suppression in neural responses, where predictable stimuli elicit reduced activity due to fulfilled predictions.^[5] Learning occurs by adjusting model parameters to reduce long-term errors, akin to variational Bayesian inference.^[2] Predictive coding has broad implications beyond basic perception, influencing models of attention, where precision adjustments prioritize salient errors, and action, through active inference where predictions guide behavior to confirm expectations.^[3] It also informs computational psychiatry, linking aberrant prediction error signaling to disorders like schizophrenia, where excessive or imprecise errors may underlie hallucinations or delusions.^[6] In artificial intelligence, predictive coding inspires energy-efficient learning algorithms that mimic cortical hierarchies for tasks like image recognition.^[4] Ongoing research continues to test its neural plausibility through neuroimaging and electrophysiological studies, refining its role in unifying diverse brain functions.^[7]

Historical Development

Early Concepts in Cybernetics and Signal Processing

The foundational ideas of predictive coding emerged in the mid-20th century within the field of cybernetics, pioneered by Norbert Wiener during the 1940s. Wiener's work on feedback control systems, initially motivated by anti-aircraft fire control during World War II, involved predicting the future positions of targets through extrapolation of stationary time series, using linear filters to minimize prediction errors in noisy environments.^[8] This approach emphasized the role of negative feedback in stabilizing systems by comparing predicted outputs to actual observations and adjusting accordingly. In his seminal 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine, Wiener extended these engineering principles to biological systems, arguing that feedback mechanisms underpin adaptive behavior in living organisms, such as neural regulation and sensory-motor coordination. Parallel developments in signal processing built on Wiener's prediction theory, integrating it with information theory to address redundancy in communication channels. In the 1940s, Wiener formalized linear prediction as a method for estimating signal values based on past observations, optimizing filters to reduce mean-squared error and thereby enhance signal detection amid noise.^[9] Claude Shannon's 1948 A Mathematical Theory of Communication provided the theoretical underpinning by quantifying redundancy—the excess information in signals beyond what is necessary for reliable transmission—and demonstrating how exploiting statistical predictability could minimize channel capacity requirements while reducing errors.^[10] These concepts laid the groundwork for error-minimizing systems in engineering, where predictions of signal trajectories allowed for efficient encoding by transmitting only deviations from expected patterns. A key application of these ideas appeared in perceptual models during the 1960s, notably the "analysis-by-synthesis" framework proposed for speech recognition. In his 1960 paper, Kenneth N. Stevens introduced a model where incoming auditory signals are interpreted by generating internal predictions of possible speech sounds, then synthesizing and matching these hypotheses against the input to select the best fit, thereby minimizing perceptual discrepancies.^[11] This approach, building on cybernetic feedback and predictive filtering, highlighted how top-down expectations could guide bottom-up analysis in sensory processing, influencing early computational models of human audition. By the 1970s, these principles manifested in practical data compression techniques, such as differential pulse-code modulation (DPCM), which served as a direct precursor to broader predictive coding applications. DPCM, an extension of pulse-code modulation, predicts the current signal sample from previous ones and encodes only the difference (or prediction error), achieving significant bitrate reductions—often by factors of 2 to 4—for speech and other signals while maintaining fidelity. Pioneered in works like Bishnu S. Atal and Manfred R. Schroeder's 1970 paper on adaptive predictive coding, DPCM demonstrated how error signals could be quantized and transmitted efficiently, inspiring later adaptations in telecommunications and foreshadowing biological interpretations of prediction in sensory systems.^[12]

Key Milestones in Neuroscience

The concept of predictive coding in neuroscience traces its philosophical roots to Hermann von Helmholtz's theory of unconscious inference, proposed in the 1860s, which posited that perception involves the brain making automatic, inferential judgments about sensory inputs to construct a coherent view of the world, often without conscious awareness.^[13] This idea laid a foundational precursor for later neuroscientific models by emphasizing top-down influences on perception, though it remained largely qualitative until the late 20th century. An early neurophysiological application appeared in 1982, when Mandyam V. Srinivasan, Simon B. Laughlin, and Andreas Dubs proposed predictive coding as a mechanism for inhibition in the retina. Their model suggested that retinal neurons predict the activity of neighboring cells based on spatial correlations in natural images, transmitting only prediction errors to reduce redundancy and enhance efficient coding of visual information.^[14] This work provided one of the first explicit neural implementations of predictive coding principles in sensory processing. In 1992, David Mumford further developed the idea for higher cortical areas, proposing a computational architecture for the neocortex where hierarchical layers generate predictions about lower-level features, with error signals propagating upward to refine internal models. This framework drew on Bayesian inference to explain how the visual cortex could represent complex scenes through predictive feedback connections.^[15] The formalization of predictive coding as a neuroscience theory occurred in the 1990s, particularly with Rajesh P. N. Rao and Dana H. Ballard's 1999 paper, which proposed that visual cortex neurons implement hierarchical predictions where higher-level areas send top-down signals to anticipate lower-level sensory features, thereby minimizing prediction errors and explaining extra-classical receptive field effects observed in experiments.^[1] Their model demonstrated how such mechanisms could account for neural responses in areas like V1 and V2, marking a pivotal shift toward viewing the brain as a predictive system. From the 2000s onward, Karl Friston advanced predictive coding by integrating it with the free-energy principle in his 2005 paper, arguing that the brain minimizes variational free energy as a proxy for surprise, unifying perception, action, and learning under a framework where prediction errors drive adaptive inference across hierarchical cortical structures. Friston's contributions elevated predictive coding from a perceptual model to a comprehensive theory of brain function, influencing fields like neuroimaging and computational psychiatry. The 2010s saw a surge in predictive coding's adoption within neuroscience, particularly through its alignment with the Bayesian brain hypothesis, which frames cognition as probabilistic inference where priors and likelihoods update via error signals to optimize predictions.^[16] This integration was highlighted by events such as the 2013 conference "The World Inside the Brain: Internal Predictive Models in Humans and Robots," which fostered interdisciplinary discussions on how predictive mechanisms underpin neural computation from sensory processing to decision-making.^[17]

Core Principles

Prediction and Error Signals

In predictive coding, the brain maintains internal generative models that produce top-down predictions about expected sensory inputs based on prior knowledge and context. These predictions are compared against actual bottom-up sensory data, generating prediction errors that quantify the mismatch between what was anticipated and what is observed. Prediction errors serve as the primary signals for updating and refining the internal models, enabling adaptive learning and perception without transmitting redundant information upward through the sensory hierarchy.^[18] Prediction errors play a central role in perceptual inference by driving adjustments to the generative models, thereby minimizing discrepancies over time and facilitating accurate representations of the environment. When sensory input aligns closely with predictions, errors are suppressed, allowing the brain to focus resources on novel or unexpected features; conversely, large errors trigger revisions in higher-level expectations to better anticipate future inputs. This error-driven process underpins efficient perception, as it prioritizes deviations that carry informational value for survival and action. The core mechanism involves a bidirectional flow of information: forward (bottom-up) propagation of prediction errors from lower sensory levels to higher cortical areas signals surprises that require model updates, while backward (top-down) transmission of predictions from higher to lower levels anticipates and preempts sensory data. This can be conceptualized as follows:

Sensory Input: Raw data enters at the lowest level.
Prediction Comparison: Top-down predictions meet incoming signals, computing errors (e.g., e = x - \hat{x}, where x is observed input and \hat{x} is predicted).
Error Propagation: Unsuppressed errors ascend to update higher models.
Prediction Update: Revised models descend new predictions to refine lower-level processing.

