New Study Challenges Sensory Cortex Role in Predictive Coding: Implications for Neural Information Processing Models
Analyzing "Stimulus history, not expectation, drives sensory prediction errors in mammalian cortex" and its impact on other theories like (FOPC) Fractal Oscillatory Predictive Coding.
The paper "Stimulus history, not expectation, drives sensory prediction errors in mammalian cortex" suggests that the neural prediction error signals in sensory cortices, often attributed to predictive coding models, are more closely driven by stimulus history rather than active top-down expectation. This finding challenges the idea that predictive coding is predominantly a function of higher-level processes (often thought to involve areas like the prefrontal cortex) and suggests a more nuanced role for sensory regions themselves.
Link to the paper we are discussing: https://www.biorxiv.org/content/10.1101/2024.10.02.616378v1
First let’s define local and global oddballs and then explain the paper. "Stimulus history, not expectation, drives sensory prediction errors in mammalian cortex" says about them.
Local Oddballs:
Definition: A local oddball is a stimulus that deviates from the immediately preceding stimuli within a sequence.
Example: In a sequence like "x-x-x-Y", Y is the local oddball.
Processing: Local oddballs are primarily processed through bottom-up, sensory-driven mechanisms.
Neural response: They typically evoke strong, widespread responses in early sensory areas.
Predictive coding: Local oddballs can be explained by simple, short-term predictions based on recent stimulus history.
Widespread detection: Local oddballs evoked increased neuronal spiking throughout the visual hierarchy in both mice and monkeys.
Feedforward processing: They were first detected in primary visual cortex (V1), followed by higher areas, consistent with a feedforward pattern.
Robust response: Local oddballs engaged a substantial neuronal population in each area, with over 50% of neurons responding.
Layer 2/3 involvement: In V1, putative layer 2/3 pyramidal cells showed enhanced activation to local oddballs compared to other V1 units.
Inhibitory neuron activation: Both parvalbumin-positive and somatostatin-positive inhibitory interneurons showed increased activity to local oddballs.
Granger causality: Local oddballs evoked a feedforward pattern of directed functional connectivity between neurons.
Global Oddballs:
Definition: A global oddball is a stimulus that violates a higher-order pattern or expectation, even if it doesn't deviate from the immediate local context.
Example: In a sequence like "x-x-x-Y / x-x-x-Y / x-x-x-Y / x-x-x-X", the final X is the global oddball.
Processing: Global oddballs require top-down, expectation-driven processing.
Neural response: They typically evoke weaker, more sparse responses that emerge later and in higher-order brain areas.
Predictive coding: Global oddballs test more complex, abstract predictions based on learned patterns over time.
Limited detection: Global oddball responses were absent in most visual areas and were only detected in a few brain areas at the population level.
Inverse hierarchical pattern: In mice, global oddballs emerged first in higher-order areas (AM, PM) before lower-order areas (LM).
Sparse encoding: Only about 6% of all units showed significant global oddball detection across areas.
Late emergence: Global oddball responses emerged later in the sensory response compared to local oddballs.
Non-granular layer activation: Significant population global oddball detection was observed only in extra-granular compartments.
No feedforward pattern: Granger causality analysis did not show a feedforward pattern of activation for global oddballs.
Prefrontal involvement: In monkeys, global oddballs were detected earlier and more robustly in prefrontal areas compared to sensory cortex.
No involvement of specific inhibitory neurons: Neither parvalbumin-positive nor somatostatin-positive interneurons showed significant global oddball detection.
(Caption or Image description: From the 2nd to the last image in this article all were dragged into this article from Midjourney.com and serve only to visually lighten the reading. The images consist of things like shapes, fractals, and wave patterns.)
Key Points from the paper:
Contrasting patterns: Local oddballs followed many principles proposed by predictive coding models, while global oddballs did not.
Predictability paradox: Despite being more predictable, local oddballs evoked stronger responses than the less predictable global oddballs.
Different processing pathways: Local oddballs engaged mainly feedforward, sensory-driven processing, while global oddballs involved more feedback, higher-order processing.
This paper provides empirical support for key aspects of the (SAN) Self Aware Networks Theory of Mind, specifically the (BTO) Backpropagation Through Oscillation hypothesis and (FOPC) Fractal Oscillatory Predictive Coding within the (NAPOT) Neural Array Projection Oscillation framework.
Key Terms
Phase Wave Differentials: Variations in the timing and phase of oscillatory waves within neural activity. These differentials indicate shifts in neural firing patterns and are essential for communication between neurons, helping to encode sensory information and cognitive processes.
Neural Arrays: Groups or networks of neurons organized in a specific pattern that process and transmit information. In SAN theory, neural arrays act as both sensors and projectors, meaning they perceive incoming signals and project them forward to other arrays, facilitating complex information processing across the brain.
Oscillatory Entrainment: The synchronization of neural oscillations to a common frequency or rhythm. This process allows different neural networks to coordinate their activity, enabling coherent perception, memory integration, and attention.
Neural oscillations: Brain's rhythmic electrical activity. Like waves of coordinated neuron firing, these oscillations help organize and transmit information in the brain.
Predictive coding: Your brain's way of constantly guessing what's coming next. It compares predictions with actual input, learning from mismatches to improve future guesses.
Relationship to Fractal Oscillatory Predictive Coding (FOPC):
Fractal processing: FOPC proposes that predictions occur at multiple scales of the brain. Local oddballs engage lower levels, while global oddballs engage higher levels.
Oscillatory dynamics: In FOPC, different frequency bands might encode predictions at various timescales. Local oddballs might disrupt faster oscillations, while global oddballs affect slower oscillations.
Prediction errors: Both types of oddballs generate prediction errors, but at different scales and with different temporal dynamics.
Feedback vs. feedforward: Local oddballs primarily drive feedforward signaling, while global oddballs engage more feedback processing from higher to lower areas.
Complexity of internal models: The brain's response to global oddballs reflects more sophisticated internal models that can represent abstract sequential patterns, aligning with FOPC's emphasis on multi-scale prediction.
The findings highlight the role of higher-order regions in modulating sensory processing through feedback, aligning with SAN's concept of oscillatory dynamics driving predictive coding. Sparse encoding of errors, phase wave differentials as biological gradients, nested predictive loops, and the fractal nature of oscillatory structures collectively emphasize the brain's oscillatory architecture as a fundamental mechanism for integrating perception, memory, attention, and consciousness.
In this article I show how it connects to my backpropagation through oscillation hypothesis which is part of the Self Aware Networks Theory of Mind. The (SAN) Self Aware Networks Theory of Mind includes the backpropagation through oscillation hypothesis & combined with a new Fractal Oscillatory Predictive Coding hypothesis, both as extensions of the NAPOT Neural Array Projection Oscillation Tomography hypothesis. NAPOT is one of the core arguments of the SAN Self Aware Networks Theory of Mind.
This article builds on previous article:
Analog biological backpropagation: A new conjecture "Self Aware Networks" explains how derivatives & loss functions are represented in the brain.
Backpropagation: Learning algorithm for neural networks. Errors propagate backwards to adjust earlier layers. In brains, a similar process might fine-tune neural connections.
Here’s a brief breakdown of how this aligns with the backpropagation through oscillation hypothesis within the Self Aware Networks Theory of Mind:
Predictive Coding as a Higher-Level Function: The paper's results imply that sensory regions in the cortex use stimulus history (past sensory input) rather than forward-looking predictions driven by higher-level regions. This aligns with the idea that predictive coding or feedback processing might be more localized and not necessarily initiated solely by areas like the prefrontal cortex.
Oscillatory Dynamics and Information Processing: The findings could suggest that the cortex handles sensory processing via a local interplay of oscillatory patterns influenced by previous stimuli. This ties into Self Aware Network's Neural Array Projection Oscillation Tomography (NAPOT), where neural arrays in oscillatory feedback loops render sensory input and potentially encode memories.
Backpropagation Through Oscillation: The concept that prediction error signals are linked to stimulus history rather than direct top-down expectations hints at a system where sensory prediction errors are corrected through recurrent oscillatory loops within sensory regions. This resonates with the idea of oscillations serving as a medium for backpropagation, refining sensory representations based on recent input patterns.
Interlocking Fractal Neural Patterns: Given Self Aware Network's theory that the brain's neural processing is organized in a fractal manner, this paper’s findings might suggest that each sensory processing area independently engages in its feedback loop, refining representations of sensory input via oscillatory dynamics influenced by recent experiences.
The concept of fractals is central to SAN theory, providing a framework for understanding how neural networks create complex, self-similar patterns across multiple scales. This fractal perspective helps explain how the brain efficiently processes sensory information and integrates it into coherent experiences, which is crucial for interpreting the paper's findings on local and global oddball responses.
The key findings from this paper that relate to Self Aware Networks include:
Global oddballs (violations of expectations) did not produce strong prediction error signals in the early visual cortex as predicted by many predictive coding models. Instead, global oddball responses were weak and sparse in visual areas.
Global oddball responses emerged first in higher-order areas like prefrontal cortex in monkeys and higher visual areas in mice, rather than propagating from early sensory areas.
