# The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg

Birgitta Dresp, Luiz Pessoa

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Special Issue Reprint

# The Resonant Brain

A Themed Issue Dedicated to
Professor Stephen Grossberg

Edited by
Birgitta Dresp-Langley and Luiz Pessoa

mdpi.com/journal/information
The Resonant Brain: A Themed
Issue Dedicated to Professor
Stephen Grossberg
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# The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg

Guest Editors

Birgitta Dresp-Langley

Luiz Pessoa
## Guest Editors

Birgitta Dresp-Langley

Centre National de la

Recherche Scientifique

UMR 7357 CNRS

Université de Strasbourg

Strasbourg

France

Luiz Pessoa

Department of Psychology

University of Maryland

College Park

United States

## Editorial Office

MDPI AG

Grosspeteranlage 5

4052 Basel, Switzerland

This is a reprint of the Special Issue, published open access by the journal *Information* (ISSN 2078-2489), freely accessible at: www.mdpi.com/journal/information/special_issues/The_Resonant_Brain.

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name Year, Volume Number, Page Range*.

ISBN 978-3-7258-3738-0 (Hbk)

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© 2025 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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About the Editors

Birgitta Dresp-Langley

Prof. Dr. Birgitta Dresp-Langley holds a Ph.D. in Cognitive Science from Paris Descartes University. She currently is a Full CNRS Research Professor/Directeur de Recherche CNRS Titulaire at the Centre National de la Recherche Scientifique in France. Her research interests are centered on perceptual processes, cognition, and the philosophy of Artificial Intelligence (AI).

Luiz Pessoa

Prof. Dr. Pessoa is a Full Professor of Psychology at the University of Maryland and a Director of the Maryland Neuroimaging Center. His research interests are centered on behavioral and functional MRI methods used to study cognition and emotion, with an emphasis on the interactions between cognitive and emotional brain systems.
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Editorial

# Editorial to the Special Issue “The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg”

Birgitta Dresp-Langley <fnref n="1" />,* and Luiz Pessoa <fnref n="2" />

This Special Issue offers a collection of research and model approaches to fundamental principles, mechanisms, and model architectures closely linked to the conceptual foundations of contemporary neural network research laid down by Stephen Grossberg [1] and his colleagues. Their pioneering conceptual work and models have since inspired and been explored further in state-of-the-art interdisciplinary research [2–7]. Analyzing and explaining brain mechanisms and data is considered a key to understanding cognition, with the overarching goal of explaining how the human mind arises from cooperative and competitive mechanisms through the step-by-step integration of information into brain representations. Information may be defined, in the context of this topical issue, in terms of either external physical information or internally encoded signals and emotions. Integration is achieved by mechanisms that may be pre-conscious, sub-conscious, and, ultimately, fully conscious [7,8], achieving cognitive representations across hierarchically organized layers of brain processing. The articles in this collection are directly relevant to topics ranging from the quest for explainable and predictive Artificial Intelligence (AI) [9] to the conscious representation of complex visual configurations in variable contexts [10] by the human brain. A unified understanding of how our brains see [11], hear [12], and feel [5,6] in order to learn about and know the world and to effectively interact with it clarifies how autonomous adaptive intelligence is achieved [13]. The research collected in this Special Issue is mostly concerned with mechanistic approaches to adaptive behavior. These can help solve large-scale problems in machine learning, AI, and robotics [14]. Brains embody universal developmental codes and laws found in all living cells and synapses [15], from the most primitive to the most advanced. These laws govern networks of interacting cells to support development and learning in all species, from mollusks to humans. Brain evolution across species has culminated in humans’ unique ability to study their own minds and to understand how minds derive meaning from an inherently ambiguous world—a skill that helps us adapt to changing environments. Despite the general advocacy for developing AI systems, significant limitations exist. A major one is the missing conceptual link between cognitive neuroscience and AI. While breakthroughs in AI often are claimed to be inspired by cognitive neuroscience, this is, however, most often not the case. Leveraging the most recent advances in cognitive neuroscience to emulate technology for novel solutions in machine learning and AI was a major part of Grossberg’s work, despite an inevitable contradiction: the pursuits of cognitive neuroscience and AI are, by nature, distinct. Cognitive neuroscience aims at clarifying mechanisms of cognition in living brains by studying neural activities linked to specific mental and behavioral states [16]. AI, on the other hand, is

Received: 7 March 2025

Accepted: 12 March 2025

Published: 16 March 2025

**Citation:** Dresp-Langley, B.; Pessoa, L. Editorial to the Special Issue “The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg”. *Information* **2025**, *16*, 234. https://doi.org/10.3390/info16030234

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Centre National de la Recherche Scientifique (CNRS) UMR7357, CNRS-Université de Strasbourg, F-67081 Strasbourg, France</footnote>

<footnote n="2">² Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA; pessoa@umd.edu</footnote>

<footnote>* Correspondence: birgitta.dresp@cnrs.fr</footnote>
a black box approach aimed at predicting outcomes on the basis of algorithms that most
often do not even remotely take into account the workings of the biological brain [17].

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Grossberg, S. *Conscious Mind, Resonant Brain: How Each Brain Makesa Mind*, onlineed; OxfordAcademic: NewYork, NY, USA, 2021.
   [CrossRef]

2. Kellman, P.J.; Shipley, T.F. A theory of visual interpolation in object perception. *Cogn. Psychol.* **1991**, *23*, 141–221. [CrossRef] [PubMed]

3. Groberg, S.; Williamson, J.R. A neural model of how horizon talandinter laminar connections of visual cortex develop into adult circuits that carryout perceptual groupings and learning. *Cereb. Cortex* **2001**, *11*, 37–58. [CrossRef] [PubMed]

4. Pilly, P.K.; Grossberg, S. How do spatial learning and memory occur in the brain? Coordinated learning of entorhinal grid cell sand hippocampal place cells. *J. Cogn. Neurosci.* **2012**, *24*, 1031–1054. [CrossRef] [PubMed]

5. Pessoa, L. Précis on The Cognitive-Emotional Brain. *Behav. Brain Sci.* **2015**, *38*, e71. [CrossRef] [PubMed]

6. Damasio, A.R. *The Feeling of What Happens: Body and Emotion in the Making of Consciousness*; Houghton Mifflin Harcourt: Boston, MA, USA, 1999.

7. Dresp-Langley, B. Why the brain knows more than we do: Non-conscious representations and the irrole in the construction of conscious experience. *Brain Sci.* **2011**, *2*, 1–21. [CrossRef] [PubMed]

8. Singer, W. Consciousness and the structure of neuronal representations. *Philos. Trans. R. Soc. B* **1998**, *353*, 1829–1840.

9. Dresp-Langley, B. The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience. *Information* **2023**, *14*, 82.
   [CrossRef]

10. Pinna, B.; Škilters, J.; Porcheddu, D. Accentuation as a Mechanism of Visual Illusions: Insights from Adaptive Resonance Theory(ART). *Information* **2025**, *16*, 172. [CrossRef]

11. Ptito, M.; Bleau, M. Bouskila. The Retina: A Window into the Brain. *Cells* **2021**, *10*, 3269. [CrossRef] [PubMed]

12. Reeves, A. The Psychometric Function for Focusing Attentionon Pitch. *Information* **2023**, *14*, 279. [CrossRef]

13. Sternberg, R.J. A theory of Adaptive Intelligence and Its Relationto General Intelligence. *J. Intell.* **2019**, *7*, 23. [CrossRef] [PubMed]

14. Sandini, G.; Sciutti, A.; Morasso, P. Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents. *Front. Comput. Neurosci.* **2024**, *18*, 1349408. [CrossRef] [PubMed]

15. Leisman, G.; Koch, P. Resonating with the World: Thinking Critically about Brain Criticality in Consciousness and Cognition. *Information* **2024**, *15*, 284. [CrossRef]

16. Churchland, P.S.; Sejnowski, T.J. *The Computational Brain*; MIT Press: Cambridge, MA, USA, 2016.

17. Goh, W.W.B.; Kabir, M.N.; Yoo, S.; Wong, L. Ten quick tips for ensuring machine learning model validity. *PLoS Comput. Biol.* **2024**, *20*, e1012402. [CrossRef] [PubMed]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Article

# Resonating with the World: Thinking Critically about Brain Criticality in Consciousness and Cognition

Gerry Leisman <fnref n="1" />,<fnref n="2" />,* and Paul Koch †

**Abstract:** Aim: Biofields combine many physiological levels, both spatially and temporally. These biofields reflect naturally resonant forms of synaptic energy reflected in growing and spreading waves of brain activity. This study aims to theoretically understand better how resonant continuum waves may be reflective of consciousness, cognition, memory, and thought. Background: The metabolic processes that maintain animal cellular and physiological functions are enhanced by physiological coherence. Internal biological-system coordination and sensitivity to particular stimuli and signal frequencies are two aspects of coherent physiology. There exists significant support for the notion that exogenous biologically and non-biologically generated energy entrains human physiological systems. All living things have resonant frequencies that are either comparable or coherent; therefore, eventually, all species will have a shared resonance. An organism's biofield activity and resonance are what support its life and allow it to react to stimuli. Methods: As the naturally resonant forms of synaptic energy grow and spread waves of brain activity, the temporal and spatial frequency of the waves are effectively regulated by a time delay (T) in inter-layer signals in a layered structure that mimics the structure of the mammalian cortex. From ubiquitous noise, two different types of waves can arise as a function of T. One is coherent, and as T rises, so does its resonant spatial frequency. Results: Continued growth eventually causes both the wavelength and the temporal frequency to abruptly increase. Two waves expand simultaneously and randomly interfere in an area of T values as a result. Conclusion: We suggest that because of this extraordinary dualism, which has its roots in the phase relationships of amplified waves, coherent waves are essential for memory retrieval, whereas random waves represent original cognition.

**Keywords:** resonance; brain; consciousness; criticality; entrainment; continuum theory; biofield

## 1. Introduction

### 1.1. Resonating with the World

The present study aims to integrate consciousness and cognition as a function of resonance, defined as an effect that is amplified when two systems are in harmony. A tuning fork set to 440 Hz, or tuned to concert pitch "A", as is currently done in symphony orchestras, would have a second fork vibrate in sympathy with or in phase with it if one were to tap it. If we were to silence the first tuning fork, we would still be able to hear the vibrations coming from the fork that we did not touch. We might say that the second fork is experiencing an induced sympathetic resonant vibration or frequency. Both forks are tuned to the exact same frequency. We are aware of instances where people have broken glass with just their voices. Whoever wants to try it should lightly tap the glass with a nail or finger and then listen for the glass's natural resonance or pitch. The next step would be to sing a long note in the person's voice. Until the glass is broken, the

**Citation:** Leisman, G.; Koch, P. Resonating with the World: Thinking Critically about Brain Criticality in Consciousness and Cognition. *Information* **2024**, *15*, 284. https://doi.org/10.3390/info15050284

Academic Editor: Luis Martínez López

Received: 26 February 2024

Revised: 13 April 2024

Accepted: 15 May 2024

Published: 17 May 2024

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**Copyright:** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Movement and Cognition Laboratory, Department of Physical Therapy, University of Haifa, Haifa 3498838, Israel</footnote>

<footnote n="2">² Resonance Therapeutics Laboratory, Department of Neurology, University of the Medical Sciences, Havana 11600, Cuba</footnote>

<footnote>* Correspondence: g.leisman@alumni.manchester.ac.uk; Tel.: +972-52-420-5643</footnote>

<footnote>† Deceased.</footnote>
vibration's amplitude will increase steadily. Alternatively, a suspension bridge In Tacoma, Washington, built with steel and concrete, fell in 1940. On November 1 of that year, the bridge experienced a modest, seemingly negligible vibration along with a constant wind of about 45 mph. Unfortunately, some areas of the bridge's resonant frequency coincided with the vibration's frequency. The vibration increased until it reached a destructive resonance frequency, which caused the bridge to collapse. There is a video of the event available [1].

A big steel and concrete bridge was demolished by resonance in one instance, while a glass was broken in another. In light of this, we may destroy a minuscule object, perhaps a living bacterium. We might be able to modify the biological liquid crystal using a unique electronic signal, but this would require a device of some sort. A tool designed to cause a resonant vibration in a cell or other living entity was patented by James Bare [2–4]. For instance, a 100 Hz input would cause the resonating cell to output 100 pulses per second, similarly for 200 Hz, and so forth. Once the matching frequency is found that will entrain with the input frequency, we can start looking for the “magic frequency” 1 Hz at a time. One high and one low input frequency are required, with the higher frequency being 11 times greater than the lower or the 11th harmonic [5], where microorganisms start to break apart. The application matters a lot. Cancer cells of a certain type are sensitive to frequencies between 100,000 and 300,000 Hz [6,7].

We have long wondered how a biologically based machine could be built with billions of neurons that could outperform several of the most sophisticated computers. The brain has significant processing capability and nearly infinitely expandable storage [8–10]. The idea that the brain may tune itself to a level where it can be excitable without disorder in a manner analogous to a phase transition has recently been supported. Our neurological systems have a propensity to maintain a balance between rest and chaos. Information processing is optimized when there is a state of flux between quiescence and chaos [11]. This brings up the notion of criticality in the context of the resonance of living entities.

## 1.2. Schumann Resonance and Living

The “Schumann Resonance”, often referred to as the “Earth’s heartbeat” Refs. [12–14], is a naturally occurring electromagnetic resonance frequency in the Earth’s electromagnetic field. Winfried Otto Schumann predicted it mathematically in 1952. The Schumann Resonance occurs due to the space between the Earth’s surface and the ionosphere acting as a resonant cavity for electromagnetic waves.

The primary frequency of the Schumann Resonance is around 7.83 Hz Ref. [12], though it can vary slightly. It is influenced by various factors such as solar activity, lightning activity, and ionospheric conditions. Some people believe that these resonances can have effects on human health and consciousness, although scientific research in this area is ongoing and often controversial [12,13]. Some propose that these resonances may have an impact on our circadian rhythms [15] and overall well-being [13], but conclusive evidence is still lacking here as well.

The idea of resonance in relation to consciousness often refers to theories proposing that certain frequencies or patterns of electromagnetic activity in the brain might correlate with particular states of consciousness. For instance, some research suggests that brain wave frequencies, such as those associated with alpha, beta, delta, and theta waves, may correspond to different cognitive states, such as relaxation, alertness, deep sleep, or meditative states [16].