Such a loop ensures that only prediction errors, rather than all sensory details, are relayed upward, optimizing neural bandwidth.^[18] Unlike classical feedforward processing, which relies on unidirectional transmission of sensory information from periphery to cortex for sequential analysis, predictive coding emphasizes this bidirectional interplay to achieve greater efficiency and contextual integration. Feedforward models treat perception as passive accumulation of bottom-up signals, often leading to redundant computations, whereas predictive coding actively anticipates inputs, reducing the need for exhaustive signaling and enhancing robustness to noise or ambiguity.

Hierarchical Inference

In predictive coding, hierarchical inference operates through a multi-layer architecture in the brain, where lower levels process and predict fine-grained sensory details, while higher levels infer more abstract causes of those sensations. This structure allows the brain to build increasingly complex representations of the world by integrating information across scales, with each level contributing to an overall model of sensory inputs. For instance, primary sensory areas like the early visual cortex handle basic features, whereas higher cortical regions interpret contextual or categorical information.^[18]^[19] Error propagation in this hierarchy involves residual prediction errors being passed upward from lower to higher levels via feedforward connections, signaling unexplained aspects of the input that require refinement of higher-level representations. In response, higher levels generate and send downward predictions through feedback connections, which suppress or modulate activity at lower levels to better align with expected sensory patterns. This bidirectional message passing enables efficient inference by minimizing discrepancies across the hierarchy without redundant transmission of all sensory data.^[19]^[18] At each level, the brain constructs hierarchical generative models that approximate the probabilistic structure of the environment, allowing predictions to be generated from abstract causes downward to sensory specifics. These models learn statistical regularities, such as the dependencies between features, to form a coherent "top-down" explanation of bottom-up data. In visual processing, for example, lower levels might predict oriented edges in a scene, while higher levels predict entire objects like a face, with errors from edge mismatches propagating up to adjust object representations and refined predictions flowing back to sharpen edge detection.^[19]^[18]

Mathematical Framework

Bayesian Foundations

The Bayesian brain hypothesis posits that the brain functions as a probabilistic inference machine, maintaining internal representations of the world in the form of probability distributions and continuously updating these representations by integrating prior beliefs with incoming sensory evidence according to Bayesian principles.^[20] Under this framework, sensory inputs serve as likelihoods that inform the revision of priors—pre-existing expectations about environmental causes—yielding posterior distributions that best explain observed data.^[3] This hypothesis suggests that neural processes approximate optimal Bayesian inference to handle uncertainty in perception and decision-making, with empirical support from psychophysical studies demonstrating human behavior aligns with Bayesian predictions in tasks involving sensory integration.^[20] In predictive coding, this Bayesian approach is operationalized through a generative model, where the brain posits hidden causes underlying sensory inputs and generates top-down predictions of expected sensations based on prior distributions over those causes. Prediction errors arise when actual sensory data deviate from these predictions, signaling the need to update the model's parameters via approximate Bayesian updates; this process inverts the generative model to infer the most likely hidden states, effectively minimizing surprise or prediction mismatch.^[19] The generative model's hierarchical structure allows priors at higher levels to constrain lower-level inferences, enabling efficient approximation of intractable Bayesian computations in real-time neural processing.^[3] Predictive coding achieves Bayesian inference through variational methods, which bound and minimize the free energy—a proxy for surprise or the divergence between predicted and actual sensory states—to approximate intractable posterior distributions over hidden causes. This variational free energy minimization provides a tractable scheme for the brain to optimize its internal model, ensuring predictions align with sensory evidence while regularizing against overfitting through prior constraints.^[3] By iteratively refining approximations, the brain converges on Bayes-optimal representations without exhaustive computation.^[19] A central feature of this framework is empirical Bayes, wherein hyperparameters governing the priors are not fixed but learned directly from sensory data across hierarchical levels, inducing data-driven empirical priors that adapt the generative model to environmental statistics. This approach leverages the hierarchical nature of neural architectures to estimate higher-level parameters from aggregated lower-level evidence, enhancing the model's flexibility and accuracy in inferring causes.^[19]

Prediction Error Minimization Equations

In predictive coding, the fundamental prediction error at a given level is defined as the difference between the observed sensory input x and the top-down prediction \mu generated from higher-level representations. This error, denoted \varepsilon = x - \mu, quantifies the mismatch that drives perceptual inference by signaling discrepancies between expectations and reality. The core objective of predictive coding is to minimize this prediction error, typically formulated as an optimization problem to reduce the sum of squared errors across observations, \sum \varepsilon^2. In a Bayesian framework, this minimization approximates the reduction of variational free energy F, which bounds the divergence between an approximate posterior q(\mu) and the true posterior p(\mu|x), expressed as F = \mathrm{[KL](/page/KL)}[q(\mu) \| p(\mu|x)] \approx \sum \varepsilon^2 / \sigma^2, where \sigma^2 represents sensory noise variance.^[21] To achieve minimization, predictions are updated iteratively via gradient descent on the free energy. The update rule takes the form \mu^{t+1} = \mu^t - \partial F / \partial \mu, where the change in the predictive mean \mu at time step t is proportional to the gradient of F with respect to \mu, effectively adjusting higher-level representations to better explain sensory data.^[21] In hierarchical predictive coding, errors propagate across multiple levels, with the prediction error at level l given by \varepsilon_l = x_l - g(\mu_{l+1}), where g is the generative function mapping predictions from the higher level l+1 to the representation at level l. This structure enables successive refinement, as errors at lower levels inform updates at higher levels, fostering a unified inference process throughout the hierarchy.^[21]