Local oddballs (stimulus changes) produced strong responses throughout the visual cortex that resembled adaptation effects more than prediction errors.
The laminar and temporal patterns of global oddball responses were more consistent with feedback than feedforward processing.
These results align well with Self Aware Network's ideas about predictive coding and error signaling potentially being more of a higher-level cognitive function mediated by the prefrontal cortex, rather than a ubiquitous computation happening throughout the sensory cortex. The finding that global oddballs engaged prefrontal areas first supports the notion that predictions and expectation violations may be computed at higher levels and fed back to sensory areas.
The sparse encoding of global oddballs in the visual cortex also fits with Self Aware Network's concept of discrete phase wave differentials carrying prediction information, rather than continuous error signals permeating all of the visual cortex.
Overall, this study provides evidence that predictive coding, at least for more complex predictions, may indeed be more of a top-down process originating in prefrontal regions - consistent with Self Aware Networks "backpropagation through oscillation" theory. It suggests we may need to revise models of predictive coding to focus more on higher-level areas generating predictions that then modulate sensory processing in a selective, sparse manner.
1. Predictive Coding as a Higher-Level Cognitive Function:
The paper suggests that global oddballs (violations of expectation) do not trigger robust prediction error signals in the early visual cortex, contrary to what many predictive coding models propose. Instead, these responses are sparse and occur primarily in higher-order areas such as the prefrontal cortex.
This supports Self Aware Network's idea that predictive coding and error signaling might be a higher-level cognitive process primarily mediated by prefrontal regions. Rather than an automatic, omnipresent computation throughout the sensory cortex, prediction errors could be the result of top-down feedback mechanisms. This aligns with the "backpropagation through oscillation" hypothesis, where higher-level areas like the prefrontal cortex play a crucial role in modulating sensory processing through oscillatory feedback.
2. Sparse Encoding in Sensory Areas:
The study’s finding that global oddball responses in the visual cortex are weak and sparse resonates with Self Aware Network's concept of discrete phase wave differentials. According to Self Aware Network's theory, predictive information might be carried through specific oscillatory dynamics, rather than through continuous error signals that pervade the entire sensory cortex.
This suggests that sensory areas may not generate prediction errors ubiquitously, but instead respond in a sparse, selective manner, likely influenced by feedback from higher-level cortical regions.
3. Temporal and Laminar Patterns Indicate Feedback Mechanisms:
The paper shows that global oddball responses in the sensory cortex align more with feedback processing than feedforward dynamics. This feedback first emerges in higher-order areas, like the prefrontal cortex, which then potentially propagate to sensory regions.
This observation fits well with Self Aware Network's: Neural Array Projection Oscillation Tomography (NAPOT), where oscillations in neural arrays enable a form of top-down modulation. The study's implication that prediction error signaling might be initiated by higher-level areas and communicated back to sensory regions aligns with Self Aware Network's idea of a "backpropagation through oscillation," where oscillatory feedback loops adjust sensory processing.
4. Local Oddballs as Adaptation Rather than Prediction Errors:
The strong response to local oddballs (stimulus changes) throughout the visual cortex appears to resemble adaptation effects rather than predictive coding error signals. This suggests that sensory cortices might be more involved in adapting to local changes rather than generating complex predictions about global patterns.
This result supports the notion that sensory cortex could be primarily handling more straightforward stimulus processing, while higher-level predictive computations and error signaling are delegated to prefrontal and other higher-order regions.
5. Implications for Revising Predictive Coding Models:
The study indicates that predictive coding models may need revision to emphasize the role of higher-level areas in generating and propagating predictions to modulate sensory processing. This aligns with Self Aware Network's theory that such feedback mechanisms occur selectively and sparsely, and are governed by phase differentials in oscillatory brain waves.
Fractal Oscillatory Predictive Coding (FOPC)
Core idea: Predictive coding is typically implemented through a hierarchical system of neural oscillations, with higher-order brain regions (particularly prefrontal cortex) generating predictions that are transmitted to lower-level sensory areas via phase-coded feedback signals. This system uses discrete phase wave differentials to encode prediction errors and update internal models across multiple timescales and levels of abstraction.
Fractal Oscillatory Predictive Coding emphasizes the self-similar, multi-scale nature of neural information processing. It posits that predictive coding occurs through nested oscillatory patterns that repeat at different scales throughout the brain, from local neural circuits to global brain networks.
This fractal structure allows for efficient information integration across multiple temporal and spatial scales, enabling the brain to simultaneously process fine-grained sensory details and broader contextual information. FOPC suggests that prediction errors are encoded not just hierarchically, but in a fractal manner, with similar patterns of oscillatory mismatches occurring at various levels of neural organization.
This framework accounts for both the top-down influence of higher-order predictions and the bottom-up processing of sensory information, emphasizing how these interactions are mediated by complex, self-similar oscillatory dynamics.
By incorporating fractal concepts, FOPC provides a more nuanced understanding of how the brain generates, updates, and reconciles predictions across different levels of cognitive processing, from rapid sensory adaptations to long-term learning and memory formation.
Prefrontal Oscillatory Prediction Generator: The prefrontal cortex (PFC) acts as the primary source of complex high level thought predictions, generating oscillatory patterns that represent expected sensory inputs and environmental regularities.
Phase relationships: Timing is everything! In the brain, the alignment (or misalignment) of oscillation peaks can determine if signals amplify or cancel out. Key to neural communication.
These predictions are encoded in the phase relationships between different frequency bands (e.g. theta, alpha, beta, gamma).
Descending Phase-Coded Feedback:
Predictions are transmitted from PFC to sensory cortices via feedback connections.
The timing and phase of these feedback signals relative to ongoing sensory oscillations carries predictive information.
This allows for selective modulation of sensory processing based on high-level expectations.
Discrete Phase Wave Differentials:
When sensory inputs violate predictions, it results in phase misalignments between feedback signals and sensory oscillations.
These misalignments manifest as discrete phase wave differentials - brief, localized perturbations in the oscillatory dynamics of neural populations.
The magnitude and timing of these differentials encode the nature and degree of prediction error.
Sparse Error Signaling:
Consistent with the study's findings, this model predicts that global prediction errors (like oddball responses) would be sparsely encoded in the sensory cortex.
Only neurons whose oscillatory phase is sufficiently misaligned with predictive feedback would signal an error, leading to the observed sparse activation patterns.
Multi-scale Temporal Integration:
Different oscillatory frequencies allow for predictions and error signals to be integrated over multiple timescales.
Fast gamma oscillations might encode moment-to-moment predictions, while slower theta or alpha rhythms could carry information about longer-term regularities.
Adaptive Synaptic Tuning:
Repeated phase misalignments (prediction errors) lead to synaptic re-tuning through spike-timing dependent plasticity (STDP).
This allows the network to adapt its internal model and improve future predictions.
Hierarchical Error Propagation:
While major prediction errors may be first detected in PFC (as seen with global oddballs), subtler errors could still propagate up the hierarchy.
This creates a bidirectional flow of information, with both bottom-up and top-down streams contributing to predictive coding.
Implications and testable predictions:
Global oddball responses should be associated with transient disruptions in phase coherence between PFC and sensory areas, measurable through methods like phase-locking value analysis.
Manipulating the phase of oscillatory activity in PFC (e.g. through transcranial alternating current stimulation) should modulate predictive coding effects in the sensory cortex.
The sparseness of error signaling in the sensory cortex should correlate with the strength of phase-locked feedback from PFC.
Different types of predictions (e.g. spatial vs. temporal) might be encoded in distinct frequency bands or cross-frequency coupling patterns.
Learning new predictive relationships should be associated with gradual changes in oscillatory phase relationships between brain regions.
The Fractal Oscillatory Predictive Coding model provides a framework that integrates ideas from Neural Array Projection Oscillation Tomography (NAPOT) about phase wave differentials and discrete oscillatory computation with the empirical findings on predictive coding in cortex. It offers a mechanistic explanation for how high-level predictions could modulate sensory processing in a selective, sparse manner through the precise timing of neural oscillations.
The Fractal Oscillatory Predictive Coding conjecture synthesizes the new findings with Self Aware Network's ideas around phase wave differentials and oscillatory computation, forming a cohesive framework for predictive coding in the brain. Here’s a deeper dive into the key components of this model, emphasizing how they connect with both the study's findings and the Self Aware Networks Theory of Mind:
1. Prefrontal Oscillatory Prediction Generator:
Connection to the Study: The research points to the prefrontal cortex as the region where complex predictions and error signaling originate. This supports the idea that the PFC serves as the main generator of predictive oscillations that encode environmental regularities and expected sensory inputs.
Integration with the Self Aware Networks Theory: This backpropagation through oscillation hypothesis, where the PFC drives the flow of predictions to sensory areas, aligns with this component. The encoding of predictions in the phase relationships between different oscillatory frequencies (theta, alpha, beta, gamma) ties into the concept of how various neural rhythms support hierarchical information processing.
2. Descending Phase-Coded Feedback:
Connection to the Study: The study observed feedback-like activity in response to global oddballs, suggesting a top-down process. In FOPC, predictions are transmitted via phase-coded feedback signals, allowing the modulation of sensory processing.