Some proponents of consciousness theories suggest that external electromagnetic fields, including the Schumann Resonance or other natural and artificial electromagnetic fields, could potentially influence or resonate with the brain's electromagnetic activity, leading to alterations in consciousness or cognitive states [17,18]. However, it is important to note that these ideas often remain speculative and are subject to ongoing scientific investigation and debate.

While there is interest in exploring the potential links between electromagnetic phenomena and consciousness, conclusive evidence for direct causation or significant influence
remains elusive. Further interdisciplinary research involving neuroscience, physics, and psychology is needed to better understand the complex relationship between electromagnetic fields and consciousness. We will, however, attempt to integrate what is known about resonance related to both consciousness and cognition.

## 1.3. Resonance in Cognitive-Motor Interaction

Resonance in human motor function refers to the synchronization or amplification of movement patterns in response to external stimuli or internal processes. This concept is often studied in the context of biomechanics, neuroscience, and motor control to understand how the nervous system coordinates and modulates movements.

One example of resonance in motor function is the phenomenon of entrainment, where rhythmic external stimuli, such as music or a metronome, can synchronize and influence the timing and rhythm of movements. This is commonly observed in activities like dancing, where individuals coordinate their movements to the beat of the music.

Internal processes, such as emotions or intentions, can also influence motor resonance. For instance, when observing someone performing a specific action, mirror neurons in the observer's brain may fire, leading to a mirroring or resonance of the observed movement within the observer's own motor system. This phenomenon is thought to play a role in empathy, imitation, and social interaction.

Resonance can also occur within the motor system itself. When certain movement patterns or muscles are activated, there can be a tendency for nearby muscles or movement patterns to resonate or become activated as well. This can be advantageous for efficiency in movement execution but can also contribute to unintended movements or muscle co-activation in some cases.

Overall, resonance in human motor function involves the synchronization, amplification, or modulation of movement patterns in response to various internal and external factors. Understanding these mechanisms is crucial for optimizing movement control, rehabilitation strategies, and enhancing motor performance, but most importantly for cognitive function [19].

## 1.4. Criticality and Consciousness

The relationship between criticality and consciousness is a complex and multifaceted topic that spans various disciplines, including philosophy, psychology, neuroscience, and even physics. Briefly, *criticality* in physics refers to the point at which a system undergoes a phase transition, exhibiting properties that are neither completely ordered nor completely disordered. Criticality often refers to a state of a system where it operates at a point of instability or sensitivity, often at the boundary between order and chaos. Systems at criticality often display scale invariance, where patterns repeat across different scales. In neuroscience, criticality is a concept that describes the state of neuronal networks when they are balanced between order and disorder. It suggests that the brain operates most efficiently by optimizing information processing and is most adaptable when it is in a state of criticality.

In bringing these two concepts together, one area of interest is in exploring whether criticality in neural networks is somehow related to or necessary for consciousness. Some researchers hypothesize that consciousness may emerge from the dynamic patterns of neural activity that occur at the critical point [20]. In this view, criticality could be a fundamental property of neural systems that gives rise to conscious experience. However, this is still a highly debated and speculative area of research.

Furthermore, criticality and consciousness intersect in discussions about the nature of complex systems, emergence, and the relationship between physical processes and subjective experience. Understanding how criticality in neural networks or other complex systems might contribute to or correlate with consciousness could potentially shed light on the mysteries of the mind. However, it is essential to approach these topics with caution
and rigorous scientific inquiry due to their complexity and the many unanswered questions
surrounding them.

The relationship between criticality and consciousness lies in the idea that the brain, as a complex system, may operate near criticality to facilitate flexible and adaptive cognitive processes, including those associated with consciousness. Some researchers [21] propose that critical brain dynamics may be necessary for the emergence of conscious experiences, as they allow for the integration of information across different brain regions and the generation of complex patterns of activity.

However, the precise mechanisms by which criticality and consciousness are related
are still not fully understood and remain the subject of ongoing research and debate. It is
an exciting area of inquiry that continues to push the boundaries of our understanding of
the brain and the mind.

1.5. *Criticality in Consciousness and Cognition*

Consciousness and cognition are fascinating aspects of human existence that ought to be broken down. Consciousness, for our purposes, refers to the subjective awareness of ourselves and the world around us [22]. It involves our thoughts, perceptions, feelings, and experiences. Consciousness refers to the state or quality of being aware of and able to perceive one's surroundings. It involves a range of cognitive processes, including attention, perception, memory, and self-awareness. Despite decades of research, the nature of consciousness remains one of the most challenging problems in science and philosophy. There are various theories about consciousness, including the integrated information theory [16], the global workspace theory [16], and panpsychism [16], each offering different perspectives on how consciousness arises and functions. A more in-depth discussion is beyond the scope of the present paper.

Consciousness is often described as the “hard problem” in philosophy because it
is challenging to understand how subjective experiences arise from physical processes
in the brain. Cognition refers to the mental processes involved in acquiring, processing,
storing, and using information. It encompasses a wide range of mental activities, including
perception, attention, memory, language, reasoning, and problem solving.

These two concepts are deeply intertwined. Consciousness is closely related to cogni-
tion because our conscious experiences are shaped by our cognitive processes, and vice
versa. For example, our perception of the world around us is influenced by our cognitive
abilities to interpret sensory information, and our conscious thoughts can influence our
cognitive processes like attention and memory. Researchers in neuroscience, psychology,
philosophy, and other fields continue to explore the relationship between consciousness
and cognition, seeking to understand how they interact and how they emerge from the
underlying biological mechanisms of the brain.

Criticality in cognition and consciousness refers to the idea that these processes exhibit a state of dynamic balance between order and chaos, where they operate at an optimal point between too much rigidity and too much randomness.

In cognitive science, criticality suggests that the brain operates near a phase transition
point where it can quickly adapt to changing circumstances and efficiently process informa-
tion. This state is often associated with complex behaviors, learning, and creativity. It is
like the brain is finely tuned to the edge of chaos, where it can both maintain stability and
exhibit flexibility.

Similarly, in consciousness studies, criticality implies that consciousness emerges from
the collective activity of neurons operating at this critical state. This perspective suggests
that consciousness is not a binary phenomenon but rather a continuous spectrum that
emerges from complex interactions within the brain.

Understanding criticality in cognition and consciousness is crucial for unraveling the
mysteries of how our minds work and how consciousness arises from neural processes. It
provides insights into phenomena such as learning, memory, decision making, and even
disorders like epilepsy or schizophrenia, where deviations from criticality may occur.
## 2. Resonating with the Brain in Consciousness and Cognition

### 2.1. *Thinking Critically about Brain Criticality in the Context of Resonance*

Complex systems that are on the verge of a phase change between randomness and order are said to be in a critical state. There is a unique improvement in information-processing abilities in this kind of system, and we might even speculate that the brain may play a key role in it. Criticality, computation, and cognitive processes are beginning to show linkages. We can better grasp the nature of cognition and neural processing by comprehending the concept of criticality.

Neurons that fire simultaneously link together, according to Hebb [23]. We now understand that neurons seek a key set point and regime when they functionally join with others [11]. Ma and colleagues have backed the idea that criticality is subject to active regulation. Criticality is mediated by inhibitory neurons in both Ma and colleagues' models and in our own [24–26]. Larger brain networks seem to be regulated by these neurons. According to Koch and Leisman [25], the function of criticality is to develop a computational model that may be used to optimize information processing, including the storing and transmission of intricate sensory patterns and parts of memory.

Power laws serve as the foundation for the majority of measurements in clinical neurophysiology and psychophysics. These power laws are used to separate background activity from noise using a variety of methodologies, such as the Fast Fourier Transform, signal averaging techniques, coherence, dipole source localization, and Hilbert–Huang transforms, among others. The exponent relation is a good substitute for direct use in information processing. We might want to look into biofield physiology as a way of explaining the process of living to better comprehend the relationship between criticality, resonance, consciousness, “awakeness”, awareness, and integrated brain function.

### 2.2. *Critical Biofield Systems and Information*

Biological systems generate a variety of fields that are only partially diffused so that they can more effectively self-regulate and organize their physiology. This is done to make physiological organization more efficient. Alterations to physiological regulatory systems can be brought about by biofields in a manner that is comparable to alterations brought about by molecular control systems. Electroencephalograms (EEGs) and magnetoencephalograms (MEGs) are two examples of the types of biofields that can cause changes in the synchronization of the brain's electrical activity (EEG).

It has been demonstrated that biofields are made up of forces that are dispersed across a broad region and can encode information [27,28]. These pressures, which also affect physiology, can control and affect the physiology of tissues and cells. As a complementary function of physiological coherence for metabolic processes, biofields coordinate the integration of several different levels of physiological activity, both temporally and spatially [29]. This is done as a result of a biofield's ability to maintain physiological coherence. When referring to the qualities that constitute a living creature, the terms “biofield activity” and “resonance” can be used interchangeably in this context because they mean the same thing.

Analyzing the functional connection while at rest using MRI helps us understand the brain's functioning architecture better. The technique depends on slow correlations in the blood oxygen level-dependent signal (BOLD) (e.g., 0.01–0.1 Hz) in the brain. These gradual correlation patterns have been utilized to identify functional networks and to explain how they grow, alter with age, differ between people, and get disrupted in disease [30–36]. Though the fundamental mechanisms remain unclear, it is thought that delayed BOLD fluctuations and their associations represent brain processes [37,38].

Two distinct types of often-seen dynamics in the brain may be related to two separate underlying systems or processes. The pacemaker may affect dynamics that show a narrow band-limited power [39]. For example, the dominant occipitally recorded EEG alpha rhythm during restful wakefulness may be generated by an alpha pacemaker. A particular subset of thalamocortical neurons with inherent rhythmic bursting at alpha frequencies and gap junction couplings make up alpha pacemakers [40]. Even though this model of
oscillating resting-state activity is well supported, e.g., [39,41], the most commonly recog-
nized hypothesis in the field holds that brain activity propagates within an anatomically
limited small-world network to produce correlations [42,43]. Scale-free dynamics, or 1/f
dynamics, are predicted by this model [44–46]. Large events are rare, whereas little events
are frequent in 1/f dynamics because of the inverse relationship between event amplitude
and frequency. More specifically, as frequency is increased, even by a small exponent,
power can change inversely (Pα1/fβ, with exponents ranging from 0 to 3) [47]. Though
they can also happen in other noncritical systems, complex dynamic systems possess 1/f
dynamics when operating at a critical point, when ordered and disordered phases of the
system are balanced [48,49]. The 1/f features of many neural signals, including local field
potentials, have supported the development of brain activity models critically operating by
self-organizing properties [50–52].

A high level of sensitivity to particular stimuli, and by extension, particular signal
frequencies, is necessary for coherent physiology. This sensitivity is required, among
other things, for the effective coordination and integration of internal biological systems.
Coherent physiology requires not just the efficient coordination and integration of the
body's internal biological systems, but also a significant sensitivity to particular stimuli
and, as a result of this, particular signal frequencies. Exogenous energy that is generated
either biologically or non-biologically appears to be vulnerable to and entrains human
physiological systems, according to a significant body of research in this area. During
physiological activity, it is possible to collect measurements of electrical and magnetic
fields; however, this may lead to confusion, since metabolic processes or the outcomes of
physiological action are not completely known [30,31].

It is recognized that there will eventually be shared resonance, both within and
between individual species, because all living things resonate at comparable or coherent
frequencies. In the neurosciences, resonance theory has long been noted. Crick and
Koch [32], Fries [33,34], and Koch [35] have studied the concept of resonance. According
to Fries, a critical component of the nervous system's operation is brain synchronization,
resonance, or "communication through coherence".

2.3. *Consciousness as a Function of Resonance*

Resonance is thought by many theorists to contribute to conscious states. According to Dehaene's global workspace theory, integrated conscious activity is the outcome of long-distance connections between synchronous and resonant cortical areas [35,36]. By claiming that while consciousness, which is associated with being awake and aware, is a function of resonance, it is not always a property of all resonant states, Grossberg [37] extended the idea of brain-communication-based resonance. Phase changes in resonance were also considered vital for understanding brain function by Freeman and Vitiello [38]. When Pockett proposed an electromagnetic field theory based on the synchronization from movement feedback when differentiating between conscious and non-conscious fields, she raised the significance of resonance in understanding integrated brain activity [39,40]. Others have developed further resonance-based theories of brain integration, such as Bandyopadhyay and colleagues' [41] Fractal Information Theory [42–44]. As a result, albeit still in theory and without practical application, resonance's importance in comprehending brain communication systems has begun to gain some traction.

The concept of resonance is intimately connected to awareness, inter-regional connec-
tions or disconnections in the brain, and its integrative function. It can be used to describe
synchrony, vibration, or harmony more broadly. Similar resonance patterns can be found in
the brain's synchronized electrical cycles. Resonance's significance in fostering integrated
brain activity must also be properly understood in the context of death.

3. Resonating with Life (and Death): Gaps in Current Understanding

One of the numerous hypotheses that try to explain life and living is the assumption
that life function comes from organized computer-like activity in the brain's neural net-
works that are connected with mental states. Another notion is that the temporal binding of information in these networks is associated with synchronous oscillations between the cortex and thalamus. How we consciously think may be impacted by the complexity of neural computing.

Death itself is not noteworthy until a clinical definition is required. However, it is more important to comprehend the mechanisms at play during a lack of consciousness. Although death seems to us to be a real event and an undeniable state, a corpse, whether human or otherwise, continues its life functions even after it has been proven to be dead. There is minimal disagreement that the person is dead despite a few isolated cells, nests, or cell subnetworks that continue to function.

As we continue, the argument becomes more challenging, since, despite the brain having “died”, peripheral bodily organs like the heart, lungs, liver, and others still function due to modern developments. In the past, we had thought that the body perished when the entire brain expired, and consequently, physical functions ceased. Nevertheless, the development of modern technology has made it difficult for us to determine the precise moment of death. The constituent components of conscious or micro-conscious entities need to combine to provide us with the vividness of the conscious experience, bringing us to the combination problem.

### 3.1. *The Combination Problem in General Resonance Theory*

Can components of lower-level micro-consciousness join together to produce higher-level macro-consciousnesses? According to the suggested explanation, distinct brain regions may undergo bandwidth phase shifts and changes in the speed of information transmission between regions through common resonance in the context of mammalian consciousness. More complicated variations of consciousness may form as a result of this phase transition, and the nature and content of those varieties will rely on the specific combination of component neurons that are present at any one time. To distinguish this perspective from emergent materialism, we can provide a more thorough understanding of the comparative development and categorization of consciousness and propose that in all physical processes, awareness appears as a continuum of increasing richness. This can be referred to as a metasynthesis, and it is also known as general resonance theory.