Neural Implementations

Cortical Hierarchies and Feedback

In the neocortex, predictive coding is anatomically supported by a hierarchical organization of cortical areas interconnected through reciprocal feedforward and feedback pathways, enabling the flow of prediction errors upward and predictions downward. Feedforward connections, conveying sensory-driven prediction errors, primarily originate from the superficial layers (layers 2 and 3) of lower cortical areas and target layer 4 of higher areas, where initial error computations occur upon integration with incoming thalamic inputs. Conversely, feedback connections, carrying top-down predictions, arise from the deep layers (layers 5 and 6) of higher areas and project to the superficial layers of lower areas, modulating sensory processing by subtracting expected signals from incoming data. This layer-specific segregation aligns with the core mechanics of predictive coding, where superficial layers primarily process and transmit prediction errors upward via feedforward connections, and deep layers generate and transmit top-down predictions downward via feedback connections, facilitating hierarchical inference across the cortical column.^[22] Feedback loops in predictive coding are exemplified by top-down projections from primary visual cortex (V1) to the lateral geniculate nucleus (LGN) in the thalamus, where layer 6 pyramidal neurons in V1 convey predictions to modulate thalamic relay cells before sensory signals reach the cortex. These projections suppress LGN activity for expected stimuli, effectively implementing error minimization at early sensory stages by gating redundant information. Anatomical evidence underscores the dominance of such feedback: in the cat visual system, corticogeniculate synapses from V1 onto LGN relay neurons significantly outnumber retinogeniculate synapses, highlighting the substantial infrastructure for predictive modulation despite weaker individual synaptic strengths compared to direct retinal inputs.^[22]^[23] The canonical microcircuit model provides a unified framework for these anatomical features, positing a standardized columnar organization across sensory cortices where reciprocal connections support bidirectional signaling for prediction and error exchange. In this model, layer 4 acts as the primary site for bottom-up error signals derived from sensory discrepancies, which are then routed to superficial-layer output neurons for upward transmission of prediction errors via feedforward connections, while deep-layer neurons generate and disseminate top-down predictions via long-range feedback axons. This architecture, observed consistently in visual, auditory, and somatosensory cortices, ensures efficient hierarchical processing, with empirical support from laminar recordings showing distinct oscillatory patterns—gamma for error-driven feedforward and alpha/beta for predictive feedback—that align with the model's predictions.^[22]

Precision Weighting Mechanisms

In predictive coding, precision weighting refers to the process by which the brain assigns importance to prediction errors based on their estimated reliability, effectively modulating the influence of sensory inputs and top-down predictions during inference.^[19] Precision is formally defined as the inverse of the variance in the noise associated with a signal, denoted as \pi = 1/\sigma^2, where \sigma^2 represents the variance of the noise; this metric quantifies the confidence or certainty in a given prediction or observation.^[19] By weighting errors according to their precision, the system prioritizes more reliable signals in updating internal models, thereby optimizing the balance between bottom-up sensory data and hierarchical priors.^[24] A key distinction in precision weighting arises between sensory precision and the precision of priors. High sensory precision indicates low noise in incoming data, leading the brain to trust bottom-up prediction errors more heavily and adjust generative models accordingly; conversely, low sensory precision, such as in noisy environments, increases reliance on precise priors from higher cortical levels to suppress or reinterpret ambiguous inputs.^[19] This dynamic allows predictive coding to adapt to varying levels of uncertainty, ensuring robust perceptual inference even when sensory evidence is unreliable.^[25] Neuromodulatory systems play a crucial role in tuning these precision weights. Acetylcholine, for instance, enhances sensory precision by increasing the gain on prediction error signals in sensory cortices, thereby amplifying the impact of reliable bottom-up inputs during tasks requiring focused attention.^[26] Dopamine, on the other hand, modulates the precision of unsigned prediction errors in cortical regions, facilitating learning and adaptation by selectively weighting errors that signal novelty or salience.^[27] These mechanisms are integrated into the core minimization process of predictive coding through weighted prediction errors, expressed as \epsilon' = \epsilon / \sigma, where \epsilon is the raw error; the objective then becomes minimizing the sum of weighted squared errors, \sum \pi \epsilon^2, which balances the contributions of precise signals in variational free-energy minimization.^[19] This formulation ensures that inference remains statistically efficient, prioritizing errors from sources with high precision while downweighting those from noisy or uncertain origins.^[28]

Applications in Perception and Cognition

Sensory Processing

In predictive coding frameworks, sensory processing involves the brain generating top-down predictions about incoming exteroceptive signals from external environments, such as visual and auditory stimuli, and updating these predictions based on bottom-up error signals to minimize discrepancies. This process enables efficient perception by suppressing predictable sensory inputs while amplifying unexpected ones, thereby resolving perceptual ambiguities in real-time. For instance, in vision, the brain anticipates object locations and features based on prior experiences, allowing it to infer the world without processing every detail exhaustively.^[29] Visual phenomena like the rubber hand illusion and motion aftereffects illustrate how prediction error resolution shapes exteroceptive perception. In the rubber hand illusion, synchronous visual and tactile stimulation of a fake hand induces ownership feelings by generating prediction errors between expected and observed multisensory inputs; the brain resolves these errors by updating its internal model to incorporate the artificial limb as part of the body.^[30] Similarly, motion aftereffects occur when prolonged exposure to motion in one direction creates a strong prediction for continued movement; upon cessation, the opposing static input produces a large error signal, perceived as illusory motion in the opposite direction until predictions adapt.^[31] These examples demonstrate predictive coding's role in integrating sensory cues hierarchically to maintain perceptual stability. In auditory processing, predictive coding facilitates speech perception through anticipatory filling-in of phonemes, where the brain uses contextual priors to predict ambiguous or noisy sounds. For example, when a phoneme is obscured by noise, top-down predictions from higher-level language knowledge generate expected acoustic patterns, reducing error signals and enabling seamless comprehension without full bottom-up reconstruction.00134-0) This mechanism enhances robustness in noisy environments, as seen in studies where expected speech tokens elicit reduced neural responses compared to unexpected ones.^[32] Empirical support for these processes comes from fMRI studies revealing prediction error signals in early sensory areas. Summerfield et al. (2008) demonstrated that visual cortex activity in humans reflects prediction errors during perceptual inference, with reduced responses to expected stimuli and heightened activity for mismatches, consistent with predictive coding's attenuation of fulfilled predictions.^[33] Such findings localize error signaling to primary and secondary sensory regions, underscoring the framework's neural plausibility. Predictive coding enhances sensory efficiency by reducing bandwidth demands through prediction of expected inputs, which explains the prevalence of sparse coding in sensory neurons. By transmitting only error signals rather than raw data, the system minimizes redundancy, allowing sparse neural representations where only a small fraction of neurons fire to convey rich information about the environment.^[34] This aligns with observations in visual and auditory cortices, where predictable stimuli evoke sparser activity, optimizing information transfer under neural constraints.^[35]