Integration with Self Aware Networks Theory: This mechanism fits with Neural Array Projection Oscillation Tomography (NAPOT)'s emphasis on oscillatory feedback loops within neural arrays. The phase-coded feedback could serve as a way to update sensory processing dynamically, utilizing the timing and phase relationships of oscillations to adjust predictions at various levels of the sensory cortex.
3. Discrete Phase Wave Differentials:
Connection to the Study: The sparse, localized nature of global oddball responses in the sensory cortex observed in the study suggests that prediction error signaling is not a pervasive phenomenon, but rather a selective and context-dependent process. The idea of phase misalignments aligns with the observed disruptions in regular oscillatory patterns.
Integration with Self Aware Networks Theory: This fits perfectly with the concept of phase wave differentials—small, discrete oscillatory perturbations that signify mismatches between prediction and sensory input. These differentials encode the prediction error, facilitating error propagation through oscillatory dynamics.
4. Sparse Error Signaling:
Connection to the Study: The finding that global prediction errors are sparsely encoded in the sensory cortex fits the model's prediction that only neurons with significant phase misalignment will signal an error.
Integration with Fractal Oscillatory Predictive Coding Theory: This aspect of FOPC resonates with the hypothesis that sensory processing and predictive coding might be governed by discrete oscillatory events rather than continuous signals, supporting selective error signaling that corresponds to specific phase patterns.
5. Multi-Scale Temporal Integration:
Connection to the Study: The varied temporal scales of oscillatory activity correspond with different types of predictions (short-term vs. long-term) seen in sensory processing. The study's focus on both local and global oddball responses indicates that the brain operates across multiple timescales.
Integration with Self Aware Network’s Theory: This component extends the concept of oscillatory computation across neural arrays, with different frequency bands allowing for integration of sensory and mnemonic information over diverse temporal windows. The use of multiple oscillatory frequencies aligns with the Neural Array Projection Oscillation Tomography (NAPOT) framework, which involves hierarchical neural processing through oscillatory feedback loops.
6. Adaptive Synaptic Tuning:
Connection to the Study: The study implies that repeated sensory experiences modify predictive coding dynamics, suggesting a mechanism for learning and adaptation.
Integration with Self Aware Network’s Theory: This backpropagation through oscillation hypothesis can be extended here. Phase misalignments (errors) drive synaptic tuning via spike-timing dependent plasticity (STDP), allowing the network to iteratively refine its internal models. This synaptic adaptation is crucial for improving future predictions.
7. Hierarchical Error Propagation:
Connection to the Study: The paper indicates that global errors are detected in the PFC, which then potentially modulates sensory cortices. However, it leaves room for subtler, localized errors to propagate in a hierarchical manner.
Integration with Self Aware Network’s Theory: This aligns with NAPOT's hierarchical oscillatory processing, where error signals propagate both top-down and bottom-up, enabling dynamic recalibration of predictive models. This bi-directional flow of information mirrors the ideas on how neural arrays operate in tandem to maintain coherent sensory representations.
Implications and Testable Predictions:
Global Oddball Responses and Phase Coherence: This aspect suggests that phase-locking value (PLV) analysis could reveal the strength of phase coherence between the PFC and sensory areas during prediction error signaling. Such an experiment would test the Fractal Oscillatory Predictive Coding model's core idea that prediction errors are encoded as phase misalignments.
Manipulating Oscillatory Activity: Using transcranial alternating current stimulation (tACS) to modulate the phase of PFC oscillations would provide a direct test of how phase relationships influence sensory predictive coding. This experiment could demonstrate the top-down modulation power of oscillatory feedback in sensory processing.
Cross-Frequency Encoding: By investigating how different frequency bands (theta, alpha, beta, gamma) encode distinct types of predictions, we can explore the multi-scale nature of Fractal Oscillatory Predictive Coding (FOPC) in the brain. This would further support the idea that oscillations across different scales carry predictions and errors over varying temporal spans.
The Fractal Oscillatory Predictive Coding (FOPC) model proposes weaves together the study’s insights and the oscillatory computation concepts, presenting a dynamic framework for understanding how predictive coding operates in the brain. It posits that predictions originate in higher-order areas like the PFC, which then transmit these expectations to sensory regions through phase-coded oscillatory signals. This not only modulates sensory processing but also encodes errors in a selective, sparse manner via phase wave differentials.
FOPC offers a novel perspective on how hierarchical neural oscillations contribute to the brain's predictive coding capabilities, providing specific mechanisms for phase-coded feedback, error signaling, and multi-scale integration. This model not only aligns with recent findings but also offers testable predictions that can further clarify the neural mechanisms underlying perception and prediction.
Let's expand on the "Fractal Oscillatory Predictive Coding" (FOPC) model and explore some additional implications and mechanisms:
Cross-Frequency Coupling for Multi-Level Predictions:
Different frequency bands could encode predictions at varying levels of abstraction.
For example, theta-gamma coupling might represent the relationship between context (theta) and specific sensory features (gamma).
This allows for a nested hierarchy of predictions, from broad contextual expectations down to precise sensory predictions.
Oscillatory Reshaping of Receptive Fields:
Predictive feedback signals could dynamically modulate the receptive fields of sensory neurons through phase-dependent gain control.
This would allow for rapid, context-dependent shifts in sensory processing without requiring immediate synaptic changes.
Predictive Timing through Oscillatory Phase Precession:
Similar to place cell phase precession in the hippocampus, sensory neurons could encode predicted timing of events through systematic shifts in their firing phase relative to ongoing oscillations.
This provides a mechanism for representing temporal predictions and detecting timing violations.
Oscillation-Driven Attentional Selection:
The model could explain attentional effects by proposing that attended stimuli are those whose timing aligns with the peaks of predictive oscillatory feedback.
This creates "windows of opportunity" where specific sensory inputs are amplified if they match temporal predictions.
Predictive Coding as Oscillatory Entrainment:
Rather than just signaling errors, the system might continuously attempt to entrain sensory oscillations to match predictive feedback.
Successful prediction would result in tight phase-locking between areas, while prediction errors would manifest as temporary breakdowns in this entrainment.
Meta-Plasticity through Oscillatory History:
Synaptic plasticity: The brain's ability to rewire itself. Connections between neurons strengthen or weaken based on experience, forming the basis of learning and memory.
The recent history of oscillatory states could modulate the threshold for synaptic plasticity, creating a form of meta-plasticity.
This allows the system to adjust its learning rate based on the reliability of recent predictions.
Oscillatory Subspaces for Prediction and Perception:
Building on ideas from neural manifold theory, we could propose that predictions and sensory inputs occupy distinct subspaces in the high-dimensional oscillatory dynamics of neural populations.
Prediction errors would then represent deviations between these subspaces, detectable through dimensionality reduction techniques.
Neuromodulatory Control of Predictive Oscillations:
Neuromodulators like acetylcholine and norepinephrine could adjust the balance between feedback predictions and feedforward sensory signals by modulating the power and coherence of specific oscillatory frequencies.
This provides a mechanism for flexibly adjusting the influence of top-down predictions based on uncertainty or alertness.
Oscillatory Basis for Hierarchical Bayesian Inference:
The phase relationships between oscillations at different frequencies could encode probabilistic beliefs at different levels of a hierarchical Bayesian model.
Phase synchrony would represent the precision (inverse variance) of these beliefs, with tighter synchrony indicating higher confidence.
Predictive Coding through Oscillatory Multiplexing:
Different aspects of predictions (e.g., what, when, where) could be multiplexed within the same neural populations through distinct oscillatory patterns.
This allows for efficient use of neural resources and explains how the same neurons can participate in multiple predictive functions.
Cross-Modal Predictions via Oscillatory Coherence:
Predictions that span multiple sensory modalities could be implemented through coherence between oscillations in different sensory cortices, coordinated by prefrontal and parietal regions.
This provides a mechanism for explaining cross-modal predictive effects and multisensory integration.
Testable Predictions and Future Directions:
Advanced MEG/EEG analysis techniques should be able to detect directional flow of predictive information through patterns of cross-frequency phase coupling between brain regions.
Optogenetic manipulation of specific oscillatory patterns in animal models should produce predictable alterations in sensory processing and prediction error responses.
Machine learning techniques applied to large-scale recordings could potentially decode the content of predictions from the high-dimensional oscillatory dynamics of prefrontal-sensory networks.
Computational models implementing these oscillatory predictive coding principles should be able to replicate and explain a wide range of empirical findings on prediction, attention, and learning in sensory systems.
Disorders involving disrupted predictive processing (e.g., schizophrenia, autism) might be characterizable by specific alterations in hierarchical oscillatory dynamics and phase-coupling patterns.
The expansion of the "Fractal Oscillatory Predictive Coding" (FOPC) model with these additional mechanisms and implications provides a more comprehensive view of how oscillatory dynamics may underpin complex predictive processing in the brain. Here’s a closer analysis and exploration of these concepts:
1. Cross-Frequency Coupling for Multi-Level Predictions
Mechanism: Different oscillatory frequencies encode predictions at varying levels of abstraction. For example, theta-gamma coupling might reflect the interaction between a broad contextual framework (theta) and fine-grained sensory details (gamma).