### 3.2. *Everything Has a Unique Resonance*

Everything in the universe is always changing and evolving. Certain frequencies cause even seemingly immovable things to vibrate, oscillate, or resonate. As a result, everything is genuinely oscillating, and coordinated oscillation between two states is what defines resonance. Different oscillating processes often start vibrating at the same frequency when their frequencies are close enough [45]. Sometimes they “sync” in unexpected ways that accelerate and enrich the flow of energy and information. By examining this occurrence, significant insights into the nature of consciousness—both to humans and other mammals, and on a more basic level—may be gained. Using a range of resonant examples from biology, physics, chemistry, and neuroscience, the authors of [35] aimed at describing the nature of synchrony in the experience of consciousness, and have included certain species of fireflies that start to flash their bioluminescent parts in unison when they are in large groups. Mammalian awareness and the ability of human brains to fire large numbers of neurons at specific frequencies are believed to be closely related to various forms of neural synchronization [32–36]. We always see the same face of the moon because its spin precisely matches its orbit around the planet. When photons with the same frequency and power are discharged simultaneously, a laser is produced. At this point, it can be helpful to consider how resonance and synchrony differ concerning our inquiries.

### 3.3. *What Is Resonance and Synchrony?*

Resonance or synchronization is the propensity for several processes to move in unison, or oscillate, at the same or at a similar frequency. Synchrony, also known as the harmonic
oscillator theory or complex network theory, is the study of how connected oscillators behave around one another.

The following two queries are raised by numerous instances of resonance: how do the constituent elements of resonant structures—a phrase that refers to any grouping of resonating components—interact with one another, and, once achieved, how do they reach resonance? Examining each question individually, we need to pay close attention to the two examples above by examining (1) the significant neural synchronization in mammalian brains and (2) the synchronization of flashes in fireflies.

The sort of communication between each resonant structure will vary depending on the scenario being studied, and in many resonance cases, clear conclusions are difficult to derive. Each firefly has access to visual signals, but it is also likely that it has access to olfactory, chemical, and perhaps electrical or magnetic stimuli. One of the two examples of intricate resonant structures we can utilize is fireflies. Based on empirical studies, it seems that a key factor in how firefly populations coordinate the synchronization of their bioluminescent flashing is through visual perception [45].

It is less clear how each neuron communicates with the others when neuronal synchronization occurs in the brain. Neural synchronization that happens too quickly, relying solely on electrochemical neuronal communication, is known as electrical field gamma synchrony; it requires electrical field signaling, according to Freeman's studies of the brains of rabbits and cats. As noted by Freeman and Vitiello [38], the beta and gamma bands of carrier waves often re-synchronize over large distances in minuscule time lags, according to high-temporal-resolution EEG readings, providing evidence for a variety of intermittent spatial patterns. Axodendritic synaptic transmission, the predominant mechanism for brain interactions, should cause the EEG oscillations to experience distance-dependent delays because of successive synaptic delays and restricted propagation velocities. The coherence of global brain gamma "synchrony" cannot be adequately explained by gap junction coupling alone, according to Craddock et al. [46]. However, what is currently known suggests strongly that shared resonance plays a critical role in the development of mammalian consciousness, including human consciousness.

### 3.4. *How Is Shared Resonance Attained in Resonating Structures?*

How do entities that are in communication with one another through resonance modify their resonance frequencies to reach resonance with one another? In many circumstances, entities that are initially unsynchronized manage to become entrained. What driving forces are behind these processes?

We may compare the mechanisms that enable firefly synchrony to conscious human activities in terms of how fireflies time their flashes. For instance, our brains send a series of neuronal pulses to our fingers when we wish to lift them, causing the desired action. Similar to fireflies, it is conceivable that when a fly decides to flash its lights, its brain sends an electrical pulse to its abdomen. At that point, there are chemical and physiological mechanisms supporting the fly's bioluminescence.

It might seem unusual to attribute intent to fireflies. Nonetheless, it would make sense that fireflies would be aware of and able to intentionally manage their physiological functions, especially those associated with large organs such as their light-producing organ. Their actions are intricate and show a variety of "behavioral correlates of consciousness". By comparing the different neurological and behavioral similarities between humans and other mammals, we may recognize that fireflies probably only have a basic level of conscious awareness without implying that they have anything close to the depth of human consciousness.

Based on the supposition that fireflies possess a primitive kind of consciousness, compared to human consciousness, we may provide a high-level explanation for how their flashes are synchronized without considering the intricacies of sub-level mechanisms. This makes an explanation relatively simple: the body obeys the will of the brain, in the same way that any conscious activity in a person, dog, cat, etc. would be explained. However, the
synchronized flashing of fireflies without intellect or consciousness can also be explained. Strogatz and his colleagues proposed that firefly synchrony can be explained by intrinsic biological oscillators that naturally synchronize with nearby additional oscillators [45].

It would be difficult to argue that individual neurons want to be in synchrony, but there is evidence for some form of neuronal consciousness, however basic in comparison to whole-brain consciousness. How is it that neurons synchronize swiftly and regularly? Although numerous indications point to different kinds of field effects, these are still unknown. How this communication occurs in each neuron, allowing them to quickly adjust their electrical cycles to meet rapidly changing macroscopic patterns, is still unknown.

Keppler [47] focused on phase changes observed in mammalian brains and the general "criticality" in which these brains appear to be, meaning that they are very sensitive to even minute changes. It is possible to think of the brain mechanisms underlying conscious activities as a complicated system that functions close to a key point of a phase transition. With appropriate stimulation, the brain can transition from a disordered phase with irregular dynamics and spontaneous activity to an ordered phase with long-range connectivities and stable attractors.

The idea of a "phase transition" could be significant when thinking about the combination problem and macro-consciousness. Phase transitions include the freezing of water into ice and the condensation of water vapor into droplets. Similar to brain states, neural states can oscillate in reaction to seemingly unimportant inputs. Dehaene [36] provides evidence in favor of the theory that mammalian consciousness depends on phase transitions.

Fries describes a trio of the cognitive frequencies of specific electrical brain wave combinations [33,34]. He notes that gamma-band synchronization carries out the attended stimuli's selective communication, beta-band synchronization mediates top-down attentional influences, and a 4 Hz theta rhythm resets the gamma band regularly. Because nature is made up of numerous lawful processes, each will resonate at different frequencies. Additionally, processes that are close to one another may eventually synchronize and resonate at the same frequency.

# 4. Criticality of Resonating Consciousness

## 4.1. *Interaction of Physical Systems and Consciousness*

Hunt [48] and Schooler and associates [49] proposed a set of psychophysical ideas that would clarify how consciousness and physical systems interact. They elaborate on why it is that resonance is essential for establishing macro-scale awareness by combining several micro-conscious entities at different organizational levels.

Without subscribing to panpsychism or panexperientialism [50–53], we can state that connected consciousness is significantly primitive in the vast bulk of matter, with maybe only an electron's or an atom's primitive humming of awareness. However, in certain kinds of groupings of matter, as in sophisticated biological life forms, awareness can grow notably richer in comparison to the bulk of matter [54,55].

All objects could be considered to be at least somewhat conscious based on the observable behavior of electrons, atoms, molecules, bacteria, paramecia, mice, bats, and rats. An emergentist perspective contends that consciousness appeared where it had not before existed and arose at a specific period in the history of each species that experiences consciousness. Panpsychism is the opposing position to this theory.

Panpsychism contends that rather than emerging, matter and the mind have always been connected, and vice versa (these two sides of the same coin are equally significant). However, the mind, which is constantly connected to all matter, is typically quite primitive. Only a very small amount of consciousness exists within an electron or an atom. But as matter becomes more complex, so too does the mind, and vice versa. Nevertheless, in this situation, complexity does not matter. Instead, it is the result of stronger internal and external connections that resonate. A mathematical version of the concept was developed by Hunt [50,54], emphasizing how resonant connections result in larger-scale consciously aware beings and how relevant characteristics may be characterized and measured.
Although we will not go into great detail here, (see [48,50,52,55], supported by others [49,51,53]), according to Tononi and Koch [53], within the context of the integrated information theory, elementary particles are either charged or not. As a result, a proton has one positive charge, an electron has one negative charge, and a photon, which is a carrier of light, has no charge at all. According to Tononi and Koch, charge is a feature that these particles have. A charge is just present in uncharged materials; it does not arise from them. They reason that consciousness is no different. Matter is arranged to give rise to consciousness. It is ingrained in the system's architecture. It is a characteristic of complicated things that cannot be boiled down to the operations of more basic attributes.

When two entities resonate at the same frequency, they bond or join together in a variety of ways. Such common resonance and binding may sometimes lead to the blending of different characteristics of cognitive, perceptual, and motor elements into a more cohesive whole, depending on the entities under discussion [9]. According to the quantum field theory, at the most basic level, matter is more akin to a standing wave of concentrated energy. Each of these waves is regional in both distribution (spatially) and time (temporally).

Since matter is fundamentally wave-like, it is easy to understand how information transference, and ultimately the emergence of macro-conscious forms, are associated with shared resonance. All physical processes are fueled by waves of various kinds, at least partially. Waves collaborate instead of competing when multiple entities resonate at the same frequency. This leads to significantly faster energy and information transfers between the components of whatever resonant structure we are observing, as well as a much wider bandwidth. Subsequently, the information flows merge into a larger harmonic, and the resonant wave shapes of the micro-conscious organism become coherent rather than occurring out of phase (decoherently).

Furthermore, Christof Koch [35] suggested that feedback pathways may play a crucial role in producing a type of standing wave or resonance in the brain network if the information is amplified and sustained in the brain by top-down attention. Beyond the degree of synchronization generated by sensory input alone, neurons can coordinate their spiking activity if they receive more local and widespread feedback. This amplifies their postsynaptic impact in comparison to when they activate independently. On this basis, a significant network of neurons may be developed that is capable of exerting an impact throughout the cortex and between lower brain structures. This would be the slow mode upon which conscious awareness rests, according to Koch.

Expanding on Koch's theories, Fries [33,34] proposed a "communication through coherence" theory. Fries [34] offered the following example of shared resonance concerning neural resonance/coherence: Inputs happen at various times during the excitability cycle due to a lack of coherence, and their effective connection will be consequently reduced. Nonetheless, inputs will spread faster and over a wider bandwidth if they fall within the same excitability cycle.

Through a variety of biophysical information pathways, organisms gain from faster information sharing. More macro-scale levels of consciousness can emerge thanks to these speedier and richer information flows than would be feasible in similar-scale structures like sand heaps or rocks because biological structures are more active and more interconnected than rocks or sand. However, the kind of interconnectivity that is required has to be predicated on resonance processes, which usually cause a phase shift in the information flow rate as a result of the conversion of incoherent structures into coherent ones.

We know that gamma synchrony being a main neurological correlate of consciousness in human brains is a notion supported by Dehaene [36]. He was particularly interested in certain "signatures of consciousness", such as late-onset gamma synchrony [56]. However, a combination of gamma and lower harmonic frequencies is frequently linked to mammalian consciousness [34]. This common resonance creates an electromagnetic field that, through certain neuronal electrochemical firing patterns, might act as the source of macro-conscious information [39,57-60]. Gamma synchrony is used as an example of how more neurons get
incorporated into the common gamma synchrony, which Hameroff [56] refers to as “the conscious pilot”. This is backed up by other slower-frequency waves [34]. However, when the shared gamma synchrony recedes from specific neurons, it allows those neurons to return to their previous resonance state, resulting in a more localized pattern.

To entrain additional neurons, this moving large-scale wave absorbs them into a semi-
stable gamma wave pattern. This allows the pace of information interchange to shift from
being mostly electrochemical to electromagnetic and allows the capacity for information
processing of the several micro-conscious entities made up by those neuron clusters to
be combined into a macro-conscious entity. The larger harmonic is entrained with the
smaller-scale harmonics through this process; hence, all of the constituents' "windows"
are opened to one another, facilitating more fluid information transmission. The pace of
change of macro-consciousness is precisely the same as that of its constituent neurons and
associated fields. Figure 1 shows an example of such a procedure.

<figure><img src="image_6.png" /></figure>

**Figure 1.** Division of human gamma-band activity in response to a mild visual stimulus into components associated with awareness (A) and attention (B). When attention is focused on the stimuli, there is strong gamma-band activity 200–250 ms after the stimulus initially appears. This is noteworthy because gamma-band activity associated with putative stimulus awareness started 350 ms post stimulus onset, and this was followed by gamma activity in a higher-frequency zone associated with stimulus-driven selective attention. Given this, while it may not be a reliable indicator of conscious experience, the probability of being aware of the stimuli appears to be correlated with local gamma-band activity in sensory cortical areas starting 200–250 ms after stimulus initiation. Regardless of whether the stimulus was attended to or not, it evoked mid-frequency activity in the gamma range (between 54 and 64 Hz) with a latency of approximately 240 milliseconds post stimulus (left panel). A 350 ms lag was seen in high-frequency gamma-band activity (approximately 76–90 Hz) if spatial (selective) attention was directed toward the stimulus (right panel). The three stars in each panel stand for the effects in the framed regions that are of utmost importance. Either the awareness effects in the regions included in brackets receive attention-related effects, or the awareness effects in the left panel receive attention-related effects (right panel). [Modified from Wyart and Tallon-Baudry [61]; Figure 3C is theirs].

Whitehead [62] indicated that the many become one and are augmented by one. In other words, lower-level entities are employed to create a new, higher-level entity; however, the lower-level entities are not eliminated during the binding process. Rather, they are bound into the new entity, hence increasing the number of entities by one. The many increase one by one until they achieve unity. Numerous subsidiary micro-conscious entities are included when the “conscious pilot” circumnavigates the brain; these numerous aspects of consciousness merge into a single dominating consciousness, leaving the subsidiary consciousnesses alone.

4.2. *Connecting Biofield Resonance and Consciousness*

Numerous natural phenomena, especially biological ones, appear to critically exist
on the cusp of order and chaos [63–67]. It has been demonstrated that criticality-tuned
models display brain-like dynamics [68,69], which has given rise to the Critical Brain Hypothesis [70–72]. A variety of helpful informational characteristics are displayed by systems that are tuned to criticality, facilitating the effective dissemination of and receptivity to information [73–76]. For instance, using data from the human connectome, Marinazzo and colleagues showed that information is maximized in an Ising scheme [77,78] at the critical temperature and that, beyond criticality, a law of marginal decreasing returns is attained [79]. These concepts have been expanded upon to more widely imply that critical systems are advantageous to evolution, since they are better at responding to their surroundings and guaranteeing their survival [80,81]. The Ising model is simple and applicable to numerous systems, including the integrated information theory (IIT).