Interoception and Embodiment

In predictive coding frameworks, the brain generates top-down predictions about interoceptive signals—arising from internal bodily states such as visceral sensations, including heartbeat timing and intensity—to anticipate and maintain physiological homeostasis. These predictions minimize surprise by comparing expected interoceptive inputs against actual afferent signals, thereby enabling efficient regulation of bodily functions like cardiovascular and gastrointestinal activity. For instance, heartbeat-evoked potentials demonstrate how neural responses to cardiac signals are attenuated when aligned with predictions, reflecting active inference in interoceptive processing.^[36]^[37] A key extension of this process involves allostasis, where predictive coding supports proactive regulation of energy needs and internal milieu before disruptions occur, rather than merely reacting to homeostatic imbalances. As outlined by Seth, this predictive approach to allostasis integrates interoceptive inferences to forecast and preempt bodily demands, such as metabolic adjustments, fostering adaptive self-regulation.^[38] Interoceptive prediction errors further contribute to emotional inference, where discrepancies between predicted and actual bodily states give rise to feelings of anxiety or surprise as signals of potential dysregulation. These errors inform the brain's generative models of the embodied self, shaping subjective emotional experiences through hierarchical updating.^[38] Empirical evidence highlights the insula cortex as a central hub for processing interoceptive prediction errors, integrating ascending signals from the viscera with descending predictions to support error-based learning of bodily states. Functional imaging studies show heightened insula activity during mismatches in interoceptive predictions, underscoring its role in embodiment and self-awareness.^[38]^[39]

Applications in Action and Decision-Making

Active Inference

Active inference extends the predictive coding framework from passive perception to active engagement with the environment, positing that agents select actions to minimize future prediction errors by sampling sensory data that aligns with their internal models. Under this formulation, perception updates beliefs to reduce surprise through error minimization, while action actively reshapes the sensory landscape to confirm or fulfill those beliefs, effectively treating behavior as an "imperative" form of inference. This approach unifies perception and action under the free energy principle, where agents avoid surprises—defined as discrepancies between expected and observed states—by either updating their generative models or intervening in the world.^[3] Central to active inference is the minimization of expected free energy, a quantity that bounds the surprise anticipated under a given policy of actions. The expected free energy G for a policy \pi decomposes into an epistemic component, which resolves uncertainty by gathering information, and pragmatic components, which minimize risk by achieving preferred outcomes. Formally,

G(\pi) = \mathbb{E}_{Q(o|\pi)} \left[ D_{\text{KL}} [ Q(\mu | \pi, o) || Q(\mu | \pi) ] \right] + \text{pragmatic terms (e.g., expected [utility](/page/Utility) or [risk](/page/Risk))},

where the KL divergence term captures the expected information gain from updating beliefs about hidden states \mu given future observations o, relative to the prior belief under the policy, and pragmatic terms encode costs or divergences from prior preferences. Policies are selected by choosing the \pi that minimizes G(\pi), balancing exploration to reduce epistemic uncertainty with exploitation to fulfill generative priors on sensory states. This process ensures agents act to make their predictions self-fulfilling, such as by moving toward expected rewarding locations.^[40] A representative example is saccadic eye movements in visual processing, where the agent generates predictions about retinal input based on spatial priors. To minimize expected free energy, the eyes execute rapid saccades toward regions of high predictive uncertainty or salience, effectively testing hypotheses about the visual scene and resolving ambiguities in the generative model. This active sampling reduces surprise by aligning incoming sensory data with anticipated patterns, demonstrating how active inference drives exploratory behavior to refine perceptual inferences. Friston introduced this imperative extension of predictive coding in 2010, framing active inference as the behavioral counterpart to perceptual error minimization within the free energy principle.^[3]^[41]

Motor Control

In motor control, predictive coding manifests through forward models that anticipate the sensory outcomes of actions, enabling the brain to generate movements and correct them based on prediction errors. These forward models rely on efference copies, which are internal replicas of motor commands sent to sensory areas to predict the consequences of self-initiated actions, thereby distinguishing self-generated sensory inputs from external stimuli.^[42] This mechanism allows for efficient processing by suppressing expected sensations, reducing the computational load during voluntary movements. The cerebellum plays a central role in implementing predictive coding for motor adaptation via error-based learning, where it uses climbing fiber signals to convey prediction errors and refine internal models. For instance, in prism adaptation experiments, where visual feedback is shifted by prism goggles, the cerebellum drives rapid recalibration of reaching movements by minimizing discrepancies between predicted and actual hand positions, as evidenced by impaired adaptation in patients with cerebellar lesions. This process supports fine-tuning of motor outputs through iterative updates to forward models, enhancing accuracy in tasks requiring precise coordination.^[43] Motor predictions in predictive coding operate hierarchically, with lower levels handling kinematic details like joint angles and muscle activations, while higher levels integrate goal-directed intentions and contextual plans. In the motor cortex, this hierarchy is reflected in agranular architecture that prioritizes descending predictions over ascending error signals, allowing top-down intentions to guide action without constant low-level corrections. Empirical evidence for these mechanisms includes the suppression of self-produced tactile sensations, such as reduced tickle responses during self-touch compared to external touch, mediated by corollary discharges that align predictions with actual feedback.

Applications in Psychiatry and Disorders

Psychosis and Hallucinations

In predictive coding frameworks, disruptions in the balance between top-down predictions and bottom-up sensory evidence are implicated in the generation of psychotic symptoms, particularly through alterations in the precision assigned to prior beliefs versus prediction errors. High precision on internal priors in schizophrenia can lead to persistent false predictions that are not adequately updated by sensory input, resulting in hallucinations experienced as veridical perceptions. This mechanism posits that overly rigid expectations override ambiguous or noisy sensory data, fostering experiences detached from external reality.^[44] A key aspect of this account involves elevated precision weighting of prior beliefs, where the brain fails to attenuate strong internal models in favor of new evidence, thereby sustaining hallucinatory content. In individuals with schizophrenia, this high prior precision manifests as un-updated predictions that dominate perception, explaining the persistence of hallucinations even in the absence of confirmatory stimuli. Empirical evidence from behavioral and neuroimaging studies supports this, showing increased susceptibility to suggestion-induced hallucinations under conditions of sensory uncertainty.^[44] The dopamine hypothesis of schizophrenia integrates with predictive coding by suggesting that excess dopaminergic activity enhances the salience or precision of prediction errors, promoting aberrant assignment of significance to neutral stimuli and contributing to delusional ideation. This aberrant salience arises when dopamine modulates the gain on unexpected signals, leading to false inferences about environmental relevance and reinforcing psychotic beliefs. Such dysregulation links neurochemical imbalances to the phenomenological experience of heightened motivational pull toward irrelevant cues. Bayesian models of delusions in psychosis reveal weakened sensory updating, where patients exhibit reduced flexibility in revising beliefs based on new evidence, favoring priors instead. These computational approaches highlight how imprecise error signaling perpetuates maladaptive inferences across psychotic states. Regarding positive symptoms, predictive coding provides a specific account of auditory verbal hallucinations (AVHs), the most common hallucinatory experience in schizophrenia, affecting up to 70% of patients. In this view, AVHs emerge from deficient predictive suppression of self-generated speech signals, causing internally produced thoughts to be misattributed as external voices due to unmet predictions. Functional MRI studies demonstrate reduced deactivation in auditory cortex during self-speech in hallucinating patients, reflecting impaired forward modeling and heightened precision on erroneous external attributions. This failure in hierarchical inference loops treats endogenous activity as exogenous input, vividly simulating heard speech.