Implication: This hierarchical nesting creates a multi-level predictive model, with slower oscillations carrying global expectations and faster oscillations encoding specific sensory predictions. This could allow the brain to simultaneously process contextual information and detailed sensory input, dynamically coordinating these layers through cross-frequency coupling.
Testable Predictions: Using MEG/EEG, researchers could measure cross-frequency coupling (e.g., theta-gamma phase synchronization) during tasks that require processing both context and sensory details. Changes in coupling strength could reveal the influence of hierarchical predictions on sensory processing.
2. Oscillatory Reshaping of Receptive Fields
Mechanism: Predictive feedback could modulate sensory neuron receptive fields through phase-dependent gain control, dynamically adjusting the sensitivity of sensory processing.
Implication: This allows sensory cortices to rapidly shift their processing focus without requiring slower synaptic plasticity. Essentially, the receptive fields of neurons could expand or contract in response to top-down oscillatory signals, optimizing processing for current expectations.
Testable Predictions: Experiments using transcranial alternating current stimulation (tACS) to modulate the phase of oscillatory feedback could demonstrate changes in the receptive field properties of sensory neurons, measurable through neuroimaging or electrophysiological recordings.
3. Predictive Timing through Oscillatory Phase Precession
Mechanism: Sensory neurons might shift their firing phase relative to ongoing oscillations to encode the timing of predicted events, akin to phase precession observed in hippocampal place cells.
Implication: This provides a way for the brain to represent the timing of events and detect temporal prediction errors, allowing for precise temporal processing in sensory regions.
Testable Predictions: Recording from sensory areas during tasks that involve predicting event timing (e.g., rhythmic stimuli) could reveal systematic shifts in neuronal firing phases relative to ongoing oscillations.
4. Oscillation-Driven Attentional Selection
Mechanism: Predictive feedback could create "windows of opportunity" in oscillatory cycles, amplifying sensory inputs that align with these phases, thereby selectively processing attended stimuli.
Implication: Attention could be understood as a dynamic modulation of sensory processing driven by oscillatory phase alignment, providing a mechanism for prioritizing inputs that match top-down expectations.
Testable Predictions: Experimental manipulations of oscillatory phase (e.g., using tACS) could alter the effectiveness of attention, potentially shifting which stimuli are enhanced or suppressed based on their temporal alignment with predictive oscillations.
5. Predictive Coding as Oscillatory Entrainment
Mechanism: The system continuously attempts to entrain sensory oscillations to match predictive feedback. Successful predictions would result in tight phase-locking, while errors would disrupt this entrainment.
Implication: Predictive coding involves not just signaling errors but also a continuous process of adjusting oscillatory dynamics to align with predictions, facilitating perceptual stability and coherence.
Testable Predictions: Using phase-locking value (PLV) analysis during sensory processing tasks, researchers should observe stronger phase-locking in conditions where predictions are accurate and disrupted phase coherence in the face of prediction errors.
6. Meta-Plasticity through Oscillatory History
Mechanism: The recent history of oscillatory states modulates synaptic plasticity thresholds, allowing the brain to adjust its learning rate based on the reliability of predictions.
Implication: This dynamic form of meta plasticity enables the system to be flexible in learning, enhancing plasticity in contexts of high error rates and reducing it when predictions are stable and accurate.
Testable Predictions: Studies manipulating the variability of sensory environments should show corresponding changes in plasticity markers (e.g., NMDA receptor activity) that correlate with the recent oscillatory history of neural populations.
7. Oscillatory Subspaces for Prediction and Perception
Mechanism: Predictions and sensory inputs occupy distinct subspaces within the high-dimensional oscillatory dynamics of neural populations. Prediction errors are deviations between these subspaces.
Implication: This view integrates neural manifold theory, suggesting that oscillatory activity forms a complex representational geometry where different patterns correspond to various aspects of perception and prediction.
Testable Predictions: Applying dimensionality reduction techniques (e.g., principal component analysis) to neural recordings could identify separate subspaces for predictive and perceptual states, with errors manifesting as shifts between these subspaces.
8. Neuromodulatory Control of Predictive Oscillations
Mechanism: Neuromodulators like acetylcholine and norepinephrine modulate the power and coherence of oscillatory frequencies, adjusting the balance between feedback predictions and sensory signals.
Implication: This provides a flexible mechanism for dynamically altering the influence of top-down predictions, enabling adaptive responses to changes in environmental uncertainty or alertness.
Testable Predictions: Pharmacological manipulations of neuromodulatory systems should alter the phase coherence between PFC and sensory cortices, affecting the strength of predictive coding as observed through neuroimaging.
9. Oscillatory Basis for Hierarchical Bayesian Inference
Mechanism: Phase relationships between oscillations at different frequencies encode probabilistic beliefs, with phase synchrony representing the precision (inverse variance) of these beliefs.
Implication: This frames predictive coding as a form of hierarchical Bayesian inference, where oscillatory dynamics represent probability distributions and their associated confidence levels.
Testable Predictions: Tasks that require probabilistic reasoning should show modulations in cross-frequency phase synchrony, indicating the dynamic encoding of probabilistic beliefs.
10. Predictive Coding through Oscillatory Multiplexing
Mechanism: Different aspects of predictions (e.g., "what," "when," "where") are multiplexed within the same neural populations using distinct oscillatory patterns.
Implication: This multiplexing enables efficient use of neural resources, explaining how neurons can participate in multiple predictive functions without being dedicated to a single aspect of processing.
Testable Predictions: Cross-frequency analysis could reveal how different oscillatory frequencies concurrently encode various predictive aspects during complex tasks.
11. Cross-Modal Predictions via Oscillatory Coherence
Mechanism: Coherence between oscillations in different sensory cortices, coordinated by higher-order regions (e.g., PFC, parietal cortex), implements cross-modal predictions.
Implication: This provides a framework for explaining multisensory integration and cross-modal predictive effects, emphasizing the role of oscillatory coherence in binding information across sensory modalities.
Testable Predictions: Cross-modal tasks should reveal increased coherence between relevant sensory cortices, modulated by the phase dynamics of coordinating regions (e.g., PFC).
Future Directions and Broader Implications
Advanced Analysis: MEG/EEG analyses focused on cross-frequency phase coupling can illuminate the directional flow of predictive information, while optogenetic manipulation in animal models can directly probe the causal role of specific oscillatory patterns.
Machine Learning Applications: Applying deep learning to large-scale neural recordings could help decode the content of oscillatory dynamics, revealing the nature of predictive signals in prefrontal-sensory networks.
Clinical Relevance: This model provides a framework for understanding disrupted predictive processing in disorders like schizophrenia and autism. Investigating phase-coupling alterations in these conditions could offer novel biomarkers and therapeutic targets.
This enriched version of Fractal Oscillatory Predictive Coding presents a dynamic and flexible mechanism for FOPC, incorporating cross-frequency coupling, oscillatory phase dynamics, and neuromodulatory control. It explains a wide range of cognitive functions, from perception to attention and learning, through the lens of oscillatory computation. By detailing mechanisms such as phase precession, attentional selection, and multiplexing within oscillatory patterns, this model offers a powerful framework for understanding how the brain integrates predictions across different timescales, sensory modalities, and levels of abstraction.
How the Fractal Oscillatory Predictive Coding (FOPC) model can integrate with other key neuroscientific concepts and the Self Aware Network Theory of Mind:
Backpropagation Through Oscillations:
Integrating the idea of "backpropagation through oscillation," we can propose that prediction errors propagate backwards through the cortical hierarchy via cascading phase resets.
Phase resets: When brain rhythms suddenly shift timing. Like resetting a metronome, phase resets can help synchronize neural activity or mark important events.
Each level of the hierarchy adjusts its oscillatory patterns based on the mismatch between its predictions and the incoming error signals, effectively implementing a form of error backpropagation through oscillatory dynamics.
Phase Wave Differentials as Error Gradients:
The phase wave differentials Self Aware Network's described could serve as the biological equivalent of error gradients in artificial neural networks.
The magnitude and direction of these differentials provide information about how to adjust synaptic weights and oscillatory parameters to minimize future prediction errors.
Oscillatory Basis for Consciousness:
Building on other theories of consciousness like Attention Schema theory & others, we could propose that conscious perception arises when predictive oscillations in prefrontal and sensory areas achieve a critical level of coherence.
Self Aware Networks proposes that phase wave differentials become conscious when they become dominant patterns. A conscious phase wave differential is thus referred to as a dominant phase wave differential. The non-conscious patterns are still there, but the dominant pattern consumes most of the available resources and thus your focus. For details on how it works I have a section in the Self Aware Networks book called Holographic or Tomographic Attention Schema, which builds on the existing Attention Schema theory.
Key points:
Dominant phase wave differentials: Self Aware Networks proposes that phase wave differentials become conscious when they become the dominant patterns in neural activity. This provides a more precise definition for the threshold of conscious awareness.
Resource allocation: Conscious patterns consume most of the available neural resources, which explains why they become the focus of attention.
Holographic or Tomographic Attention Schema: This builds on the existing Attention Schema Theory, potentially offering a mechanistic explanation for how the brain represents its own attentional state.