Consciousness can be defined in the context of IIT either by top-down processing or phenomenologically [82–84]. Integrated information measures how processes inside a system interact and restrict one another in emergent, non-reducible ways. This enables us to quantify the characteristics of a system that its parts are unable to describe. For a system to be both highly connected and informative, it must find a balance between segregating forces that serve to distinguish the system and integrating forces that provide new types of information that are not nonexistent within the individual components. These precise characteristics are predicted to appear in Ising models at the beginning of a phase transition, close to the critical temperature.

It should be noted that there is nothing novel about the connection between IIT and the concept of criticality. Kim and Lee have observed that, in a Kuramoto model, criticality and EEG are connected. This describes synchronization [85–87]. The generalized Ising model, which simulates brain dynamics by displaying phase transitions and critical spots, is the simplest model linked to empirical pairwise correlation data [77,88]. Its dynamics have recently been shown to resemble those of the brain, leading to the development of the Critical Brain Hypothesis [89–91].

Criticality may be useful for optimizing the brain's capacity to process information [92], for understanding learning [93], and for memory [25,94,95]. It has also been observed [79] that when characterizing consciousness using IIT with an Ising model as a substrate, "consciousness" experiences a phase shift at criticality in the studied neural network motifs. This suggests, not least because of studies pointing to a possible crucial role of the brain, that "consciousness" might merely be the product of the brain's innate propensity to self-organize towards criticality. Further research into the interdependencies between the two hypotheses in patient and simulation data is made possible by the apparent synergy between the Critical Brain Hypothesis and IIT.

### 4.3. Grossberg's Input Regarding the Connection between Awareness and Biofield Resonance

How to account for our qualia, or phenomenal experiences, or the mental states that correlate to our experiences, is the difficult problem of consciousness. These could be regarded as the fundamental elements of conscious experience. The study of subjective experience, which originated with Wilhelm Wundt's introspective approach in the late 1800s, is a similar concept in psychology. To answer this problem, a theory of consciousness needs to model how emergent properties (such as self-organization, chaos theory, and self-referentiality theories) of different brain systems interact to incorporate specific features of each conscious psychological experience. In this instance, emergent features function more as a research guide than as an analytical tool. This is achieved by Grossberg's [96] adaptive resonance theory, or ART. Advanced brains may be able to learn to pay attention to, recognize, and predict things and events in a changing environment on their own, according to the cognitive and neurological theory known as ART. As part of its explanation of the mechanical linkages between the processes of consciousness, learning, expectancy, attention, resonance, and synchronization, ART has projected that "all conscious states are resonant states". It offers functional as well as mechanical interpretations of the data, covering a wide range of topics, from the dynamics of conscious perceptual, cognitive, and cognitive-emotional experiences to the synchronization of individual spikes. With the
advancement of ART-related science, it is now possible to categorize the brain resonances underlying conscious perceptions of seeing, hearing, experiencing, and knowing. This classification can provide insight into psychological and neurological data in both healthy and sick people. Moreover, not all brain dynamics can be characterized by ART, and not all resonances result in consciousness. Important elements of these explanations of conscious and unconscious processes include the cerebral cortex's organization into distinct cell layers (laminar computing) and the brain's global layout into computationally complementary cortical processing streams (complementary computing).

### 4.4. Formulating a Theory Linking the Conscious Mind and Resonant Brain Dynamics

Consider a theory in which the interactions of brain mechanisms produce dynamical states that approach the spatiotemporal dynamics and patterning of the resonant neural representations representing discrete conscious qualia, or the parametric properties of these qualia. Additionally, suppose that these resonant states produce observable data about these experiences that come from human subjects' non-invasive neurobiological and psychological experiments, and that these data are consistent with data from various types of neurobiological studies, including multiple-electrode neurophysiological studies on monkeys exposed to the same stimuli. Let us also assume that these resonant states resemble the qualities of the subjective reports of these qualia.

Considering their significant similarities to a variety of data types and experienced qualia, one could argue that these dynamical resonant states are more than just “neural correlates of consciousness” [97]. Instead, they are psychologically grounded, mechanical representations of the qualia that constitute each conscious experience. Strong proof that these brain representations are the sources and maintainers of these conscious sensations would be provided if there were a correlation of this kind between the detailed characteristics of conscious qualia and detailed brain representations for a significant enough body of psychological data. Such a theory would have yielded a hypothesis connecting the conscious mind to brain dynamics. Until the brain–mind connection hypothesis is validated, a “theory of consciousness” cannot be declared.

Should a scientist or philosopher maintain—despite a hypothesis of connection—that a theory of the hard problem cannot contribute to its solution until one can “see red” or “feel fear”, then no scientific theory can aspire to resolve the hard problem. This is because current research only explains the dynamical processes involved in the experience of distinct conscious qualia using a mechanical theoretical framework. However, just as the theory of relativity transformed our understanding of space and time and the quantum theory of matter, so too can a well-founded, albeit slowly evolving, theory of consciousness fundamentally transform our understanding of consciousness as it begins to explain an increasing number of distinct psychological, neurobiological, and even biochemical processes. The process of measuring our minds has limits, just like quantum theories. We cannot personally enter a neuron that is involved in conscious experience, just as we cannot personally ride an electron. Despite their limited capacity for empathy, physicists believe they have a solid understanding of the physical world since they can explain and predict enough of it.

### 4.5. The Conscious Mind and Resonant Dynamics Are Linked by the Adaptive Resonance Theory

Many psychological data concerning an individual's conscious experiences, including their spatiotemporal dynamics, can be explained by ART, in addition to the psychological processes and physiological underpinnings of dynamic brain states whose characteristics resemble the subjective elements of unique conscious experiences. Additionally, ART determines which brain regions these states might occur in and how they might all originate from a single set of identical neural mechanisms, even though these dynamical states each perform distinct functional tasks and are adaptable to different brain regions.

In a dynamic state known as resonance, interactions between top-down and bottom-up pathways produce reciprocal excitatory feedback signals that amplify and synchronize
neuronal firings throughout the brain network. Grossberg [96] refers to this type of dynamical state as “adaptive resonance”. These synchronized cells frequently oscillate in synchrony with one another through their activity. Resonant cell activity also highlights some cells for attention processes, providing insight into how the brain stores attended events. Because learning can occur within the adaptive weights or long-term memory (LTM) traces that are present at the synapses of these pathways, it is known as an adaptive resonance. Separate conscious qualia, according to ART, may arise as emergent, or interactive, characteristics of specialized neural networks when various specialized anatomies experience an adaptive resonance and receive distinct kinds of input from the brain and the environment. Specifically, ART suggests that in addition to the ability to identify these sensations, brain resonances also serve as parametric components of conscious experiences of feeling, hearing, and seeing.

Most likely, not every cell that maintains an adaptive resonance represents the conscious elements of an event. The capacity to detect psychological discriminations (such as color or fear) that are components of conscious experience is restricted, according to ART, to cells that can only do so at specific brain processing stages. This is supported by neurophysiological data about perceptual experiences [98], which have been described and, in some cases, even predicted by neural models [99]. If resonance is a more general concept than consciousness, then a theory about this more general concept would have to explain why this is the case as well as how conscious states arise from that broader function. Accordingly, it is false to say that “all resonant states are conscious states”, despite ART’s prediction that “all conscious states are resonant states”. Brain dynamics can also happen in the absence of resonance potential, particularly during the formation of motor and spatial representations. Certain conscious experiences might not be supported in this situation.

It is important to remember that awareness was not the initial intended application of ART, even though it can shed light on the brain's underpinnings of conscious experiences. Instead, it was intended to provide predictions and insights for large-scale databases pertaining to the understanding of learning, perception, emotion, and cognition in psychology and neuroscience. Reviewing aspects of how ART explains data whose features naturally lead to brain representations of conscious qualia is important to build a self-contained exposition, and in particular to allow for new data explanations and predictions about ART. It is also interesting that the current work accounts for a far greater amount of awareness-related evidence than prior research employing ART or any other concept does. It goes beyond summarizing ART; it accomplishes more than that. A large number of the conclusions regarding consciousness that the article makes also depend on the synthesis of different but concurrent streams of modeling research that are compiled into a single piece. Examining the salient characteristics of these several modeling streams is necessary to provide a self-contained description of their synthesis. To distinguish it from its forebears, this synthesis and expansion of the ART model is known as the conscious ART, or cART, model.

No other theory has been able to explain the functional significance of a wide range of neurobiological and psychological data, both normal and clinical, until the present cART theory. This holds regardless of whether one is interested in the hard problem or whether one's definition of the hard problem precludes any scientific theory.

## 4.6. *The Conflict between Stability and Adaptability and Lifelong Learning*

Throughout their lives, humans can quickly assimilate vast amounts of new information and integrate it into coherent conscious experiences that aid in the development of a sense of identity. All it takes to be amazed by this ability is to see an opera once, after which we can recall a great deal of information about it, including conscious moments of seeing, hearing, feeling, and knowing. Humans can pick up new information quickly without losing sight of what they already know, even in situations where we are unaware of how the laws of any given environment alter or evolve. When forgetting of this kind does happen, it is called catastrophic forgetting.
The phrase “stability–plasticity conundrum” was originally used by Grossberg [100] to describe how the brain learns new information rapidly and reliably without losing track of previously learned information. ART was created to explain how brains deal with the stability–plasticity dilemma. Over time, it has evolved into a neurological and cognitive theory that explains how the brain can learn to predict, identify, and pay attention to objects and events on its own in a changing environment, thereby preventing catastrophic forgetting. Of all the existing theories of cognition and neurology, ART offers the widest range of predictions and explanations.

How does the ART tale involve consciousness? ART allowed for the discovery of me-
chanical connections between learning, expectation, attention, resonance, and synchroniza-
tion processes in the brain to explain psychological facts about seeing, hearing, experiencing,
and knowing. The stability–plasticity problem was thus handled. Because similar reso-
nances were seen in the spatiotemporal patterning of cell activity inside networks of feature
detectors, it became evident from these findings that they also displayed parametrical prop-
erties of individual conscious experiences. The functional units of the brain’s short-term
memory (STM) and long-term memory (LTM) are distributed patterns across networks of
feature-selective cells, as early mathematical studies have demonstrated [101]. Subsequent
studies have demonstrated how these dispersed patterns facilitate synchronous resonant
states that direct attention toward the brain’s key information-representative characteristics,
like feature patterns that reflect the parametric characteristics of distinct conscious experi-
ences. These discoveries over several decades contributed to an increasing understanding
of the connections among consciousness, learning, expectation, attention, resonance, and
synchrony processes (CLEARS).

According to ART, any brain representation that resolves the stability–plasticity para-
dox uses a variation of the CLEARS process [96]. The reasons for why “all conscious
states are resonant states”, that a large number of animals are intentional beings that pay
attention to salient objects, and that brains are capable of learning both many-to-many
and many-to-one maps—representations that provide us with extensive information about
specific objects and events—are all explained by CLEARS mechanisms.

Bhatt et al. and others claim that ART is responsible for achieving these qualities [102,103]. More specifically, when a top-down anticipation and a bottom-up input pattern sufficiently match, a synchronous resonant state incorporates an attentional focus. This also explains how top-down attentive matching may help to resolve the stability–plasticity conundrum by focusing attention on salient combinations of cues, known as critical feature patterns, which characterize how attention may operate via a form of self-normalizing “biassed competition”. This resonance’s capacity to accelerate learning suppresses unobserved outliers that might have led to catastrophic forgetting and incorporates the attended critical feature pattern into the top-down expectations that the recognition categories read out, as well as the bottom-up adaptive filters that activate them. Adaptive resonance comes from this source.

Research in the fields of psychology and neurobiology has confirmed several connec-
tions between the mechanisms represented in the CLEARS mnemonic. For instance, ART
predictions for somatosensory learning [104,105] and auditory learning [106] are consistent
with the link between attention and learning. From the normal and abnormal aspects of
human and animal perception and cognition to the spiking and oscillatory dynamics of
hierarchically organized laminar thalamocortical and corticocortical networks in multi-
ple modalities, ART has been continuously developed to explain and predict ever-larger
behavioral and neurobiological databases [100].

4.7. *Why Not AI Representations?*

We must explain why artificial intelligence (AI) and artificial neural network tech-
niques are not a part of the paradigm being supported and developed here to fully justify
our position. While being a major advocate of energy-based models, Yann LeCun [107] has
given less attention to probabilistic graphical models that give a probability to each poten-
tial combination of relevant components that could exist. He has employed "energy-based models (EBM)" that establish a connection between energy and configurations that eliminate the need for suitable normalization of probability distributions. Such systems must be trained to be discriminative to associate lower energy with acceptable configurations and higher energies with bad configurations. While we briefly describe the overall problem with AI models below, the main issue is that they are undoubtedly artificial and not very intelligent in comparison with human neurocognitive functions [108].

It has been commonly thought that the mind functions in the brain similarly to a computer [108]. These theories overlook aspects of neurophysiology that conflict with the process of modeling the brain computationally. Glial cells comprise around 80% of the brain; at all levels of neural processes, such as dendritic-dendritic processing, electrotonic gap junctions, cytoplasmic/cytoskeletal activity, and living conditions (the brain is alive!), these cells demonstrate broad apparent randomness. Moreover, the emergence theory does not contain any testable hypotheses. We may process cognitive events when we have some degree of volitional control or coordination, knowledge of our senses and experiences (i.e., cognition), or both. Consciousness emerges spontaneously; no threshold or reason is required. The idea that the human brain functions similarly to a computer has been advanced for nearly fifty years by psychologists, linguists, and neuroscientists who study human behavior. Examining how the infant's brain develops refutes the computer analogy. Computers are guided in everything they do by algorithms. But that is not how people operate as individuals do.

In light of this, why do so many researchers discuss mental health and cognition using language that sounds like it was generated by a computer system? It is difficult, if not impossible, to comprehend intelligent human behavior without the problematic information processing (IP) model. A flawed syllogism with two reasonable premises and a fallacious conclusion forms the basis of the IP paradigm. The idea that all computers are capable of intelligent behavior is the first tenable assumption.

The idea that all computers contain information processors is a second tenable assumption. The erroneous conclusion: every organism capable of information processing is capable of intelligent action. Beyond technical terms, it is counterproductive to assume that simply because computers analyze data, so should humans. Historians will probably see the IP metaphor in the same way that we do with mechanical and hydraulic analogies when it is finally abandoned. Rather than utilizing a model more closely associated with physical ideas such as resonance to observe and quantify occurrences, let us employ a more intelligent and natural approach to "artificial intelligence".