Neurodevelopmental Conditions

Predictive coding impairments in neurodevelopmental conditions, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), are characterized by atypical processing of prediction errors and priors from early development, leading to altered sensory integration and attention. In ASD, individuals often exhibit reduced precision assigned to top-down priors, resulting in a greater reliance on bottom-up sensory details and a detail-focused perceptual style.^[45] This mechanism is thought to stem from inflexible adjustment of prediction error precision, where unexpected sensory inputs fail to update internal models effectively, contributing to sensory sensitivities and challenges in generalizing experiences.^[46] For instance, EEG studies in children with ASD show diminished P300 responses to unexpected auditory deviants and enhanced dorsolateral prefrontal cortex activation to expected stimuli, indicating disrupted hierarchical error signaling.^[46] In ADHD, predictive coding disruptions manifest as an over-reliance on novel sensory details, with reduced neural responses to expected events and heightened activation to surprises, which may underlie attention deficits and impulsivity.^[46] This pattern suggests difficulties in modulating precision for anticipated inputs, leading to inefficient filtering of irrelevant stimuli and persistent exploration of the environment. A 2024 neurodevelopmental perspective frames ADHD as involving divergences in predictive model formation and error minimization, particularly in sensory attenuation during action.^[47] Eye-tracking evidence further supports these impairments; in ASD, individuals show fewer anticipatory gaze shifts to predicted locations in social and nonsocial routines, reflecting weakened predictive use of cues like eye direction or object trajectories.^[48] One study found that autistic participants were less likely to direct gaze toward expected outcomes following learned visual associations, with prediction errors eliciting atypical scanning patterns. A 2021 systematic review of empirical evidence on prediction in ASD highlights domain-general differences, including reduced habituation to repeated stimuli and altered frontostriatal responses to errors, which may impair adaptive learning from infancy.^[49] A 2025 study on predictive coding and attention in developmental disorders proposes that early predictive impairments contribute to broader cognitive atypicalities in ASD and ADHD, with interventions targeting precision weighting showing promise for enhancing social cue processing.^[50] These findings underscore lifelong developmental trajectories influenced by predictive coding, distinct from acquired disruptions in other psychiatric contexts.

Applications in Artificial Intelligence

Predictive Models in Machine Learning

Predictive coding has inspired the development of neural network architectures in machine learning that emphasize hierarchical prediction and error minimization for unsupervised learning tasks. One foundational example is the predictive coding network proposed by Rao and Ballard in 1999, which models visual processing as a generative process where higher-level neurons predict the activity of lower-level ones, updating representations based on prediction errors to learn features like oriented edges in natural images.^[1] This approach enables efficient feature extraction by focusing computations on discrepancies between predictions and sensory inputs, rather than exhaustive bottom-up processing. In machine learning applications, predictive coding principles underpin variants of autoencoders, where prediction errors serve as signals for tasks like anomaly detection and denoising. For instance, in denoising autoencoders, the network learns to reconstruct clean inputs from noisy versions by minimizing reconstruction errors, analogous to resolving prediction mismatches in predictive coding; this has been shown to improve robustness in image restoration tasks.^[51] Similarly, for anomaly detection, high prediction errors from autoencoder reconstructions flag outliers, as deviations from learned generative models indicate unusual data points, with applications in fraud detection and fault monitoring.^[52] These methods draw from predictive coding's error-driven learning, promoting sparse and efficient representations without labeled data. Predictive coding also connects to deep learning through variants of Boltzmann machines, which use energy-based formulations for probabilistic representation learning. The Helmholtz machine, an early hierarchical model, employs top-down generative passes akin to predictions and bottom-up inference to approximate posteriors, using restricted Boltzmann machine layers to learn disentangled features in unsupervised settings. Extensions like multi-prediction deep Boltzmann machines further integrate multiple predictive objectives to enhance generative capabilities and representation quality.^[53] Variational autoencoders, which optimize evidence lower bounds via prediction-like inference, similarly embody these ideas, linking predictive coding to modern deep generative models.^[54] A key advantage of these predictive coding-inspired energy-based models is their ability to reduce computational demands by prioritizing error signals over full forward passes, enabling scalable learning in high-dimensional spaces. For example, by suppressing predictable activity through top-down inhibition, networks minimize energy expenditure while maintaining accurate inferences, as demonstrated in recurrent architectures where predictive mechanisms emerge from efficiency constraints.^[55] This not only lowers training costs but also aligns with biological plausibility, fostering advancements in resource-efficient AI.