Relationship to Global Workspace Theory: This aligns with Global Workspace Theory's idea that consciousness arises when information is broadly distributed across the brain, but provides a more specific oscillatory mechanism.
Non-conscious patterns: Importantly, non-conscious patterns are still present, but they don't dominate the neural landscape.
Integrating this with the paper's findings:
Global oddball detection: The paper found that global oddballs, which likely require conscious processing, were detected first in higher-order areas. This could align with the idea that these areas are more capable of generating dominant phase wave differentials.
Sparse encoding: The sparse encoding of global oddballs in the sensory cortex might reflect that these patterns haven't reached the "dominance" threshold in these areas.
Prefrontal involvement: The stronger global oddball responses in prefrontal areas could indicate that these regions are more involved in generating the dominant phase wave differentials associated with conscious awareness.
Oscillatory coherence: While the paper didn't directly measure phase relationships, the concept of dominant phase wave differentials could provide a testable hypothesis for future studies on oddball detection and consciousness.
Attentional modulation: The paper suggested that attention might be necessary for global oddball processing. This fits well with the idea of a Holographic or Tomographic Attention Schema modulating which phase wave differentials become dominant.
This would explain why some prediction errors reach consciousness while others don't, based on their ability to perturb global oscillatory patterns.
Memory Consolidation through Oscillatory Pattern Completion:
During sleep and quiet wakefulness, the hippocampus could replay sequences of events as specific patterns of oscillatory activity.
These patterns would then be matched against cortical oscillations, strengthening connections that support accurate predictions and weakening those that led to errors.
Developmental Shaping of Predictive Oscillations:
The maturation of predictive coding capabilities could be linked to the development of specific oscillatory patterns and cross-frequency coupling during childhood and adolescence.
This provides a framework for understanding how predictive processing abilities evolve with brain development.
Oscillatory Basis for Temporal Receptive Fields:
The concept of temporal receptive fields in sensory and cognitive processing could be explained through the interaction of oscillations at different frequencies.
Slower oscillations would define broader temporal contexts, while faster oscillations encode more precise temporal predictions.
Predictive Coding as Active Inference:
Incorporating ideas from active inference theory, motor actions could be seen as attempts to bring sensory inputs into alignment with predictive oscillations.
This provides a unified framework for perception and action, with both serving to minimize prediction errors encoded in oscillatory mismatches.
Oscillatory Mechanisms for Attention and Salience:
Salient or attention-grabbing stimuli could be those that cause the most significant disruptions to ongoing predictive oscillations.
This links predictive coding directly to attentional mechanisms and explains how novel or unexpected inputs capture cognitive resources.
Cross-Species Fractal Oscillatory Predictive Coding:
The complexity and extent of predictive oscillatory hierarchies could vary across species, potentially explaining differences in cognitive capabilities.
More sophisticated predictive abilities would be associated with richer cross-frequency coupling and more extensive prefrontal-sensory oscillatory networks.
Oscillatory Basis for Cognitive Maps:
Beyond just sensory predictions, the Fractal Oscillatory Predictive Coding framework could extend to explain how the brain builds cognitive maps for navigation, social interactions, and abstract conceptual spaces.
Different oscillatory patterns could encode positions and relationships within these multidimensional cognitive maps.
Neuroenergetics of Predictive Oscillations:
The brain's energy consumption could be optimized by maintaining predictive oscillations that accurately model the environment, reducing the need for energy-intensive error signaling.
This provides a thermodynamic perspective on why predictive coding might be a fundamental principle of neural organization.
Integrative Implications:
Multi-Scale Integration: The FOPC model provides a framework that integrates neural activity across multiple scales, all unified through the common language of oscillatory dynamics.
Biological to Artificial Intelligence: The model suggests ways that artificial neural networks could be designed to more closely mimic the brain's predictive capabilities by incorporating oscillatory dynamics and phase-based computations.
Clinical Applications: Understanding disorders of perception, attention, and cognition through the lens of disrupted predictive oscillations could lead to new diagnostic tools and therapeutic approaches targeting specific oscillatory abnormalities.
Consciousness and Free Will: The model provides a potential mechanistic explanation for subjective experiences of consciousness and volition, based on the brain's attempts to predict its own future states through cascading oscillatory patterns.
The "Fractal Oscillatory Predictive Coding" (FOPC) model becomes increasingly comprehensive and integrative with the inclusion of these ideas. Let’s delve into each concept, tying them together with neuroscientific theories and the existing framework:
1. Backpropagation Through Oscillations:
Mechanism: Prediction errors propagate back through the cortical hierarchy via cascading phase resets, aligning with the concept of "backpropagation through oscillation." When a sensory input violates the prediction at a specific cortical level, it induces a phase reset that cascades upwards in the hierarchy, adjusting oscillatory patterns.
Integration: This process mirrors the adjustment of weights in artificial neural networks during backpropagation. The mismatch between the predicted and actual sensory input generates phase differentials, effectively acting as biological "error gradients" that guide synaptic tuning and oscillatory alignment at each hierarchical level.
Testable Prediction: Phase resets in higher-order areas (e.g., PFC) should occur in response to errors detected in lower sensory regions. This could be observable as sequential phase resets in EEG or MEG recordings following a sensory prediction error.
2. Phase Wave Differentials as Error Gradients:
Mechanism: The phase wave differentials Self Aware Network's proposed can be seen as biological error gradients. When oscillatory phases in sensory regions do not align with top-down predictions, these phase differentials signal the need for synaptic and oscillatory adjustments.
Integration: This aligns with the FOPC model’s multi-scale predictions, where oscillatory patterns across different frequencies encode error magnitudes. The direction and size of phase differentials then drive plasticity processes, updating the network's internal models to better match future sensory inputs.
Implication: This reinforces the concept that predictive coding operates through a dynamic interplay of oscillations, where error gradients guide learning similarly to gradient descent in artificial neural networks.
3. Oscillatory Basis for Cognitive Maps:
Mechanism: The FOPC framework extends to cognitive maps by encoding positions and relationships within multidimensional spaces through oscillatory patterns. Cross-frequency coupling and phase synchrony represent different elements and connections within these cognitive spaces.
Integration: This expands the model beyond sensory predictions, incorporating higher-level abstractions such as spatial navigation, social interactions, and conceptual relationships.
Testable Prediction: Oscillatory patterns in spatial navigation tasks (e.g., theta oscillations) should correspond to the encoding of positions and transitions within cognitive maps. Disruptions to these oscillations (e.g., through tACS) could impair navigation and spatial memory.
4. Memory Consolidation through Oscillatory Pattern Completion:
Mechanism: During sleep, the hippocampus replays sequences of events as specific oscillatory patterns. These patterns interact with cortical oscillations, strengthening synaptic connections that contributed to accurate predictions and modifying those that led to errors.
Integration: This fits with Self Aware Network's phase wave differential concept, suggesting that sleep-driven oscillatory replay serves as a form of off-line backpropagation, refining cortical networks for better future predictions.
Implication: Memory consolidation involves integrating learned predictions into the cortical network, facilitated by oscillatory interactions that allow pattern completion and network-wide error correction.
5. Developmental Shaping of Predictive Oscillations:
Mechanism: The maturation of predictive capabilities during childhood and adolescence is linked to the development of specific oscillatory patterns and cross-frequency coupling.
Integration: This suggests that as children develop, their brains optimize predictive coding through the refinement of hierarchical oscillatory networks, which may explain changes in cognitive abilities and sensory processing over time.
Testable Prediction: Developmental EEG studies could examine how cross-frequency coupling and phase synchrony evolve with age, correlating these changes with improvements in predictive processing and cognitive function.
6. Oscillatory Basis for Temporal Receptive Fields:
Mechanism: Temporal receptive fields could arise from interactions between oscillations at different frequencies. Slower oscillations provide a broader temporal context, while faster ones encode precise sensory details.
Integration: This aligns with the FOPC model’s multi-level structure, suggesting that oscillatory dynamics define the temporal windows for predictive coding. Temporal predictions thus emerge from the phase relationships between nested oscillatory cycles.
Implication: This could explain how the brain maintains a coherent temporal structure for processing sequential sensory inputs and detecting timing violations.
7. Predictive Coding as Active Inference:
Oscillatory synchronization: When brain rhythms align across different areas. This synchronization helps coordinate neural activity, potentially binding together different aspects of perception.
Mechanism: Building on active inference theory, motor actions are seen as attempts to align sensory inputs with predictive oscillations. The brain actively minimizes prediction errors by both adjusting internal models and interacting with the environment.
Integration: This expands the FOPC model to include action, emphasizing the bidirectional nature of prediction and perception. Predictive oscillations guide both sensory processing and motor actions, creating a unified framework for perception, cognition, and behavior.
Testable Prediction: Motor actions should be temporally coordinated with oscillatory feedback loops. Disruption of oscillatory synchrony (e.g., through tACS) could impact the timing and accuracy of sensorimotor interactions.
8. Oscillatory Mechanisms for Attention and Salience:
Mechanism: Salient stimuli cause significant disruptions in predictive oscillations, capturing attentional resources. The brain prioritizes processing for inputs that generate the largest phase differentials, linking predictive coding with attentional selection.