**5. Coherence in Resonance**

Fries [34] uses his communication through coherence (CTC) paradigm to explain how resonance among various brain regions has a part in "selected communication" (i.e., when coherent and resonant, the "windows" are open; when incoherent and not resonant, they are closed). Furthermore, coherence enables certain neurons to synchronize with the main resonance frequency. Fries [34] states that communication requires coherence. When there is a lack of coherence, the excitability cycle experiences random inputs, which reduces effective connectivities. A postsynaptic neural group responds mostly to the coherent presynaptic group when it receives inputs from many presynaptic groups. Selective communication is thus accomplished by selective coherence.

A lack of coherence causes inputs to come at random times during the excitability cycle, which lowers effective connectivities. If one group of synaptic inputs, which collectively create one neural representation, can elicit postsynaptic excitation followed by inhibition, then further synaptic inputs will be unable to enter the cell because inhibition will have prevented that from happening. Consequently, these additional inputs are unable to inhibit themselves, and they cannot communicate the neuronal image that they generate. By entraining the postsynaptic neuronal group's perisomatic inhibition and instructing it
to follow its rhythm, this tactic creates a selective or exclusive communication channel.
Figure 2 is an example of optimizing inter-regional brain communication.

<figure><img src="image_7.png" /></figure>

**Figure 2.** Diagram demonstrating the concept of communication through rhythm. (A) If neurons preferentially fire at a particular phase of an oscillation and the oscillations at two sites are coherent with a phase delay matching the conduction delay between them, spikes generated at the excitable phase in one region will arrive at the excitable phase in the downstream region. The construction of a communication line between two coherent zones is a feasible application for these periodic bursts of peak gain. On the other hand, if the phase and latency are not the same, spikes that occur outside of the excitable phase are less likely to result in firing (B) Average spike-field coherence (SFC) spectrum, with a distinct peak in the alpha band (f = [7, 8] Hz) for the average SFC spectrum across all units. The mean waveform (black) and individual spike waveforms (grey) for a single example unit are shown in the inset. The horizontal scale bar is 1.5 ms; the vertical scale bar is 10 SD. (C) Peak frequency histogram for each unit. Distributions of correlations between instantaneous firing rate and LFP alpha power are shown in the inset. For alpha coherent units (orange), alpha power and spike rate are generally negatively associated, while for all other units (grey), there is no correlation. (D) Spike-triggered average for the units at f = [7, 8] Hz with the maximum SFC. Multiple-cycle alpha oscillations are observed. (E) Phase preference for the alpha coherent units. (adapted from Chapeton et al. [109], with permission).

It is now thought that small organisms merged to create the organelles of highly
developed eukaryotic cells, such as mitochondria, which subsequently came together to
form multicellular organisms [110]. It seems plausible that life acquired the ability to
incorporate subjective perceptions into hierarchically nested groups of more advanced
consciousness at the same time that it obtained the ability to unite various living units
into more complicated solitary life forms. The hypothesis that was suggested by Zeki and
Bartel [111,112] to explain how consciousness develops from neuronal activity in the brain
relies on this hierarchical view of awareness.

Zeki and Bartel [111,112] contend that the brain arranges multiple conscious experi-
ences hierarchically to produce a single, cohesive experience using processing rate varia-
tions in various visual system components. They proposed that consciousness exists on
three different hierarchical levels in the brain: unified consciousness, which corresponds to
the experience of the individual experiencing the perception; micro-consciousness, which
corresponds to the diverse visual system levels (for example, V4 processes color and V5 motion); and macro-consciousness, which combines numerous aspects of the system (e.g., color binding to motion).

Zeki further suggests that there is a specific temporal order in which each of these nested levels of awareness happens, with the lower-order levels coming before and influencing the higher-order ones. The reason for this is that the lower-order levels are more fundamental. Zeki's model can be explained as follows [112]. According to Zeki, there are three distinct hierarchical levels of awareness: micro-, macro-, and united consciousness. One level must depend on the presence of the level below it. One might imagine a temporal hierarchy at each level. For the micro-consciousness level, this has been demonstrated, since color and motion are perceived differently. Zeki [110] claims that it has been demonstrated that binding across characteristics takes longer than binding within attributes at the level of macro-consciousnesses". "Myself" is the perceiving individual, the product of individual temporal hierarchies of micro- and macro-consciousnesses leading to a final, combined consciousness.

The smallest sentient observers may be found exclusively in the inorganic world at the atomic and molecular levels. Life's evolution may have given rise to the capacity to organize conscious observers into hierarchies within other conscious observers, with each level embracing a more macroscopic perspective based on the (rudimentary) level of awareness intrinsic to atoms and molecules. When the organism's unified dominating experience occurs, this process ultimately reaches its greatest level.

Zeki divides his suggested levels into chronological sequences, with higher-order events taking place later in time. In other words, Zeki's hypothesis suggests that different brain regions that are involved in consciousness may go through the same experiences for slightly different times and for varied lengths of time, with the ultimate unified awareness involving the longest moments/duration of experience.

**6. Integrating Consciousness and Cognition through Resonance**

There is evidence that shared electrical resonance is probably the origin of this combination [33,34]. This shared electrical resonance may also be preceded and led by a shared quantum entanglement resonance. The largely unchanging physical structures can support several states of consciousness because of the constantly shifting energy and information fluxes that overlay the typically stable physical structures. This is excellently shown by Fries [33], who challenges whether brain communication is dependent on neuronal synchronization, suggesting that the communication pattern could be altered by dynamic shifts in synchronization. Stable structures are the "backbone" of conscious processes, according to Fries. He thinks that the fundamental components of cognition are these adaptive modifications to the brain's communication system, which are reinforced by the more inflexible anatomical structure, but operate through activity waves.

Constraints on the common resonance states may prevent physical materials from generating macro-consciousness. Situations that are disorganized and non-resonant hinder the exchange of information spatially. Dominant consciousness—like the waking consciousness that people experience—needs large-scale common resonance. There exists a hierarchy of resonance in brain networks [113,114]. Even when higher-level resonance, which represents more degrees of integration, is temporarily absent (such as during a seizure, sleep, or death), lower-level resonance may still exist, necessitating less information integration (death, in which case lower-level resonances may continue for some time but will also, before too long, dissipate). Small spaces may prevent physical materials from developing macro-awareness [113,114].

These mechanisms may account for the observed phenomenon of important brain regions synchronizing at regional levels during seizures in the absence of global consciousness [115]. Characterizing conscious experiences during absence seizures is often challenging since higher levels of organization lose their characteristic synchrony while lower-level systems (local and individual clusters of neurons) remain in synchrony [25,27,116]. Since resonance
reflects the physical structure's dynamics, changes in resonance and awareness states do not affect the stability of the physical structure.

Some additional examples supporting the importance of resonance supporting physi-
ological processes associated with consciousness and cognition might include a dyskinesic
individual's symptoms being eradicated by light filters. We have also included references to
the effects of transcranial DC magnetic stimulation (tDCS) on consciousness and cognition,
which, although beyond the scope of the present paper, has garnered enough recent support
to better understand the relation between resonance and physiological function [117–119].

**7. Cortical Activity Waves as a Vehicle for Consciousness and Cognition**

7.1. *Continuum Waves in Consciousness and Cognition*

The concept of “continuum waves” is not a well-established term in cognitive science or neuroscience. However, if we interpret it in a broader sense as referring to the continuous flow of neural activity and information processing in the brain, we can explore how it might relate to thought processes. In cognitive neuroscience, thoughts are understood to emerge from complex patterns of neural activity and information processing within the brain. This activity involves the coordinated firing of neurons across various brain regions, forming intricate networks that give rise to cognitive processes such as perception, attention, memory, reasoning, and problem solving.

If we consider “continuum waves” as metaphorically representing the dynamic and
continuous nature of neural activity, we can think of thoughts as emerging from the
modulation and interaction of these waves within the brain. Different types of thoughts,
such as memories, emotions, or abstract reasoning, may correspond to distinct patterns or
frequencies of neural oscillations and connectivity.

Furthermore, the concept of continuum waves could also encompass the idea that thoughts exist on a spectrum, ranging from automatic and subconscious processes to deliberate and conscious ones. Just as waves in the ocean vary in intensity and frequency, thoughts can vary in their level of awareness and control.

Overall, while the term “continuum waves” may not have a direct counterpart in
cognitive science, we can conceptualize how the continuous flow of neural activity and
information processing in the brain underlies the generation and modulation of thoughts
across different domains of cognition.

7.2. *Continuum Analysis Applied to Consciousness and Cognition*

Mammalian brains differ from reptile brains in that they have a layered anatomy [120], which implies that possessing this structure has some evolutionary advantage. We here propose that the layered cortex structure permits a time delay in the interconnections between its layers and that this temporal lag efficiently controls brain activity waves, which are cortical tissue's resonant modes and the electrochemical energy they store. When enough energy is stored, these waves expand as they move to locations that are substantially farther from their origin than the normal axonal length, and they interact with most of the cells in their path until they reach their saturated amplitude.

The forgoing is grounded on the revolutionary continuum analysis of Wilson and Cowan [121,122], applied to a simple two-filamentary layered system separated by a uniform time delay in space and undergoing modest temporal variations [123]. In the Wilson-Cowan model, all other components are driven by synaptic flux into hypothetical “continuum elements”, acting as reservoirs of energy; flux and stored energy are connected via what is compared to a “leaky capacitor”. The energy is stored in the excitatory (e) and inhibitory (i) activities of the constituent cells, which are considered to be of these two types. The internal energy of each species is considered independently. Elements can be connected in four distinct ways: e-e, i-i, i-e, e-i, and i-e. The afferent species is indicated by the first index, while the efferent species is shown by the second index. The length of the typical connection probability of each afferent species is termed the connection range, which is the amount of time that decreases with distance along the layers [121].
The neuronal continuum can be impacted by waves of spreading and rising activity [124,125]. When one element exhibits some excess of excitatory activity, the return i-e connections lower the excitatory surplus in the originating element, while the e-i connections to adjacent elements raise the inhibitory activity. Oscillations are linked to an overcorrection, as is often the case. Meanwhile, e-e and i-i linkages stimulate development and proliferation by way of transmission to additional elements. Since the factors governing wavelength and frequency are essentially constant and only vary through synaptic modulation, this approach does not meet the requirements for carriers of cognitive processes [126].

Due to the phase relations necessary for the amplification of waves, the delay adds control. The wave is now generated as before by an excitatory surplus element, but it separates in two, with one half propagating to the opposite layer with a delay of T. The relationship between activity and flux in the second layer leads to a phase shift approximating $\pi/2$. A similar procedure is followed to return to the original layer. Amplification occurs only when the total phase shift is an even multiple of $\pi$. Consequently, the link between the delay and the angular temporal frequency of the favored wave is multivalued.

$$ \omega T = \left(n + \frac{1}{2}\right)\pi \quad n = 0, 1, \dots \qquad (1) $$

The integer $n$ that yields the fastest growth—which is initially exponential—is the one selected in each given scenario [127]. The chosen mode typically becomes observably dominant at this growth rate. Nonetheless, both modes expand at those values of $T$ where their growth rates are equivalent, and this situation continues until $T$ reaches a point when the new mode takes precedence. When it comes to activity waves, the propagation speed increases with the wavelength, and the wavelength is a single-valued function of frequency. As a result, the waves move through space at various speeds and occupy various locations, creating a highly erratic pattern of activity in between.

### 7.3. The Wilson-Cowan Equations

The relative active percentages $A_{sl}$ $s = e, i, l = 0,1$ at time $t$ of the various species in the two layers, within an element centered at position $x$, are the dependent variables in our model (there are four in total).

$$ \frac{\partial}{\partial t} A_{sl} + A_{sl} = S(N_{sl}) \qquad (2) $$

Here, $N$ reflects the synaptic flux into the element from all other elements, weighted by the inter-layer signal latency, and by the connection probability decreasing with distance.

$$ N_{sl}(x, t) = \sum_{u=e,i} \sum_{m=0,1} \int_{-\infty}^{\infty} B_{us,ml} A_{u,m}(X, t - T_{ml}) e^{-\frac{|X-x|}{c_{us,ml}}} \qquad (3) $$

The connection coefficients $B_{us,ml}$ possess, when the cells are in the same position laterally, a cell of species $u$ and layer $m$, with an afferent connection probability that is proportionate to the species and layer in question.

The connection probability between the afferent element at position $X$ and the element at location $x$, which decreases with lateral distance, is described by the exponent; the connection range $\varepsilon_{us,ml}$ is considered to exclusively depend on the afferent species. All inter-layer connection parameters and connections within the layers, in this calculation, are assumed to be the same in both directions. Delay $T_{ml}$ applies only if $m$ and $l$ relate to different layers when it equals $T$. A typical synapse's synaptic energy is represented by the attention parameter $F$, $T$ and $F$ remain constant here and the integral encompasses all of the space, whereas the sums cover both the species and strata.

The sigmoid function $S$ must be used as a flow limiter, since, when all cells are active, the total stored energy cannot vary, nor can it decrease when none are. Since it is assumed that both species and layers have an equilibrium activity level, which is taken to be $\frac{1}{2}$, the
active fractions fall between $-\frac{1}{2}$ and $\pm\frac{1}{2}$. When the synaptic flux is zero, S's functional form is zero with a slope of one. A decrease toward the equilibrium with a time constant of unity is represented by the second term in (2).

## 7.4. Numerical Results

$$N_{sl}(x, t) = \sum_{u=0,1} \sum_{m=e} \int_{-\infty}^{\infty} dX B_{us,ml} A_{u,m}(X, t - T_{ml}) e^{\frac{-|X-x|}{\sigma_{us,ml}}} \quad (4)$$

The excitatory active fraction is considered to have been subjected to a $\delta$-function impulse with amplitude of 0.0001 (above equilibrium) at time zero, and the first time step is carried out analytically to seed all the points. A convergence test is included in the Runge-Kutta-Fehlberg (RKF) method, which is used to simultaneously solve the equations in (2). A cubic spline integrated analytically is used to compute the integral in (3) at each function evaluation (using discrete approaches results in spurious spectral lines). The same-layer inhibitory connection range acts as the spatial unit and is used to represent all other ranges.

The 2048 amplitudes are written at intervals of several time steps in temporary files to account for the time delay; linear interpolation is employed to obtain the intermediate values required for the RKF test. To avoid end effects, the computation is terminated when a detectable signal reaches the final point. Fast Fourier Transform (FFT) is used after every time step; this establishes the connection between wavelength $\lambda$ is $\lambda = 256/k$ and spectral variable $k$.