Recent Advances in Neural Networks

Recent developments in predictive coding have significantly advanced bio-inspired neural network architectures, particularly through spiking and hierarchical models that enhance efficiency and biological plausibility in artificial intelligence systems. A prominent example is Predictive Coding Light (PCL), introduced in 2025, which proposes a recurrent hierarchical spiking neural network designed for unsupervised representation learning. PCL employs excitatory feedforward connections alongside inhibitory recurrent and top-down pathways to suppress predictable spikes, thereby minimizing energy consumption in neuromorphic hardware. Trained using spike timing-dependent plasticity (STDP) on event-based vision data, such as from dynamic vision sensors, PCL develops receptive fields resembling simple and complex cells in the visual cortex, including orientation tuning and cross-orientation suppression. On the DVS128 Gesture dataset, PCL achieves 89.12% classification accuracy while substantially reducing spiking activity compared to baseline models without inhibition, demonstrating its potential for energy-efficient processing in edge AI applications.^[56] Building on this, a 2025 study explored predictive coding-inspired deep neural networks (DNNs) to replicate brain-like responses, positioning them as biologically plausible models of cortical processing. By incorporating predictive coding dynamics into recurrent DNN architectures, the model generates activity patterns that mimic neural responses observed in biological systems, such as error signaling and hierarchical prediction updates. This approach was evaluated on tasks involving sensory prediction, where the networks exhibited emergent properties like sparse representations and adaptation to novel inputs, aligning closely with electrophysiological data from primate visual areas. The findings suggest that predictive coding principles can bridge the gap between artificial DNNs and neural realism, offering a framework for more interpretable AI systems that emulate brain computation.^[57] In the domain of anomaly detection, a 2025 model in Neural Computation leverages predictive coding to enable multi-level novelty detection within hierarchical networks. The recurrent predictive coding network (rPCN) and its hierarchical extension (hPCN) use local synaptic plasticity to minimize prediction errors, with error neurons naturally signaling novelty across abstraction levels—from low-level sensory features to high-level semantic concepts. Tested on datasets like MNIST and Tiny ImageNet, the hPCN detects pixel-level anomalies (e.g., separability score of approximately 2) and object-level deviations (score near 0 at top layers), while the rPCN matches human recognition memory capacity with 83% accuracy on 10,000 images. This unified framework integrates novelty detection with associative memory and representation learning, outperforming traditional autoencoders in robustness to correlated inputs and providing a biologically grounded method for scalable anomaly identification in AI.^[58] Finally, recent hybrid models have integrated predictive coding with transformer architectures to improve efficient prediction in large-scale AI, emphasizing bio-plausibility and computational scalability. A 2025 survey highlights generalizations of predictive coding to non-Gaussian distributions, enabling its application in transformer-based systems for tasks like sequence modeling and causal inference. For instance, predictive-coding-based transformers, as explored in Pinchetti et al. (2024), approximate standard transformer performance with comparable model complexity, achieving near-equivalent accuracy on benchmarks such as language modeling while incorporating local Hebbian-like updates for energy efficiency. These hybrids facilitate hierarchical prediction in massive datasets, reducing reliance on backpropagation and promoting neuromorphic compatibility for real-world deployment.^[59]^[60]

Challenges and Empirical Status

Supporting Evidence and Experiments

Neuroimaging studies have provided substantial evidence for prediction error signals in predictive coding through electroencephalography (EEG) and magnetoencephalography (MEG). The mismatch negativity (MMN), an early auditory evoked potential peaking around 150-200 ms post-stimulus, is interpreted as a neural marker of prediction errors when deviant stimuli violate expected sensory patterns.^[61] In hierarchical predictive coding models, MMN reflects bottom-up error signals propagating from primary auditory cortex to higher-order regions, with sources localized to the superior temporal gyrus and frontal areas via dipole modeling.^[61] A 2020 review synthesized EEG and MEG data showing that omission of expected stimuli elicits MMN-like responses, supporting generative predictions over mere adaptation effects.^[61] More recent laminar recordings in non-human primates confirm that gamma-band activity carries prediction errors upward through cortical layers, while beta-band signals convey top-down predictions.^[62] Behavioral paradigms, such as adaptation and priming experiments, demonstrate how hierarchical predictions shape perception and action. In visual adaptation tasks, repeated exposure to stimuli leads to repetition suppression in fMRI signals, interpreted as fulfilled predictions reducing neural activity, with stronger suppression for expected versus unexpected repetitions.^[63] Priming experiments reveal hierarchical inference: local priming effects (e.g., faster responses to repeated low-level features) interact with global context predictions, as shown in oddball paradigms where global rule violations elicit late P300 components only when attention is engaged.^[64] These findings support multi-level predictive coding, where lower-level predictions adapt sensory tuning, and higher-level ones modulate precision weighting for behavioral relevance.^[63] For instance, in auditory local-global paradigms, EEG markers distinguish local deviants (early MMN) from global ones (late positivity), evidencing layered error processing.^[64] Recent studies up to 2025 highlight developmental aspects of predictive coding in attention. A 2025 review in Developmental Cognitive Neuroscience examines EEG evidence from neonates to children, showing that preterm infants (31-32 weeks gestational age) exhibit repetition suppression and differential omission responses to predictable versus jittered stimuli, indicating early precision-weighted predictions.^[50] In 6-month-olds, fNIRS and EEG reveal top-down predictions during visual omissions cued by auditory learning, with stronger cortical responses correlating to later attention and language outcomes at 12-18 months.^[50] These paradigms, including unimodal oddballs, underscore how attentional modulation enhances prediction error signals from infancy, fostering cognitive development.^[50] Cross-species evidence from rodents and primates reinforces predictive coding in sensory tasks. Primate studies using laminar local field potentials in visual cortex demonstrate hierarchical error signaling: ascending gamma oscillations encode mismatches between predicted and actual inputs, while descending beta rhythms refine predictions across areas V1 to V4.^[62] Computational models trained on natural scenes replicate these dynamics, with V1 neurons showing orientation-selective error suppression matching empirical data.^[62] Such findings across species validate core predictive coding mechanisms in sensory inference.^[62]

Criticisms and Limitations

Critics of predictive coding theory argue that it overemphasizes free energy minimization as a core mechanism of brain function, potentially portraying the brain as an overly unified optimizer when diverse biological processes may be at play. In particular, the free-energy principle underlying predictive coding has been challenged for lacking conclusive evidence that the brain consistently optimizes free energy through variational Bayesian inference, with empirical support remaining inconclusive and the principle possibly functioning more as a formal modeling tool than a fundamental imperative. This overemphasis risks obscuring the mechanistic details of neural operations and the historical contingencies shaping biological systems, advocating instead for explanatory pluralism that incorporates multiple theoretical perspectives.^[65]^[65]^[65] Empirical investigations into key components of predictive coding, such as precision weighting of prediction errors, reveal mixed support in human studies, highlighting significant gaps in validation. For instance, while precision weighting can account for certain "contra-vanilla" patterns where expected stimuli elicit larger neural responses, such as in RSVP tasks or attentional cueing paradigms, it fails to consistently predict reductions in neural latency under high-precision conditions, as observed in EEG and fMRI data. These inconsistencies arise partly from the overlap in definitions of precision (encompassing attention, confidence, and expectation), limiting the ability to disentangle effects, and from sparse evidence for associated frequency-domain changes or neuromodulatory links in human cortex. Overall, the theory's reliance on precision mechanisms lacks robust, direct intracranial evidence in humans, underscoring the need for more targeted neuroimaging and behavioral experiments.^[66]^[66]^[66]^[66] Predictive coding is often distinguished from the broader framework of predictive processing, with the former referring to a specific hierarchical neural implementation involving top-down predictions and bottom-up error signals, while the latter encompasses a wider range of Bayesian inference strategies without committing to precise neural architectures. This distinction highlights a limitation of predictive coding: its mechanistic specificity may not fully capture the flexibility of predictive processing, potentially restricting its explanatory scope to perceptual and low-level cognitive tasks.^[67]^[66]^[66] Addressing these criticisms requires future research to emphasize causal interventions, such as optogenetic manipulations in animal models to test prediction error pathways, alongside advanced computational simulations that integrate predictive coding with biophysical constraints for more realistic benchmarking against empirical data. Such approaches would help resolve empirical ambiguities and clarify the theory's boundaries relative to alternatives.^[66]