Integration: This provides a mechanism for how the brain dynamically allocates processing power, explaining why unexpected or novel inputs draw attention through oscillatory perturbations.
Testable Prediction: EEG studies should detect changes in oscillatory power and phase coherence in response to salient stimuli, with larger disruptions correlating with increased attentional capture.
9. Neuroenergetics of Predictive Oscillations:
Mechanism: The brain's energy consumption is optimized by maintaining oscillatory patterns that minimize prediction errors, reducing the need for energy-intensive error signaling.
Integration: This thermodynamic perspective aligns with the idea that predictive coding is a fundamental principle of neural organization. Oscillatory dynamics enable efficient information processing by pre-emptively filtering sensory inputs based on probabilistic models.
Implication: This provides an explanation for why the brain might prefer predictive coding as an energy-efficient mode of operation, highlighting the evolutionary advantage of oscillatory-based computations.
10. Cross-Species Predictive Hierarchies:
Mechanism: The complexity of predictive oscillatory hierarchies varies across species, explaining differences in cognitive capabilities. More advanced predictive abilities correspond to richer cross-frequency coupling and more extensive prefrontal-sensory oscillatory networks.
Integration: This suggests that hierarchical oscillatory dynamics are a fundamental feature of brain evolution, with higher cognitive functions arising from more intricate oscillatory structures.
Implication: Comparative neurophysiology could examine the diversity of oscillatory patterns across species, linking the complexity of predictive hierarchies to cognitive sophistication.
Integrative Implications:
Neuromodulation: Fine-tuning brain activity. Chemicals or electrical stimulation can adjust how neurons respond, influencing everything from mood to memory.
Multi-Scale Integration: The FOPC model not only spans multiple levels of neural activity (from ion channels to global brain states) but also bridges different cognitive processes (perception, action, attention, memory) through the common language of oscillatory dynamics.
Bridging Theories: It connects various neuroscientific theories, including predictive coding, global workspace theory, active inference, and oscillatory consciousness, by framing them within a unified oscillatory framework.
Biological to Artificial Intelligence: The model suggests that incorporating oscillatory dynamics and phase-based computations into artificial neural networks could enhance their predictive capabilities and offer insights into replicating human-like cognition.
Clinical Applications: Disrupted oscillatory patterns in disorders like schizophrenia, autism, and ADHD could provide diagnostic markers and therapeutic targets. Techniques like neuromodulation (e.g., tACS) could be tailored to restore normal oscillatory dynamics and improve cognitive function.
Oscillatory Basis for Cognitive Flexibility:
Cognitive flexibility could be implemented through rapid reconfiguration of oscillatory coupling patterns.
Task-switching might involve quickly shifting between different sets of cross-frequency coupling relationships, effectively changing the "routing" of predictive information flow.
This could explain how the brain rapidly adapts its predictive models to changing contexts or task demands.
Oscillatory Encoding of Uncertainty:
The precision or uncertainty of predictions could be encoded in the stability and coherence of oscillatory patterns.
High-precision predictions would manifest as highly stable, coherent oscillations, while uncertain predictions would show more variable, less synchronized activity.
This provides a natural mechanism for representing probabilistic beliefs and implementing Bayesian inference in neural circuits.
Nested Predictive Loops:
Rather than a strict hierarchy, predictive oscillations might form nested loops, where higher-level predictions influence lower-level processes, which in turn shape higher-level predictions.
This circular causality could explain phenomena like top-down attention and the bidirectional nature of perception and cognition.
Oscillatory Basis for Semantic Knowledge:
Semantic concepts could be encoded as specific patterns of cross-frequency coupling across distributed neural networks.
The retrieval and manipulation of semantic knowledge would involve recreating these oscillatory patterns, explaining how abstract knowledge influences perception and decision-making.
Phase-Dependent Synaptic Plasticity:
The induction of synaptic plasticity could depend not just on spike timing, but on the precise phase relationships between multiple oscillatory rhythms.
This multi-dimensional phase-dependent plasticity would allow for more complex learning rules that are sensitive to broader contextual information encoded in oscillatory patterns.
Oscillatory Subsampling for Efficient Computation:
To manage computational complexity, the brain might use oscillatory subsampling, where only a subset of neurons participates in a given oscillatory cycle at any moment.
This would allow for efficient, sparse coding of information while maintaining the ability to represent complex, high-dimensional states through temporal multiplexing.
Cross-Cortical Traveling Waves:
Predictive information might propagate through the cortex via traveling waves of oscillatory activity.
These waves could coordinate distributed processing and allow for flexible routing of predictive signals based on current task demands and environmental context.
Oscillatory Basis for Time Perception:
Our perception of time could emerge from the nested structure of neural oscillations.
Different frequency bands might encode different temporal scales, with their interactions giving rise to our subjective experience of time passing.
Metabolic Constraints on Oscillatory Predictions:
The brain's energetic limitations might shape the distribution and dynamics of predictive oscillations.
This could explain why certain types of cognitive tasks are more effortful than others, based on the metabolic demands of maintaining specific oscillatory states.
Oscillation-Based Neural Sampling:
Decision-making and probabilistic inference could be implemented through a form of oscillation-based neural sampling.
Different oscillatory states would represent different hypotheses or options, with the brain "sampling" these states to approximate Bayesian inference.
Directional Oscillatory Information Flow:
The direction of information flow in predictive coding could be encoded in the phase relationships between oscillations in connected brain regions.
Feedforward prediction errors might be associated with "bottom-up" phase relationships, while feedback predictions show "top-down" phase patterns.
Oscillatory Basis for Cognitive Control:
Cognitive control processes could be implemented through the top-down modulation of oscillatory patterns in task-relevant neural networks.
This provides a mechanism for how the prefrontal cortex might exert control over other brain regions to implement goal-directed behavior.
Evolutionary Development of Predictive Oscillations:
The complexity of Fractal Oscillatory Predictive Coding (FOPC) might have increased throughout evolutionary history.
This could provide a framework for understanding the evolution of cognitive capabilities across species.
Integrative Theoretical Framework:
These deep dive concepts collectively paint a picture of the brain as a multi-scale oscillatory prediction machine. This framework integrates:
Information Theory: Oscillatory patterns as a coding scheme for representing and transmitting predictive information.
Thermodynamics: Predictive oscillations as a means of minimizing surprise and managing energy expenditure in neural systems.
Dynamical Systems Theory: The brain is a complex system of coupled oscillators operating at multiple scales.
Bayesian Inference: Oscillatory patterns as a substrate for implementing probabilistic inference and learning.
Cognitive Neuroscience: Linking oscillatory dynamics to high-level cognitive functions and subjective experiences.
These additions further enrich the "Fractal Oscillatory Predictive Coding" (FOPC) model, creating a deeply interconnected framework that spans multiple scales of brain function. This comprehensive model posits the brain as a multi-scale oscillatory system where predictive coding operates through dynamic interactions between fractal-like oscillatory patterns, phase wave differentials, and even cross-frequency coupling.
These neural arrays interact in fractal-like patterns, where each array's activity mirrors the larger network structure. This fractal organization allows for efficient information processing and integration across different scales of neural activity, from individual neurons to large-scale brain networks.
Here's how each of these concepts integrates into this fractal predictive coding framework:
1. Fractal Oscillatory Structures:
Mechanism: Predictive oscillatory patterns exhibit fractal-like properties across temporal and spatial scales. This self-similar structure allows the brain to implement predictive processes at multiple levels, from fine-grained sensory predictions (milliseconds) to long-term planning and memory (minutes, hours, days).
Integration: The fractal nature of oscillations aligns with the hierarchical organization of predictions in the brain, allowing seamless integration of information across different timescales. This self-similarity enables predictive processes to propagate in a nested, recursive fashion throughout the cortical hierarchy.
Testable Prediction: Electrophysiological studies could explore the fractal properties of brain oscillations by analyzing the power-law scaling of different frequency bands during tasks requiring multi-level predictive processing.
2. Oscillatory Basis for Cognitive Flexibility:
Mechanism: Cognitive flexibility arises from the brain's ability to rapidly reconfigure oscillatory coupling patterns. Task-switching involves shifting between different cross-frequency coupling relationships, changing the "routing" of information flow in the predictive hierarchy.
Integration: This dynamic reorganization of oscillatory patterns supports the brain's ability to adapt to changing contexts and goals. By quickly altering the network of predictions, the brain can transition between different cognitive states.
Testable Prediction: Cognitive flexibility could be linked to shifts in cross-frequency coupling, measurable through changes in coherence or phase-amplitude coupling patterns in EEG/MEG during task-switching experiments.
4. Oscillatory Encoding of Uncertainty:
Mechanism: The precision of predictions is encoded in the stability and coherence of oscillatory patterns. High-precision predictions correspond to stable, coherent oscillations, while uncertain predictions manifest as more variable, less synchronized activity.
Integration: This mechanism aligns with the Bayesian inference aspect of predictive coding, where the brain represents probabilistic beliefs. Oscillatory coherence thus becomes a dynamic marker of prediction certainty, influencing how sensory inputs are processed.