The findings for two values of delay are shown in Figures 3 and 4. Only positive amplitudes are displayed for clarity's sake. Since the neural refractory period is longer, the negative amplitudes are slightly bigger [121].

<figure><img src="image_8.png" /><figcaption>**Figure 3.** (A) The wave produced at time zero by a very slight variation (0.0001) in the excited fraction in an element located at layer 0 position. Here, we see the exponential growth of the wave amplitude in the stimulated layer, which is driven by the flux limit to saturation, with a maximum amplitude of around 0.4. Time units are decay durations, while spatial units are the same-layer inhibitory range. (B) The identical wave's spectral intensity is a function of time and wave number $k$ (into the diagram). In the same layer, the connection parameters are the following: $B_{ee} = 3.5$, $B_{ei} = 5.9$, $B_{ie} = -4.8$, $B_{ii} = -2.5$, $\sigma_e = 1.25$, $\sigma_i = 1.0$; for opposite layers, $B_{ee} = 3.0$, $B_{ei} = 1.1$, $B_{ie} = B_{ii} = -1.0$, $\sigma_e = \sigma_{ii} = 0.6$. The attention factor $F = 0.45$, and the time delay $T = 2.4$.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

**Figure 4.** Here, the parameters are the same as in Figure 3, except the delay *T* is 3.6 decay periods. The initial noise now generates two waves, with different wavelengths. The propagation speed increases with increasing wavelength so that the waves occupy different spatial regions. There is erratic activity in the intermediate space. The energy is spread between the two spectral peaks, as shown by their lesser amplitudes. Later on, a non-linear interference excites other waves with shorter wavelengths.

Figure 4 shows the scenario at a different value, in contrast.

The wave in Figure 3 is coherent due to its regularity and narrow spectral peak. Despite the random position of the starting noise, active and inactive elements behave in lockstep when they are one wavelength apart. Two waves are produced under the parameters shown in Figure 4; they propagate at different speeds and occupy various spatial regions, have different growth rates, and have different wavelengths. There is erratic activity in the middle space. Figure 5 depicts the influence the delay has:

<figure><img src="image_11.png" /></figure>

**Figure 5.** An example of a contour plot showing the spectral intensity against delay for a set of studies with constant delays after stimulation (80 decay periods). The remaining variables are the same as those in the figures that came before.
The preferred wavelength when just one wave is growing is a (decreasing) function of delay; altering the delay reorganizes the neurons into a subset that they were previously arranged into. Because the linked group is reconstituted (almost exactly) upon repeating a value of *T*, this procedure can be compared to a memory search. Regarding the theories of memory storage, we remain neutral and do not make any assumptions about the relationship between the waves and semantic content. However, since our simplistic model closely resembles the real cortex, these theories need to account for activity waves, which are the corresponding resonant modes.

**8. Conclusions: Resonance Signatures Allow Us to Adapt and Effectively Interact with Our Environment**

A multitude of delays and ensuing rhythms are possible due to the human cortex's six layers. There is currently no explanation for the different electroencephalographic frequency bands' origins [128,129]. It is evident that the current theory directly addresses that issue.

Since the location of the irregular wave pattern is fixed in only concerning the origin, the random placement of the first stimulus affects when two waves are produced. As a result, there are a wide variety of potential neuronal firing patterns in these situations. In the spirit of the continuum analysis, which divides cells into components, there are relatively fewer, but still a huge number of options, if each subset of individual cortical cells reflects a unique thought. There are about 106 billion such possibilities for about 20 billion cortical cells.

This hypothesis does not explain how these patterns are “decoded” into a combination of memories from the past and the semantic content of various wave memories or conscious awareness. It might, however, provide as an illustration of how cognitive research can benefit from the study of brain tissue as organized resonating media.

Each part of one's brain and body probably has its own resonance signature. These signals can fluctuate subtly from moment to moment, possibly fluctuating around the average frequency value. It has been hypothesized that the major cause of conscious experiences is the process by which our brains learn new knowledge about an ever-changing reality throughout our entire lives. Developing resonant states between bottom-up and top-down processes, learning top-down expectations, matching them with bottom-up data, and concentrating our attention on the predicted information clusters as they come to a consensus between what is expected and what is real in the outside world are examples of some of these processes.

Every conscious experience in the brain is thought to be a resonant condition, and learning, sensation, and their associated cognitive representations are caused by these resonant situations (see Grossberg [116]). As we gain more knowledge about the environment, our sensory and cognitive representations of it can hold true, according to Grossberg's idea. Our bodies can unlearn irrelevant learned maps as our motor and spatial representations develop from childhood to adulthood. This happens as a result of the brain trying to keep up with the body's changes. We conclude that procedural memories are not conscious because resonance cannot be produced by the inhibitory matching processes that underpin spatial and motor actions. Resonance, then, is the language that enables effective communication with our external and inner environments as well as with other people. Activity waves are how criticality is governed; but what governs them?

**Author Contributions:** Conceptualization, G.L. and P.K.; Methodology, G.L. and P.K.; Software, P.K.; Validation, G.L. and P.K.; Formal analysis, G.L. and P.K.; Investigation, G.L. and P.K.; Data curation, G.L. and P.K.; Writing—original draft, G.L. and P.K.; Writing—review & editing, G.L.; Visualization, G.L. and P.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The author declares no conflict of interest.

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Article

# The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Birgitta Dresp-Langley

Centre National de la Recherche Scientifique UMR 7357 CNRS, Strasbourg University, 67000 Strasbourg, France;
birgitta.dresp@cnrs.fr; Tel.: +33-388119117

**Abstract:** Two universal functional principles of Grossberg's Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from *aplysiae* to primates. They are re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg's pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.

**Keywords:** multisensory perception; brain representation; contextual modulation; adaptive resonance; biological learning; self-organization; matching rules; winner-take-all principle

## 1. Introduction

In his latest book [1], Grossberg discusses empirical findings and his own neural network models to illustrate and forecast how autonomous adaptive intelligence [2] is or may be implemented in artificial systems at unprecedentedly high levels of brain function [3-5]. His account of how the brain generates conscious cognition and, ultimately, individual minds provides mechanistic insights into complex phenomena such as mental disorders or the biological basis of morality and religion. The author's theoretical work clarifies why evolutionary pressure towards adaptation and behavioral success not only explains the brain but is also a source for model solutions to large-scale problems in machine learning, technology, and artificial intelligence. Adaptive brain mechanisms [6] are the key to autonomously intelligent algorithms and robots. They may be pre-determined by a universal developmental code, or "engram", that is channeled through the connectome by specific proteins/peptides embedded within pre-synaptic neuronal membranes [7] and corresponds to information provided by the electrical currents afferent to pre-synaptic neurons [8-10]. Grossberg's book [1] conveys a philosophical standpoint on shared laws of function in living systems, from the most primitive to the most advanced, showing how neurons support unsupervised adaptive learning in all known species and how such biological learning has enabled the emergence of the human mind across the evolutionary process. Bearing this in mind, the present concept paper draws from the beginnings of this journey into the mind, which is described by Grossberg's significant early work on neural processes for perception, perceptual learning, and memory, aimed at understanding how the brain builds a cognitive code of physical reality. Since perception is the first step through which a brain derives sense from the raw data of a physical environment, his account of how elementary signals in the physical environment are processed by the neural networks of the brain was a mandatory achievement for understanding how inner representations of the outside world may be generated [11,12]. The ability to derive meaning

**Citation:** Dresp-Langley, B. The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience. *Information* **2023**, *14*, 82.
https://doi.org/10.3390/info14020082

Academic Editor: Gordana Dodig-Crnkovic

Received: 30 December 2022
Revised: 27 January 2023
Accepted: 29 January 2023
Published: 1 February 2023

<figure><img src="image_12.png" /></figure>

**Copyright:** © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
from complex sensory input requires the integration of information over space and time, as well as memory mechanisms to shape that integration [13] into the contents of experience. In mammals with intact visual systems, this relies on processes in the primary visual cortex of the brain [14], where neurons integrate visual input along shape contours into neural association fields [15]. The geometric selectivity of ensembles of functionally dedicated neural networks is progressively fine-tuned by contextual modulation and experience towards long-term memory representation of all the different configurations likely to be encountered in natural scenes. Horizontal cortical connections provide a broad domain of potential associations in this process, and top-down control functions dynamically gate these associations to switch the function of a given network [16]. Grossberg's work has provided a unified model of brain learning where horizontal cortical connections provide a broad range of potential, functionally specific neural associations through a mechanism called bottom-up activation [17], as will be explained and illustrated on the basis of examples. Mechanisms of adaptive resonance and top-down matching [17] then explain how the contextual modulation of visual and other sensory input drives dynamic brain learning to gate the links within and between neural association fields towards increasingly complex memory representations [16,18]. This concept paper uses two of the functional principles of adaptive resonance theory [19] to illustrate the implications for unsupervised brain learning and adaptive intelligence. The examples chosen here are drawn from the original models and related empirical findings. These are revisited under the light of some of the most recent advances in a conceptual discussion aimed at highlighting the potential of Grossberg's pioneering insights and groundbreaking theoretical work for intelligent solutions to some of the most difficult current problems in artificial intelligence (AI) and robotics. The following sections will elaborate on the biological principles of multisensory contextual modulation in the brain in Section 2 to illustrate the relevance of adaptive resonant learning as conceptualized in the Grossberg code, the functional principles of which are then explained further in Section 3. Section 4 provides a generic ART system with its mathematical definition and an example of neural network architecture that could be implemented on this basis for autonomous and self-organizing multiple event coding to help control object-related aspects of environmental uncertainty in robotics.

## 2. Contextual Modulation in the Brain

The brain processes local information depending on the context in which this information is embedded. The representation of contextual information peripheral to a salient stimulus is critical to an individual's ability to correctly interpret and flexibly respond to stimuli in the environment. The processes and circuits underlying context-dependent modulation of stimulus-response function have mostly been studied in vertebrates [20], yet well-characterized connectivity patterns are already found in the brains of lower-level species such as insects [21], providing circuit-level insights into contextual processing. Recent studies in flies have revealed neuronal mechanisms that create flexible and highly context-dependent behavioral responses to sensory events relating to threats, food, and social interaction. Throughout brain evolution, functional building blocks of neural network architectures with increasingly complex functional architectures have emerged across species, with increasingly complex long-range connectivity ensuring information encoding in processing streams that are anatomically segregated at a cellular level. The functional specificity of individual streams, long-range interactions beyond the classic receptive field of neurons and interneurons [22], and cortical feedback mechanisms [21,23] provide an excellent model for understanding the complex processing characteristics inherent to individual streams as well as the extent and mechanisms of their interaction in the genesis of brain representation. Contextual modulation in the sensory cortex coding for vision, hearing, somatosensation, and olfaction is partly under central control by the prefrontal cortex, as shown by some of the most recent evidence from neuroscience.
## 2.1. Vision

To be able to extract structure, form, and meaning from intrinsically ambiguous and noisy physical environments, the visual brain has evolved neural mechanisms dedicated to the integration of local information into global perceptual representations. This integration is subject to contextual modulation [23]. Mechanisms with differential sensitivity to relative stimulus orientation, size, relative position, contrast, polarity, and color operate within specific spatial scales to integrate local visual input into globally perceived structure [24–27]. The differential contextual sensitivity to color and luminance contrast in visual contextual modulation involves the luminance-sensitive pathways (M-pathways) and the color-sensitive pathways (P-pathways) of the visual brain [23,28] in a simple-to-complex-cells processing hierarchy at the level of the visual cortex, already predicted in Grossberg’s early models of visual form representation [29–31]. The cooperative and competitive interactions between co-activating or mutually suppressive detectors in functionally dedicated neural networks suggested in the model were confirmed several years later in psychophysical and electrophysiological studies, taking into account response characteristics of orientation-selective visual cortical neurons as a function of the context in which visual target stimuli were presented [22,24,25]. Contextual modulation translates into effects where nearby visual stimuli either facilitate or suppress the detection of the targets (behavior) and increase or decrease the firing rates of the cortical neurons responding to the targets (brain). The cooperative and competitive brain-behavior loops depend on the geometry of so-called “perceptive fields” [22] within a limited range of size-distance ratios. The shorter temporal windows of achromatic context effects compared with chromatic contextual modulation [23,27] Cooperative mechanisms of contextual modulation in vision are subject to substantial practice (perceptual learning) effects, where top-down signals dynamically modulate neural network activities as a function of specific perceptual task constraints. Such top-down-mediated changes in cortical states reflect a general mechanism of synaptic learning [4,8], potentiating or suppressing neural network function(s) depending on contextual relevance.

## 2.2. Hearing

Sounds in natural acoustic environments possess highly complex spectral and temporal structures, spanning over a whole range of frequencies, with temporal modulations that differ within frequency bands. The auditory brain [32] is capable of reliably detecting one and the same sound in a variety of different sound contexts, as well as distinguishing between different sounds within a complex acoustic scene. Processing acoustic features like sound frequency and duration is highly dependent on co-occurring, acoustic, and other sources of stimulation and involves interactions between an auditory target’s spectral and temporal context and individual behavioral states like arousal or expectation. Current findings suggest that sensory attenuation and neuronal modulation may happen during behavioral action as a consequence of disrupted memory expectations in the case of unpredictable concurrent sounds [33]. The auditory system demonstrates nonlinear sensitivity to temporal and spectral context, often employing network-level mechanisms, such as cross-band and temporally adaptive inhibition, to modulate stimulus responses across time and frequency [33–35]. How the auditory system modulates responses to sensory and behavioral contexts is not yet understood. The superior colliculus (SC) is a structure in the mammalian midbrain that contains visual and auditory neural circuits. In mice [34], auditory pathways from external nuclei of the inferior colliculus (IC) with direct inhibitory connections and excitatory signals driving feed-forward inhibitory circuits within the SC were found. The lateral posterior nucleus (LP) of the thalamus projects extensively to the sensory cortex, a previously unknown pathway. Bidirectional activity modulations in LP or its projection to the primary auditory cortex (A1) in awake mice reveal that LP improves auditory processing by sharpening neuronal receptive fields and their frequency tuning [34,35]. LP is strongly activated by specific sensory signals relayed from the superior colliculus (SC), contributing to the maintenance and enhancement of sound
signal processing in the presence of auditory background noise and threatening visual stimuli, respectively. This shows that multisensory bottom-up pathways play a role in contextual [36] and cross-modality modulation of auditory cortical processing in mammals. Cross-modality modulation of sensory perception is necessary for survival. In a natural environment, organisms are constantly exposed to a stream of sensory input depending on the environmental context. The response properties of neurons dynamically adjust to contextual changes across all sensory modalities and at different stages of processing from the periphery to the cortex.