References

[1]
Predictive coding in the visual cortex: a functional interpretation of ...
Rao, R. P. N. & Ballard, D. H. Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Comput. 9, 721–763 ( 1997).
[2]
A theory of cortical responses - Journals
This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain ...
[3]
The free-energy principle: a unified brain theory? - Nature
Jan 13, 2010 · In summary, the theme underlying the Bayesian brain and predictive coding is that the brain is an inference engine that is trying to optimize ...
[4]
[PDF] predictive coding: a theoretical and experimental review - arXiv
Jul 12, 2022 · Predictive coding offers a potentially unifying account of cortical function – postulating that the core function of the brain is to ...
[5]
Predictive Coding and the Neural Response to Predictable Stimuli
Predictive coding posits that the brain actively predicts upcoming sensory input rather than passively registering it. Predictive coding is efficient in the ...Missing: key | Show results with:key
[6]
Predictive coding in neuropsychiatric disorders: A systematic ...
The predictive coding framework postulates that the human brain continuously generates predictions about the environment, maximizing successes and ...Missing: key | Show results with:key
[7]
The empirical status of predictive coding and active inference
Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported.Missing: key | Show results with:key
[8]
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Some predict that Norbert Wiener will be remembered for his Extrapolation long after Cybernetics is forgotten.Missing: 1940s | Show results with:1940s
[9]
[PDF] The History of Linear Prediction
In the 1940s, Norbert Wiener developed a mathematical theory for calculating the best filters and pre- dictors for detecting signals hidden in noise.
[10]
[PDF] A Mathematical Theory of Communication
We have seen that under very general conditions the logarithm of the number of possible signals in a discrete channel increases linearly with time. The capacity ...
[11]
Toward a Model for Speech Recognition | Semantic Scholar
An approach to the design of a machine for the recognition and synthesis of speech is proposed, with particular emphasis on problems of acoustical analysis.
[12]
Differential pulse-code modulation - Wikipedia
History. Delta modulation was introduced earlier than and can be considered a special case of DPCM, as it also uses a similar feedback principle. DPCM was ...Missing: 1970s | Show results with:1970s
[13]
Adaptive Predictive Coding of Speech Signals - Atal - 1970
We describe in this paper a method for efficient encoding of speech signals, based on predictive coding. In this coding method, both the transmitter and the ...
[14]
Predictive Coding: How Many Faces? - PMC - NIH
Nov 4, 2015 · Since von Helmholtz's (1867) supposition that the basis of perception is anchored in unconscious inference, it has been widely accepted that ...
[15]
When Can Predictive Brains be Truly Bayesian? - Frontiers
There is a strong appeal to the Bayesian Brain Hypothesis, as well as to the hypothesis that the brain implements cognition via hierarchical predictive coding.
[16]
Conference 'The World Inside the Brain: Internal Predictive Models ...
Conference 'The World Inside the Brain: Internal Predictive Models in Humans and Robots'. Dates: Thursday 23 May (09:30) - Friday 24 May 2013 (18:00).
[17]
[PDF] Predictive coding in the visual cortex: a functional interpretation of ...
Rao, R. P. N. & Ballard, D. H. Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Comput. 9, 721–763. (1997).
[18]
Predictive coding under the free-energy principle - PubMed Central
This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain.2. Hierarchical Dynamical... · 3. Hierarchical Models In... · 4. Birdsong And Attractors
[19]
The Bayesian brain: the role of uncertainty in neural coding and ...
This leads to the 'Bayesian coding hypothesis': that the brain represents sensory information probabilistically, in the form of probability distributions.
[20]
https://www.sciencedirect.com/science/article/pii/S0166223604003352
[21]
Canonical Microcircuits for Predictive Coding - ScienceDirect.com
Nov 21, 2012 · This Perspective considers the canonical microcircuit in light of predictive coding. We focus on the intrinsic connectivity within a cortical column and the ...
[22]
Relative numbers of cortical and brainstem inputs to the ... - PubMed
We identified corticogeniculate terminals by orthograde transport of biocytin injected into the visual cortex and identified brainstem terminals by ...Missing: et al.
[23]
The many faces of precision (Replies to commentaries on “Whatever ...
An appreciation of the many roles of “precision-weighting” (upping the gain on select populations of prediction error units) opens the door to better accounts ...Missing: seminal | Show results with:seminal
[24]
Predictive Coding: How Many Faces? - Journal of Neuroscience
Nov 4, 2015 · An alternative role of this attentional mechanism may be to alter the precision-weighting of the prediction error. It has been proposed that ...
[25]
Acetylcholine modulates the precision of prediction error in ... - eLife
Jan 19, 2024 · Acetylcholine plays a multifold role in modulating neuronal mismatch in auditory cortex, affecting the precision of prediction error ...
[26]
Precision weighting of cortical unsigned prediction error signals ...
Jun 24, 2020 · Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of ...
[27]
Free Energy, Precision and Learning: The Role of Cholinergic ...
May 8, 2013 · The free energy framework places predictive coding in a more general setting, using dynamic and hierarchical generative models of hidden ...
[28]
Predictive Coding and the Neural Response to Predictable Stimuli
Jun 30, 2010 · Predictive coding posits that the brain actively predicts upcoming sensory input rather than passively registering it. Predictive coding is ...<|control11|><|separator|>
[29]
Sensory Processing and the Rubber Hand Illusion—An Evoked ...
According to predictive coding, the RHI is caused by minimizing the prediction errors that arise from visual (i.e., seeing the touch) and tactile inputs (i.e., ...
[30]
Predictive coding - Huang - 2011 - WIREs Cognitive Science
Mar 24, 2011 · Predictive coding can also be used to model motion processing in the visual cortex. Neurons in area MST, which are tuned to optic flow such ...
[31]
Predictive Top-Down Integration of Prior Knowledge during Speech ...
Jun 20, 2012 · Here we used concurrent EEG and MEG recordings to determine how sensory information and prior knowledge are integrated in the brain during speech perception.
[32]
A Neural Representation of Prior Information during Perceptual ...
Jul 31, 2008 · Predictive coding argues that whereas sensory evoked responses—reflecting prediction error—flow forward within the cortical hierarchy ...
[33]
Toward a unified theory of efficient, predictive, and sparse coding
Dec 19, 2017 · The influential “efficient coding” theory posits that sensory circuits encode maximal information about their inputs given internal constraints, ...Missing: bandwidth | Show results with:bandwidth