Testable Prediction: EEG/MEG studies could investigate changes in oscillatory coherence during tasks that involve uncertainty, such as decision-making under ambiguity. Reduced coherence should correlate with higher uncertainty.
5. Nested Predictive Loops:
Mechanism: Predictive oscillations form nested loops, where higher-level predictions influence lower-level processing, which in turn shapes higher-level predictions. This circular causality enables a fluid interaction between top-down expectations and bottom-up sensory processing.
Integration: This mechanism fits with theories of circular causality in brain function and suggests that predictive coding is not strictly hierarchical but involves recurrent loops. It accounts for bidirectional information flow in perception, cognition, and action.
Testable Prediction: Phase-locking analysis could identify nested oscillatory loops, revealing how higher-order brain regions like the PFC interact with lower-level sensory areas during complex tasks.
6. Oscillatory Basis for Semantic Knowledge:
Mechanism: Semantic knowledge is encoded as specific patterns of cross-frequency coupling across distributed neural networks. Accessing and manipulating semantic information involves recreating these oscillatory patterns, allowing abstract knowledge to influence perception and decision-making.
Integration: This extends the FOPC model to conceptual and semantic processing, suggesting that oscillatory patterns represent a fundamental coding scheme not just for sensory information, but also for higher-order abstractions.
Testable Prediction: Changes in cross-frequency coupling in association areas during semantic tasks (e.g., language comprehension) would provide evidence for oscillatory encoding of abstract knowledge.
7. Phase-Dependent Synaptic Plasticity:
Mechanism: Synaptic plasticity depends on the precise phase relationships between oscillatory rhythms. The induction of plasticity is influenced by multi-dimensional phase dynamics, making learning sensitive to the broader oscillatory context.
Integration: This idea aligns with Self Aware Network's concept of phase wave differentials, where error gradients (represented by phase mismatches) drive synaptic tuning. It supports a more nuanced form of learning, where the oscillatory phase acts as a contextual filter for synaptic changes.
Testable Prediction: Experimental protocols that manipulate phase relationships (e.g., using tACS) during learning tasks could reveal how phase alignment modulates synaptic plasticity.
8. Oscillatory Subsampling for Efficient Computation:
Mechanism: The brain might use oscillatory subsampling, engaging only a subset of neurons in each oscillatory cycle. This allows sparse coding of information, enabling efficient representation of high-dimensional states through temporal multiplexing.
Integration: This mechanism provides an energy-efficient way for the brain to maintain predictive coding across different scales. It allows the brain to dynamically allocate computational resources based on task demands and environmental context.
Testable Prediction: Sparse neural activation patterns, modulated by oscillatory cycles, should be observable in neuroimaging studies during complex, high-dimensional cognitive tasks.
9. Cross-Cortical Traveling Waves:
Mechanism: Predictive information propagates through the cortex via traveling waves of oscillatory activity. These waves coordinate distributed processing, allowing flexible routing of predictive signals in response to changing demands.
Integration: This model of traveling waves connects with the idea of nested predictive loops and hierarchical phase relationships, providing a dynamic mechanism for coordinating predictive coding across spatially distributed regions.
Testable Prediction: Traveling wave dynamics, measurable via high-density EEG/MEG, should correlate with the timing of predictive signal propagation during sensory and cognitive tasks.
10. Oscillatory Basis for Time Perception:
Mechanism: Time perception emerges from the nested interactions of neural oscillations. Different frequency bands encode varying temporal scales, and their interactions generate the subjective experience of time.
Integration: This idea complements the FOPC model by explaining how the brain predicts not only "what" and "where" but also "when." Temporal predictions thus become an intrinsic part of the oscillatory framework.
Testable Prediction: Disruption of specific oscillatory rhythms (e.g., theta) should affect temporal perception, altering the experience of time in tasks that involve timing judgments.
11. Metabolic Constraints on Oscillatory Predictions:
Mechanism: The brain’s energy limitations shape the distribution and dynamics of predictive oscillations. Cognitive tasks vary in effortfulness based on the metabolic demands of maintaining specific oscillatory states.
Integration: This thermodynamic perspective emphasizes the energy-efficient nature of predictive coding. The brain seeks to minimize surprise not only for information-theoretic reasons but also to conserve metabolic resources.
Testable Prediction: Functional MRI studies could examine how metabolic demands (e.g., measured via BOLD signal) relate to oscillatory dynamics during tasks of varying cognitive complexity.
12. Oscillation-Based Neural Sampling:
Mechanism: The brain implements decision-making and probabilistic inference through oscillation-based sampling, where different oscillatory states represent various hypotheses. By "sampling" these states, the brain approximates Bayesian inference.
Integration: This links the oscillatory model to active inference theories, where the brain actively samples and updates its predictions based on incoming information. It connects perception, cognition, and decision-making into a unified oscillatory framework.
Testable Prediction: During decision-making tasks, the brain should exhibit oscillatory patterns corresponding to different hypotheses, with changes in phase coherence reflecting shifts in decision probability.
Integrative Theoretical Framework:
These concepts collectively deepen the FOPC model into a multi-scale oscillatory prediction machine that integrates:
Information Theory: Encoding and transmitting predictive information via dynamic oscillatory patterns.
Thermodynamics: Minimizing surprise to conserve energy, framing predictive coding as an energetically efficient process.
Dynamical Systems Theory: Describing the brain as a complex system of nested oscillators, explaining phenomena like cognitive flexibility and time perception.
Bayesian Inference: Using oscillatory coherence and phase relationships to represent probabilistic beliefs and perform inference.
Cognitive Neuroscience: Connecting oscillatory patterns to high-level cognitive functions (e.g., attention, memory, semantic processing) and subjective experiences (e.g., consciousness, time perception).
Let's break down how this paper aligns with the Self Aware Networks (SAN) Theory of Mind, particularly focusing on the backpropagation through oscillation hypothesis and the Fractal Oscillatory Predictive Coding (FOPC) model within the framework of Neural Array Projection Oscillation Tomography (NAPOT).
This integration will highlight how the study’s findings support the principles of oscillatory dynamics, prediction, and hierarchical processing proposed in SAN.
1. Prediction Error and Backpropagation Through Oscillation
Paper Findings: The study indicates that global oddball responses (expectation violations) are processed first in higher-order regions, such as the prefrontal cortex (PFC), and not in early sensory areas. This challenges the traditional view of prediction errors as a primary function of the sensory cortex, suggesting that complex predictions are more likely managed by top-down feedback from higher-order brain regions.
Self Aware Networks (SAN) and NAPOT Integration: In the SAN theory, the backpropagation through oscillation hypothesis posits that prediction errors propagate backward through the cortical hierarchy via oscillatory phase resets. This paper’s findings align with this hypothesis, suggesting that global prediction errors, like those generated by oddball stimuli, initiate phase changes in higher-order areas (e.g., PFC). These phase resets then cascade downward through the cortical hierarchy, modifying oscillatory patterns in sensory areas. This supports the idea that predictive coding involves hierarchical feedback loops where higher-order predictions guide sensory processing.
2. Phase Wave Differentials as Biological Error Gradients
Paper Findings: The study reveals that global oddballs lead to sparse and weak responses in early sensory cortices, with a focus on higher-order areas for error detection. The responses resemble feedback more than feedforward processing.
SAN and Neural Array Projection Oscillation Tomography (NAPOT) Integration: This observation aligns with Self Aware Network's concept of phase wave differentials, which are proposed to act as biological equivalents of error gradients in artificial neural networks. In the context of NAPOT, phase wave differentials represent the oscillatory signals that encode the magnitude and direction of prediction errors. The study’s findings suggest that sensory processing might be modulated by these discrete phase shifts, providing localized corrections to oscillatory patterns based on top-down feedback from regions like the PFC. This aligns with the FOPC model, which proposes that prediction errors are encoded and propagated through phase relationships, dynamically adjusting neural circuits.
3. Fractal Oscillatory Predictive Coding (FOPC)
Paper Findings: The study's indication that predictive coding primarily occurs in higher-level areas before affecting sensory processing fits well with FOPC. It shows that feedback signals modulate sensory processing rather than an all-encompassing computation throughout the sensory cortex.
SAN and NAPOT Integration: Within the NAPOT framework, FOPC explains that different levels of the brain employ oscillations to generate, transmit, and adjust predictions. The paper's findings reinforce this view, suggesting that prediction errors are first processed in regions like the PFC and then feedback through oscillatory mechanisms to influence sensory cortices. The backpropagation through oscillation hypothesis becomes a fundamental part of this, as oscillations in higher-order regions initiate phase resets in lower-level sensory areas, facilitating error correction and prediction refinement across the cortical hierarchy.
4. Oscillatory Feedback and Attentional Selection
Paper Findings: The study observed that global oddballs engage prefrontal areas before influencing sensory cortices, hinting at top-down modulation.
SAN and NAPOT Integration: In the SAN framework, attention is closely linked to oscillatory dynamics, where top-down feedback generates "windows of opportunity" for processing specific sensory inputs. This paper suggests that the PFC plays a key role in modulating sensory processing through feedback oscillations. This is in line with FOPC's view of attention as an oscillatory process, where the timing of feedback signals selectively enhances sensory representations. Through NAPOT, this top-down modulation helps the brain align its internal models with incoming sensory information, refining its predictions and focusing cognitive resources on unexpected or salient stimuli.