## 2.3. Somatosensation

Cross-modality modulation implies that coincident non-auditory (visual, tactile) processing influences the neural networks underlying contextual modulation of hearing or that non-visual (auditory, tactile) signals may reach the neural networks underlying contextual modulation of vision. Touch, for example, has a direct effect on visual spatial-contextual processing [37]. Contextual modulation and neuronal adaptation in the visual and auditory systems interact with sensory adaptation in the somatosensory system, but it is unclear which pathways and mechanisms are involved. The ability to integrate information from different sensory modalities is a fundamental feature of all sensory neurons across brain areas, which makes sense in light of the fact that visual, auditory, and tactile signals originate from the same physical object when actively manipulated. The synthesis of multiple sensory cues in the brain improves the accuracy and speed of behavioral responses [38]. In motor tasks, task-relevant visual, auditory, and tactile signals are experienced together [39], and pioneering work in neurophysiology from the 1960s demonstrated convergence of visual, auditory, and somatosensory signals at the pre-frontal cortex level in cats [40]. Also, visual signals can bypass the primary visual cortex to directly reach the motor cortex, which is immediately adjacent and functionally connected to the somatosensory cortex [41]. Effects of neuronal adaptation on response dynamics and the encoding efficiency of neurons at single-cell and population levels in the whisker-mediated touch system in rodents illustrate that sensory adaptation provides context-dependent functional mechanisms for noise reduction in visual processing [42]. Between integration and coincidence detection, cross-modality modulation achieves energy conservation and disambiguates the encoding of principal features of tactile stimuli. Sensory systems do not develop and function independently. Early loss of vision, for example, alters the coding of sensory input in the primary somatosensory cortex (S1) to promote enhanced tactile discrimination. Neural response modulation in S1 of mammals (opossums in this case) after elimination of visual input through bilateral enucleation early in development reveals the neural origins of tactile experience in naturally occurring patterns of exploratory behavior after vision loss [43]. In early blind animals, overall levels of tactile experience were similar to those of sighted controls, and their locomotion activity was unimpaired and accompanied by normal whisking. Early blind animals exhibit a reduction in the magnitude of neural responses to whisker stimuli in S1, combined with a spatial sharpening of the neuronal receptive fields. The increased selectivity of S1 neurons in early blind animals is reflected by improved population coding of whisker stimulus positions, particularly along the axis of the snout aligned with the primary axis of the natural whisker motion. These findings suggest that a functionally distinct form of tactile (somatosensory) plasticity occurs when vision is lost early in development. After sensory loss, compensatory behavior mediated through the spared senses is generated through the recruitment of brain areas associated with the deprived sense. Alternatively, functional compensation in spared modalities may be achieved through a combination of plasticity in brain areas corresponding to both spared and deprived sensory modalities.

## 2.4. Olfaction

Multisensory interactions in the brain are most strongly relied upon and, therefore, need to be optimal when the stimulus ambiguity in a physical environment is highest [44].
Sensorial as well as central cross-modal signaling mechanisms contribute to bottom-up and top-down contextual signaling. For example, both whisking and breathing are affected by the presence of odors in rodents, and the odors modulate activity in a small but significant population of barrel cortex neurons through distinct bottom-up and top-down mechanisms [45]. In the human brain, different aspects of olfactory perception in space and time have been identified by means of EEG recordings [46]. Sensorial (low-level) representations of smell expand into larger areas associated with emotional, semantic, and memory processing in activities significantly associated with perception. These results suggest that initial odor information coded in the olfactory areas evolves towards perceptual realization through computations (a long-range mechanism) in widely distributed cortical regions with different spatiotemporal dynamics [47]. Specific brain structures act as hubs for integrating local multisensory cues into a spatial framework [48], enabling short-term as well as long-lasting memory traces of odors, touch sensations, sounds, and visual objects in different dynamic contexts. Contextual modulation in the brain thus explains how olfactory and other sensory inputs translate into diverse and complex perceptions, such as the pleasurable floral smell of flowers or the aversive smells of decaying matter. The prefrontal cortex (PFC) plays an important role in this process. Recent evidence suggests that the PFC has dedicated neural networks that receive input from olfactory regions and that the activity of these networks is coordinated on the basis of selective attention, producing different brain alert states [49].

## 2.5. Prefrontal Control

In the mammalian brain, information processing in specific sensory regions interacts with global mechanisms of multisensory integration under the control of the PFC. Emerging experimental evidence suggests that the contribution of multisensory integration to sensory perception is far more complex than previously expected [42,43]. Associative areas such as the prefrontal cortex, which receive and integrate inputs from diverse sensory modalities, not only affect information processing in modal sensory pathways through down-stream signaling but also influence contextual modulation and multisensory processing (Figure 1).

<figure><img src="image_13.png" /><figcaption>**Figure 1.** The prefrontal cortex receives and integrates signals from diverse sensory structures and pathways and controls information processing in, and interaction between, modal neural networks (visual, somatosensory, auditory, and olfactory) through down-stream signaling.</figcaption></figure>
Developmental mechanisms account for the interaction between the neuronal networks involved [50], with relevance for brain-inspired intelligent robotics, as will be discussed further later herein. In animals and humans, prefrontal downstream control is necessary in cases of conflicting sensory information, where signals from different modalities compete or provide incongruent input data [51]. The brain then needs to reach a probabilistic decision on the basis of top-down control signals (perceptual experience). However, another remarkable ability of the brain is its capacity to rapidly detect unexpected stimuli. Living beings depend on rapid detection of the unexpected when it is relevant (i.e., an alarm going off, for example), because it enables them to adapt their behavior accordingly and swiftly. Prefrontal control also explains why irrelevant sounds are incidentally processed in association with the environmental context, even though the contextual stimuli activate different sensory modalities [52]. This is consistent with brain data showing that top-down effects of the prefrontal cortex on contextual modulation of visual and auditory processing depend on selective attention to a particular sensory signal [53] among several coincident stimuli. Attempts to understand how functional interaction between different brain regions occurs through multisensory integration constitute a leading-edge research area in contemporary neuroscience [54]. Low-level brain representation of information is not enough to explain how we perceive the world. To enable us to recognize and adaptively act upon objects in the physical world, lower-level sensory network representations need to interact with higher-level brain networks capable of coding contextual relevance.

### 3. Brain Signatures of Perceptual Experience

It is still unknown how the brain generates short- and long-term memory signatures of perceptual experience, or which mechanisms allow these traces to be retrieved and updated on a regular basis during life-long brain learning and development (ontogenesis). Well before contextual modulation and context-sensitive neural mechanisms were identified in neural circuits of different species, Grossberg understood that they must exist and, considering the principles of unsupervised synaptic (Hebbian) learning [8], which had been demonstrated in low-level species such as *aplysia* [55], that they would have to be universal. In his early work on adaptive resonance [19], he proposed universal functional principles for the generation of short-term and long-term memory traces and their activation in context-sensitive retrieval processes. These functional principles exploit two mechanisms of neural information processing in resonant circuits of the brain, referred to as “bottom-up automatic activation” and “top-down matching”.

#### 3.1. Bottom-Up Automatic Activation

Bottom-up automatic activation is a mechanism for the processing and temporary storage of perceptual input in short-term and working memory. Through bottom-up automatic activation, a group of cells within a given neural structure becomes potentiated and is eventually activated when it receives the necessary bottom-up signals. These bottom-up signals may or may not be consciously experienced. They are then multiplied by adaptive weights that represent long-term memory traces and influence the activation of cells at a higher processing level. Grossberg [17] originally proposed “bottom-up automatic activation” to account for the way in which pre-attentive processes generate learning in the absence of top-down attention or expectation. It appears that this mechanism is equally well suited to explain how subliminal signals may trigger supraliminal neural activities in the absence of phenomenal awareness [56,57]. Learning in the absence of phenomenal awareness accounts for visual statistical learning in newborn infants [58] and non-conscious visual recognition [59], for example. Bottom-up automatic activation may generate supraliminal brain signals, or representational contents with weak adaptive weights, as a candidate mechanism to explain how the brain manages to subliminally process perceptual input [60] that is either not directly relevant at a given moment in time or cannot be made available to conscious processing because of a local brain lesion [59].
Grossberg [9,12,17,19] suggested that bottom-up activation may automatically activate target cell populations at higher levels of processing, as in the bottom-up activation of the PCF by sensory cortices [47,49], for example.

3.2. *Top-Down Matching*

Top-down expectations are needed to consolidate traces of bottom-up representation through mechanisms that obey three properties: (1) they select consistent bottom-up signals, and (2) they suppress inconsistent bottom-up signals. Together, these properties initiate a process that directs attention to a set of critical features that are consistent with a learned expectation. However, (3) a top-down expectation by itself cannot fully activate target cells. It can only sensitize, modulate, or prime the cells to respond more easily and vigorously if they are matched by consistent and sufficiently strong (relevant) bottom-up inputs. If this were not the case, top-down expectation would produce hallucinations of events that do not exist. Top-down expectations therefore do not activate but only modulate representations, as discussed here above in 3.4. Top-down representation matching is a mechanism for the selective matching of bottom-up short-term or working memory representations to already stored and consolidated (learned) memory representations (Figure 2). Subliminal bottom-up representations may become supraliminal when bottom-up signals or representations are sufficiently relevant at a given moment in time to activate statistically significant top-down matching signals [60]. These would then temporally match the bottom-up representations (coincidence). A positive match confirms and amplifies ongoing bottom-up representation, whereas a negative match invalidates ongoing bottom-up representation. Top-down matching is a selective process where subliminal representations become embedded in long-term memory structures and temporarily accessible for recall, i.e., a conscious experience of remembering.

<figure><img src="image_14.png" /><figcaption>**Figure 2.** In the ART matching rules, bottom-up signals from the environment activate short-term memory representations in working memory, which then, in turn, send bottom-up signals towards a subsequent processing stage at which long-term memory representations are temporarily activated (top left). These bottom-up signals are multiplied by learned long-term memory traces, which selectively filter short-term representations and activate top-down expectation signals (top right) that are matched against the selected representations in working memory. The strength of the matches determines the weighting of short-term representations (bottom) after top-down matching.</figcaption></figure>
3.3. *Temporary Representation for Selection and Control*

Grossberg's universal coding rules produce temporary and long-term brain signa-
tures of perceptual experience. They address what he called the attention-pre-attention
interface problem [9,12,17,19] by allowing pre-attentive (bottom-up) processes to use some
of the same circuitry that is used by attentive (top-down) processes to stabilize cortical
development and learning. Consistently, research on human cognition [61] has confirmed
that attention ensures the selection of contents in working memory, controlled by mecha-
nisms of filtering out irrelevant stimuli and removing no longer relevant representations,
while working memory contributes to controlling perceptual attention as well as action by
holding templates available for perceptual selection and action sets available to implement
current goals [61]. Top-down matching in its most general sense generates feed-back reso-
nances between bottom-up and top-down signals to rapidly integrate brain representations
and hold them available for a consciousness experience at a given moment in time. Non-
conscious semantic priming is explained on these grounds. Statistically significant positive
top-down matching signals produced on the basis of strong signal coincidences explain
why subliminal visual representations become conscious when presented in a specific
context, especially after a certain amount of visual learning or practice [60]. Conversely,
significant negative matches produced on the basis of repeated discrepancies generating
strong negative coincidence signals could explain why a current conscious representation is
suppressed and replaced by a new one when a neutral conscious representation is progres-
sively and consistently weakened by association with a strongly biased representation, as
in evaluative conditioning and contingency learning [57,58]. Some of the above-mentioned
functional properties require long-range connectivity in cortical circuits capable of gen-
erating what Edelman [62] called "reentrant signaling". Bottom-up representations that
activate specific structures of such circuits but do not produce sufficiently strong matches
to long-term memory signals will remain non-conscious [60]. Strong positive top-down
matching of selected representations will compete with weaker or negative matches and,
ultimately, be suppressed from conscious experience, as, for example, in cases where the
conscious integration of new input interferes with the conscious processing of anything
else [35,50]. Specific instructions telling subjects what to look for, or what to attend to,
in a visual scene may generate top-down expectation signals strong enough to inhibit
matching of other relevant signals at the same moment in time [31]. Top-down matching
generates neural computations of event coincidence [63]. Results from certain observations
of motor behavior without awareness [64] highlight the potential implications of negative
top-down matching for conscious control in learning. Individuals may become aware of
unconsciously pursued goals of a motor performance or action when the latter does not
progress well or fails. This could reflect the consequence of repeated negative top-down
matching of the non-conscious bottom-up goal representation and top-down expectation
signals in terms of either memory traces of previous successes or representations of desired
outcomes. Repeated and sufficiently strong negative matching signals might thereby trigger
instant consciousness of important discrepancies between expectation and reality [65].
Awake mammals can switch between alert and non-alert brain states hundreds of times
every day. The effects of alertness on two cell classes in layer 4 of primary visual cortex,
excitatory "simple" cells and fast-spike inhibitory neurons, show that for both cell classes,
alertness increases their functional (excitatory or suppressive) strength and considerably
enhances the reliability of visual responses [66]. In simple cells, alertness increases the
temporal frequency bandwidth but preserves contrast sensitivity, orientation tuning, and
selectivity for direction and spatial frequency. Alertness selectively suppresses the simple
cell responses to high-contrast stimuli and stimulus motion orthogonal to their preferred
direction of movement. This kind of conscious feedback control fulfills an important adaptive
function and has evolved in response to the pressures of intrinsically ambiguous and
steadily changing physical environments. The mathematical development and equations
describing ART resonant learning in its most generic form were made explicit in the Cohen-
Grossberg model [67,68], which will be detailed further here below with respect to the
development of adaptive intelligence in robotics.