[34]
[PDF] Predictive coding - University of Washington
Rao RPN, Ballard DH. Predictive coding in the visual cortex: a functional interpretation of some extra- classical receptive-field effects. Nat Neurosci 1999, 2:.
[35]
Skipping a Beat: Heartbeat-Evoked Potentials Reflect Predictions ...
Sep 1, 2020 · The study found that the brain uses cardiac signals to predict auditory stimuli, and these predictions are observed in heartbeat-evoked ...
[36]
'Bodily precision': a predictive coding account of individual ...
Nov 19, 2016 · This paper applies similar principles to IAcc, proposing that individual differences in IAcc depend on 'precision' in interoceptive systems.'Perceptual inference... · A model of heartbeat... · Application of the model to...
[37]
Opinion Interoceptive inference, emotion, and the embodied self
'Interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of ...
[38]
An insula hierarchical network architecture for active interoceptive ...
Jun 29, 2022 · The unique hierarchical and modular architecture of the insula suggests specialization for processing interoceptive afferents.
[39]
Active inference and the anatomy of oculomotion - PMC
In this paper, we have demonstrated that active inference provides a sufficient and principled account of oculomotor forces that fulfil prior beliefs about eye ...
[40]
https://doi.org/10.1080/17588928.2015.1020053
[41]
Prediction signals in the cerebellum: Beyond supervised motor ...
Mar 30, 2020 · Predictive coding in climbing fibers. In a landmark study, Ohmae and Medina provided a new blueprint for how cerebellar circuits may operate to ...Cf Input To Purkinje Cells... · Cerebellar Influence On... · Conclusions And Open...
[42]
https://doi.org/10.1016/j.neuron.2011.10.018
[43]
A predictive coding perspective on autism spectrum disorders
In predictive coding schemes, higher brain areas attempt to “explain” input from lower brain areas, and then project these predictions down to lower areas, ...
[44]
Predictive coding in autism spectrum disorder and attention deficit ...
Predictive coding has been proposed as a framework to understand neural processes in neuropsychiatric disorders. We used this approach to describe mechanisms ...
[45]
(PDF) Redefining ADHD in the Light of Predictive Coding and Active ...
Jul 10, 2024 · We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory ...
[46]
Social and nonsocial visual prediction errors in autism spectrum ...
In this study, we used eye tracking methodology and found that individuals with ASD were less likely to look at the predicted location when a visual routine ...Missing: studies | Show results with:studies
[47]
(PDF) Prediction in Autism Spectrum Disorder: A Systematic Review ...
May 27, 2025 · A predictive process (bottom) generates a prediction‐modulated response upon being present with an antecedent, or a contextual setting. The ...<|control11|><|separator|>
[48]
Predictive coding and attention in developmental cognitive ...
Jan 22, 2025 · The predictive coding theory provides a framework to understand sensory processing and how it relates to cognition, but the complex hierarchical ...
[49]
Intro to Autoencoders | TensorFlow Core
Aug 16, 2024 · This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.
[50]
3. Autoencoders and anomaly detection
An autoencoder can be used to model the normal behavior of data and detect outliers using the reconstruction error as an indicator.
[51]
[PDF] Multi-Prediction Deep Boltzmann Machines - NIPS papers
We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MP-. DBM can be seen as a single probabilistic model trained to maximize a ...
[52]
Predictive Coding, Variational Autoencoders, and Biological ... - arXiv
Nov 15, 2020 · This paper reviews predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and ...
[53]
Predictive coding is a consequence of energy efficiency in recurrent ...
Dec 9, 2022 · We report that predictive coding naturally emerges as a simple consequence of energy efficiency; networks trained to be efficient not only predict and inhibit ...Missing: bandwidth | Show results with:bandwidth
[54]
Predictive Coding Light | Nature Communications
Oct 6, 2025 · Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In ...Missing: bandwidth | Show results with:bandwidth
[55]
Predictive Coding algorithms induce brain-like responses in Artificial ...
Jan 20, 2025 · This study explores whether predictive coding (PC) inspired Deep Neural Networks can serve as biologically plausible neural network models of the brain.Missing: DNNs | Show results with:DNNs
[56]
Predictive Coding Model Detects Novelty on Different Levels of ...
Jul 17, 2025 · The predictive coding model includes neurons encoding prediction errors, and we show that these neurons produce higher activity for novel ...Missing: multi- | Show results with:multi-
[57]
Review A survey on neuro-mimetic deep learning via predictive coding
This generalization allows predictive-coding-based transformers to perform almost as well as standard transformers with the same model complexity (Pinchetti et ...Review · 2. Generative Models And... · 3. Implementations Of...
[58]
[2306.15479] Predictive Coding beyond Correlations - arXiv
This paper shows predictive coding can perform causal inference, compute interventions, and infer unknown graphs from data, even in image classification.Missing: transformers | Show results with:transformers
[59]
Auditory Mismatch Negativity Under Predictive Coding Framework ...
Sep 9, 2020 · The predictive function along the auditory pathway was mainly studied by mismatch negativity (MMN)—a brain response to an unexpected disruption ...
[60]
Evidence for a hierarchy of predictions and prediction errors ... - PNAS
Thus, such an omission should induce a greater prediction error than when a standard tone is expected. ... predictive coding hypothesis. Higher-order ...
[61]
Evaluating the neurophysiological evidence for predictive ...
Mar 8, 2020 · Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP. Keywords: predictive ...<|control11|><|separator|>
[62]
Predictive Coding in the Primate Brain: From Visual to Fronto-Limbic ...
Oct 25, 2025 · In this model, sensory input arises from hidden causes that change over time, and the neural activity encodes conditional expectations of these ...
[63]
the free-energy principle, organicism, and mechanism | Synthese
Sep 10, 2018 · The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration.Missing: critique | Show results with:critique
[64]
Is predictive coding falsifiable? - ScienceDirect.com
These properties can be used to determine whether precision-weighting in predictive-coding justifiably explains a contra (vanilla) predictive pattern, ensuring ...Missing: seminal | Show results with:seminal
[65]
An Introduction to Predictive Processing Models of Perception and ...
Oct 29, 2023 · The basic idea in these models is that the brain minimizes prediction error using a hierarchically structured arrangement of simple prediction ...