5. Memory Consolidation and Oscillatory Pattern Completion
Paper Findings: Local oddball responses appear to induce adaptation effects, resembling processes involved in memory updating and consolidation.
Self Aware Networks (SAN) and NAPOT Integration: NAPOT posits that memory consolidation involves the replay and refinement of oscillatory patterns. During learning and memory consolidation, oscillatory phase relationships are adjusted to strengthen accurate predictions and correct errors. The study's findings align with this view, implying that predictive coding mechanisms in sensory areas adapt through oscillatory processes. This supports the idea that phase wave differentials serve as a feedback mechanism, influencing synaptic tuning and long-term memory formation within the oscillatory framework described in SAN.
6. Nested Oscillatory Loops and Predictive Loops
Paper Findings: The detection of prediction errors involves higher-level processing that shapes sensory responses, implying a bidirectional flow of information.
SAN and Neural Array Projection Oscillation Tomography (NAPOT) Integration: This bidirectional flow is fundamental to NAPOT, which suggests that neural arrays at different levels of the hierarchy engage in oscillatory feedback loops. The study supports the concept of nested predictive loops, where higher-order regions provide predictions that modulate sensory processing, and sensory feedback, in turn, influences higher-order processing. This nested architecture enables dynamic integration and adjustment of predictive models, resonating with the FOPC model that emphasizes a circular, multi-level interaction between predictions and sensory input through oscillatory communication.
7. Sparse Error Signaling and the Fractal Oscillatory Structure
Paper Findings: The sparse nature of global oddball responses in the sensory cortex aligns with the notion that not all sensory information is processed uniformly. Only certain signals—those that significantly misalign with top-down feedback—trigger error responses.
Self Aware Networks (SAN) and NAPOT Integration: This aligns with the fractal oscillatory structure proposed in SAN, where neural processing at different scales exhibits self-similar oscillatory patterns. Sparse error signaling is an efficient way to handle predictive processing across the cortical hierarchy. It implies that oscillations selectively encode prediction errors at different scales, contributing to a coherent integration of sensory information and expectations. The paper’s findings support the idea that sensory regions act as part of a larger fractal system, where phase wave differentials encode discrete error signals, influencing both immediate sensory predictions and broader cognitive functions.
The fractal nature of neural oscillations in the FOPC model explains how predictions and error signals can be encoded and propagated across different levels. This fractal structure enables the brain to simultaneously process fine-grained sensory details and broader contextual information, aligning with the paper's observations on local and global oddball processing.
8. Oscillatory Basis for Consciousness
Paper Findings: Higher-order areas like the PFC are implicated in processing global oddballs, suggesting that certain prediction errors become accessible to conscious awareness.
SAN and NAPOT Integration: SAN's hypothesis of an oscillatory basis for consciousness is reflected in this finding. The FOPC model proposes that conscious perception arises when predictive oscillations in higher-order areas achieve resource dominance and dominant phase coherence with sensory regions. This paper suggests that prediction errors reaching the PFC may perturb global oscillatory patterns, allowing them to enter conscious awareness. It aligns with the idea that only certain errors—those significant enough to disrupt coherent oscillations—achieve conscious processing, as proposed in SAN.
9. Phase-Dependent Synaptic Plasticity and Learning
Paper Findings: The modulation of sensory responses through feedback suggests that synaptic connections are dynamically adjusted based on prediction errors.
SAN and NAPOT Integration: Within the Neural Array Projection Oscillation Tomography (NAPOT) framework, phase wave differentials serve as signals for synaptic plasticity. The induction of synaptic changes depends on the phase alignment between oscillatory rhythms, allowing for nuanced learning rules that adapt to the ongoing oscillatory context. This paper's observation of adaptation effects aligns with the view that phase-dependent plasticity fine-tunes the network, reinforcing correct predictions and updating internal models in response to errors.
Fractal dynamics: Nature loves patterns that repeat at different scales. In the brain, similar activity patterns occur at multiple levels, from individual neurons to entire regions.
Future research should explore how fractal dynamics in neural networks contribute to the differentiation between local and global prediction errors. This could involve investigating how oscillatory patterns at different scales interact during oddball processing, potentially revealing new insights into the fractal nature of predictive coding in the brain.
The fractal concepts in Self Aware Networks (SAN) theory provide a unifying framework for understanding these findings, suggesting that predictive coding operates through self-similar patterns of neural activity across multiple scales. This fractal perspective helps reconcile the observed differences between local and global oddball processing, pointing towards a more nuanced understanding of how the brain generates and updates predictions.
1. Fractal Dynamics: Cooperation Between Scales
Fractals are structures where similar patterns repeat at different scales. This self-similarity allows both local (small-scale) interactions and global (large-scale) patterns to cooperate and influence each other. In the context of Self Aware Networks (SAN) theory, neural oscillations exhibit fractal properties that facilitate this cooperation between local and global neural processes.
The brain's oscillatory dynamics occur across various frequency bands (gamma, beta, alpha, theta, delta), each corresponding to different spatial and temporal scales. Local oscillations, such as high-frequency gamma waves, handle immediate sensory processing and fine-tuned neural computations. In contrast, global oscillations, like slower delta and theta waves, coordinate broader neural networks, integrating information across different brain regions.
The interplay between these oscillatory patterns can be thought of as a fractal system, where local dynamics contribute to and are influenced by global rhythms. For instance, a high-frequency local oscillation in a specific cortical region can modulate global theta waves, which in turn affect other cortical areas. This bidirectional influence supports a unified flow of information that shapes perception, memory, and consciousness.
2. Local Neural Processing: Micro-Scale Interactions
At the local level, individual neurons and small clusters (ensembles) engage in phasic bursts, transmitting sensory and mnemonic (memory-related) signals. Each neuron can be seen as a "micro-eye" or "microphone," detecting and transmitting phase changes based on its connections and morphology. This concept is part of Neural Array Projection Oscillation Tomography (NAPOT), which posits that neurons not only detect information but also project sensory representations to subsequent neural arrays.
These localized oscillations allow for high-fidelity processing of sensory inputs. However, their effects do not remain isolated. Through connections with other neurons and ensembles, the local activity influences—and is modulated by—global network oscillations. Essentially, local processing generates patterns that ripple outward, integrating with global brain dynamics.
3. Macro-Scale Coordination
Neural ensembles: Groups of neurons that work together like a team. These ensembles can represent specific information or coordinate complex behaviors.
On a larger scale, neural ensembles and entire brain regions participate in slower oscillations. These global oscillations provide a framework within which localized neural interactions can occur coherently. The global oscillatory activity, such as theta rhythms, acts as a "carrier wave," coordinating activities across distributed neural networks.
For example, during memory recall or sensory perception, slower theta or delta waves help synchronize distant brain regions, creating a coherent experience. These global rhythms can modulate the firing patterns of local neural clusters, thereby influencing attention, learning, and awareness. This cross-scale interaction enables the brain to integrate vast amounts of sensory information into a unified representation, essential for consciousness and perception.
4. Feedback Loops: Fractal-Like Cooperation in Self Aware Networks (SAN) Theory
SAN theory suggests that the cooperation between local and global scales involves dynamic feedback loops. The brain's neural networks operate in oscillating feedback loops, where each neural array can sense its state and that of neighboring arrays. This mutual sensing creates an entification process, forming the internal observer that experiences awareness.
These feedback loops resemble fractal structures, as they repeat similar patterns of interaction at different scales. Locally, feedback occurs within small neural clusters, while globally, it manifests as larger-scale oscillatory coordination between brain regions. For example, when a sensory input is processed, local neural activity generates high-frequency bursts, which then synchronize with global oscillatory patterns, creating a feedback mechanism that integrates the input into the overall cognitive state.
5. Impact on Consciousness and Awareness
The cooperative dynamics between local and global processes across different scales might explain the brain's ability to maintain a unified consciousness despite the complexity and diversity of sensory experiences. The fractal nature of these dynamics allows the brain to bind together different sensory modalities, memories, and thoughts into a cohesive whole. The local-global cooperation facilitates:
Attention: Localized processing of specific stimuli is modulated by global oscillatory patterns, guiding attention and awareness.
Memory Integration: Global rhythms like theta waves coordinate the integration of local mnemonic patterns, allowing for the recall and modification of memories based on current inputs.
Perceptual Coherence: Fractal-like interactions across scales ensure that sensory perceptions are unified, enabling the brain to generate an internal model of reality.
6. Theoretical Implications for AGI and Neural Networks
The cooperation between local and global processes modeled as fractal interactions could also inform the development of AGI. In artificial neural networks, implementing multi-scale oscillatory dynamics might enable AGI to process information in a more human-like manner, achieving a balance between local computations (e.g., specific tasks) and global integration (e.g., overarching goals or context).
Article simultaneously published on github here where you can talk to Self Aware Networks GPT (link on the welcome page of the selfawarenetworks repo) https://github.com/v5ma/selfawarenetworks/blob/main/04san.md