4. Towards Adaptive Intelligence in Robotics

The concept of “resonant brain states” is central to ART. They arise from the self-
organizing principles of biological neural learning, whereby our brains autonomously
adapt to a changing world. Biological neural learning, unlike the learning algorithms
that fuel artificial intelligence, is driven by evolution, with a remarkable pressure towards
increasingly higher levels of consciousness across the phylogeny [69]. Pressure towards the
development of increasingly autonomous and adaptively intelligent forms of agency also
exists in the growing field of robotics, in particular neuronobotics [70]. Detailed descriptions
and equations describing the full span of potential for the development of autonomously
intelligent robots may be found in [71–74]. The most generic functional principle that ART
aims for has been termed “hierarchical resolution of uncertainty”. Hierarchical resolution
of uncertainty means that multiple processing stages are needed for brains to generate
sufficiently complete, context-sensitive, and stable perceptual representations upon which
successful action by intelligent agents can be based. The mathematical development and
equations describing ART resonant learning in its most generic form are inspired by the
principles of Hebbian neural (synaptic) learning and are given by the Cohen–Grossberg
model [67,68]. The latter is defined in terms of the following system of nonlinear differential
equations describing interactions in time t among and between neural activities xi, or
short-term memory (STM) traces, of any finite number of individual neurons or neuronal
populations (networks)

$$
\frac{dx_i}{dt} = a_i(x_i) [b_i(x_i) - \sum_j c_{ij} d_j(x_j)]. \quad (1)
$$

With symmetric interaction coefficients *cij* = *cji* for weak assumptions of state-dependent non-negative amplification functions *ai*(*xi*), self-signaling functions *bi*(*xi*), and competitive interaction functions *dj*(*xj*). Magnitudes for *i*, *j* = 1, 2, . . . , *n* and *n* may be chosen arbitrarily. Each population in (1) can have its own functions *ai*(*xi*), *bi*(*xi*) and *dj*(*xj*). One possible physical interpretation of the symmetric interaction coefficients *cij* = *cji* is that the competitive interactions depend upon Euclidean distances between the populations. Defined as in (1), the *i*th population activity *x* can only grow to become momentarily a “winner” of the competition at times *t* where the competitive balance [*bi*(*xi*) − Σ*j* *cijdj*(*xj*)] > 0. When [*bi*(*xi*) − Σ*j* *cijdj*(*xj*)] < 0, the given population is “losing” the competition.

The ART-inspired neural network architecture for multiple event coding, represented schematically here above, can be implemented by exploiting the properties and parameters of the system described in (1). This would permit implementing robot intelligence with capacities beyond reactive behavior. The selective filtering of relevant sensory input from a multitude of external inputs and the ability to autonomously generate adaptive sequences of memory steps to identify and recognize specific visual objects in the environment permit the control of external perturbations acting on a robot-object system. This is possible in a system like the one depicted above solely because of the internal dynamics of the resonant network. The ability to correctly identify objects despite multiple changes across time is a competence required in many engineering applications that interact with the real world, such as robot navigation. Combining information from different sensory sources promotes robustness and accuracy in place recognition. However, mismatches in data registration, dimensionality, and timing between modalities remain challenging problems in multisensory place recognition [75]. We may, as ART stipulates, define intelligence as the ability to efficiently interact with the environment and to plan for adequate behavior based on the correct interpretation of sensory signals and internal states. This means that an intelligent agent or robot will be successful in accomplishing its goals, be able to learn and predict the effects of its actions, and continuously adapt to changes in real-world scenarios. Ultimately, embodied intelligence allows a robot to interact swiftly with the environment in a wide range of conditions and tasks [76]. The ART model made explicit here above in (1), a
Hebbian-learning-based and mathematically parsimonious system of non-linear equations, can be directly implemented to enable intelligent multi-event coding across time *t* (Figure 3) for robot control by adaptive artificial intelligence (neurons or neural populations).

<figure><img src="image_15.png" /><figcaption>**Figure 3.** ART-inspired neural network architecture for adaptively intelligent event coding across time *t*.</figcaption></figure>

**5. Discussion**

Grossberg's universal coding rules allow for learning in a non-stationary, unpredictable world, whereas the traditional machine learning approaches assume a predictable and controlled environment [2]. Unlike passive adaptive filters [77], they enable self-organized unsupervised learning akin to biological synaptic learning [2,4,5,8,55]. The ART matching rules actively focus attention to selectively generate short- and long-term brain signatures of critical features in the environment, which is achieved by dynamic, non-passive, steadily updated synaptic weight changes in the neural networks [9,12,17,19]. The top-down control of selective processing involves the activation of all memory traces to match or mismatch bottom-up representations globally using *winner-take-all* best-match criteria. Neural network architectures driven by the ART matching rules do not need labeled data to learn, as previously explained in [2]. In short, the Grossberg code overcomes many of the computational problems of back propagation and deep learning models. Equipping cognitive robots with artificial intelligence that processes and integrates cross-modal information according to such self-organized contextual learning ensures that they will interact with the environment more efficiently, in particular under conditions of sensory uncertainty [4,78]. The universal ART matching rules are directly relevant to a particular field in robotics that is motivated by human cognitive and behavioral development, i.e., developmental robotics. The goal is to probe developmental or environmental aspects of cognitive processes by exploring robotic capabilities for interaction using artificial sensory systems and autonomous motor capabilities on challenging environmental platforms [79]. As illustrated here in this paper, low-level sensory and high-level neural networks interact in a bottom-up and top-down manner to create coherent perceptual representations of multisensory environments. Similarly, bottom-up and top-down interactions for the integration of multiple sensory input streams play a crucial role in the development of autonomous cognitive robots by endowing agents with improved robustness, flexibility, and performance. In
cases of ambiguous or incongruent cross-sensory inputs, for example, biological inspiration plays a major role. Autonomous robots with odor-guided navigation [80] can benefit from multisensory processing capabilities similar to those found in animals, allowing them to reliably discriminate between chemical sources by integrating associated auditory and visual information. Cross-modal interaction with top-down matching can enable the autonomous learning of desired motion sequences [81], matching expected outcomes from audio or video sequences, for example. Approaches to multisensory fusion in robotic systems directly inspired by the distributed functional architecture of the mammalian cortex have existed for some time [82]. Biological inspiration exploiting top-down cross-modal processing is mandatory for autonomous cognitive robots that acquire perceptual representations on the basis of active object exploration and groping. By actively processing geometric object information during motor learning, aided by tactile and visual sensors, it becomes possible to reconstruct the shape, relative position, and orientation of objects. Service robotics is a fast-developing sector that requires embedded intelligence in robotic platforms that interact with humans and the surrounding environment. One of the main challenges in this field is robust and versatile manipulation in everyday life. Embedding anthropomorphic synergies into the gripper's mechanical design [83] helps, but autonomous grasping still represents a challenge, which can be resolved by endowing robots with self-organizing multisensory adaptive capabilities, as discussed here above. Combining biological neural network learning with compliant end-effectors would not only permit optimizing the grasping of known deformable objects [84], but also help intelligent robots anticipate and grasp unforeseen objects. Bottom-up activation combined with top-down control gives robots the capability to progressively learn in an ever-changing multisensory environment by means of self-organizing interaction with the environment (Figure 4). Implementing multisensory memories in robotics in such a way permits equipping intelligent agents with sensory-cognitive adaptive functions that enable the agents to cope with the unexpected in complex and dynamic environments [85]. On the other hand, a lack of multisensory perceptive capabilities compromises the continuous learning of robotic systems because internal models of the multisensory world can then not be acquired and adapted throughout development.

<figure><img src="image_16.png" /><figcaption>**Figure 4.** Adaptive resonance as a universal principle of biological learning gives intelligent agents the capability to cope and evolve with ever-changing multisensory environments on the basis of self-organizing adaptive behavior (interaction with the environment).</figcaption></figure>

Adaptive resonance is a powerful concept that provides model approaches for a multitude of human interactions. The relationship between the physical mechanism of
resonance and its biological significance in the genesis of perceptual experience in neural
networks across all species, from mollusks to humans, makes it a powerful concept for
human-robot interaction at all functional levels and within a wider cultural and scientific
context. Resonant brain states, established on the basis of matching processes involving
top-down expectations and bottom-up activation signals, drive all biological learning at
lower and higher levels. Learning in biological neural networks is by nature unsupervised
and best accounted for in terms of competitive *winner-take-all* matching principles [86,87].
A resonant state is predicted to persist long enough and at a high enough level of activity
to activate long-term signatures of perceptual experience in dedicated neural networks.
This explains how these signatures can regulate the brain's fast information processing,
observed at the millisecond level, without any awareness of the signals that are being
processed. Through resonance as a mediating event, the combination of universal matching
rules and their attention-focusing properties makes learning and responding to arbitrary
input environments stable. In the mammalian brain, such stability may be reflected by
the ubiquitous occurrence of reciprocal bottom-up and top-down cortico-cortical and
cortico-thalamic interactions [88].

**6. Conclusions**

Well before contextual modulation and context-sensitive mechanisms were identified
in neural circuits of different species, Grossberg understood that they had to exist. The
principles of unsupervised synaptic (Hebbian) learning had been demonstrated in low-
level species such as *aplysia*, pointing towards universal principles of perceptual coding.
In his earliest work on adaptive resonance, Grossberg laid the foundations of univer-
sal functional principles of neural network learning for the generation of brain traces of
perceptual experience and their activation by context-sensitive, dynamic, self-organizing
mechanisms producing resonant brain states. Equipping cognitive robots with artificial
intelligence based on adaptive resonance and processing and integrating cross-modal infor-
mation in self-organized contextual learning will produce intelligent robots that interact
with complex environments adaptively and efficiently, in particular under conditions of
sensory uncertainty.

**Funding:** This research received no external funding.

**Acknowledgments:** Support from the CNRS is gratefully acknowledged.

**Conflicts of Interest:** The author declares no conflict of interest.

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Article

# The Psychometric Function for Focusing Attention on Pitch

Adam Reeves

Department of Psychology, Northeastern University, Boston, MA 02115, USA; reeves@neu.edu

**Abstract:** What is the effect of focusing auditory attention on an upcoming signal tone? Weak signal tones, 40 ms in duration, were presented in 50 dB continuous white noise and were either uncued or cued 82 ms beforehand by a 12 dB SL cue tone of the same frequency and duration as the signal. Signal frequency was either constant for a block of trials or was randomly one of 11 frequencies from 632 to 3140 Hz. Slopes of psychometric functions for detection in single-interval (Yes/No) trials were obtained from three listeners by varying the signal level over a 1–9 dB range. Plots of log(d') against signal dB were fit by linear functions. Slopes were similar whether signal frequency was constant or varied, as found by D. Green. Slopes for uncued tones increased by 14% to 20% more than predicted by signal energy (i.e., 0.10), as also found previously, whereas slopes for cued tones followed signal energy corrected for an 8 dB sensory threshold. That pre-cues help attention focus rapidly on signal frequency and permit listeners to act as near-ideal detectors of signal energy, which they do not do otherwise, supports a key hypothesis of Grossberg's ART model that attention guided by conscious awareness can optimize perception.

**Keywords:** attention; audition; energy; attention

## 1. Introduction

How well do listeners focus attention on a particular signal frequency? Green [1] and others [2–6] have shown that the threshold for detecting pure tones in broadband noise is higher, by about 3 dB, when listeners do not know at what frequency the signal will be presented than when they do know. The current research was undertaken to elucidate this improvement in perception due to foreknowledge. Here, the term ‘frequency’ describes the pitch in Hz, and ‘level’ describes the amplitude in decibels (dB). The term ‘focusing attention’ describes both the listener’s objective task and his or her phenomenal awareness of the task, but not, in general, what focusing does. A general scheme for how attention can prime a signal by suppressing unwanted information is provided by Grossberg’s ART theory [7] (p. 18); see Dresp-Langley [8]. ART requires the signal to be learnt, as resonance and top-down matching with memory is required, and in the psycho-acoustic literature cited here, this is the case: only experienced listeners who have memorized the possible tones are used. In the present context, it is possible to formulate a specific hypothesis, namely, that knowing the signal frequency allows the listener to focus attention in advance on the signal’s *critical band* (CB), the band of tones around the signal which interact with it [9]. Focusing on the signal CB suppresses noise from non-signal CBs and so increases detectability (d') as compared to not knowing the signal in advance [10], a form of suppression which has been investigated physiologically in primates [11].

Experimentally, providing the same signal in every trial permits the listener to focus attention on the signal CB, whereas varying the signal at random across trials does not. Green [1] compared these two conditions, which I will term *const* when signal frequency is constant for a block of trials and *var* when signal frequency is varied. (In *var*, signal frequency is typically selected at random across trials from between 5 and 22 possible frequencies, each at least one CB from the next.) The listener’s task in both *const* and *var* is to detect weak, brief (<350 ms) signals in continuously present wide-band noise that covers the range of possible signal frequencies. Such wide-band noise is convenient

**Citation:** Reeves, A. The Psychometric Function for Focusing Attention on Pitch. *Information 2023*, *14*, 279. https://doi.org/10.3390/info14050279

Academic Editors: Gordana Dodig-Crnkovic, Luiz Pessoa and Birgitta Dresp-Langley

Received: 10 February 2023

Revised: 20 March 2023

Accepted: 2 May 2023

Published: 9 May 2023

<figure><img src="image_17.png" /></figure>

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
in that it elevates individual listeners' thresholds to the same level, within a dB or so, over a wide range of frequencies, making the experimental measurements possible in uniform conditions; this is not so for thresholds in silence, which vary idiosyncratically over individuals and frequencies.

How well attention can be focused in *const* has also been determined using the 'probe-signal' method of Greenberg and Larkin [12]. In this method, which differs from Green's, so-called 'probe' tones are occasionally presented at unexpected frequencies above or below the (constant) signal frequency. Signals and probes are near the threshold, and, apart from their frequencies, they are identical in duration and quality. Probes inside the signal CB are heard in proportion to their distance from the signal frequency, defining an 'attention band' around the signal [13–17]. Distant probe tones, those outside the signal CB, are not heard, being attenuated by up to 8 dB [17]. Although the close match between the attention band and the CB fails below 500 Hz, when the attention band more closely follows a narrower auditory filter [14], the argument of this paper will be phrased in terms of CBs as signals below 632 Hz were not used. When tones are very brief (20 ms), focusing fails to exclude neighboring CBs, and the attention band widens and peaks just below the signal frequency [15], but with longer durations, focusing on single CBs is successfully accomplished.

Proof that focusing primarily suppresses distant probe tones rather than enhancing the signal tone was provided by Scharf, Magnan, and Chays [18], who compared thresholds before and after vestibular neurotomy. This operation randomly severs the olivio-cochlear bundle, which mediates cortical feedback to the outer hair cells of the cochlear [19,20]. After neurotomy, patients lost the ability to suppress probes away from the signal CB but showed no change in signal threshold [18]. (Interestingly, the hearing of speech in noise is unaffected, speech being broadband so there is no particular 'signal' frequency.) Here, I assume that the suppression of non-signal CBs is the primary effect of attentional focusing, although Tan et al. [21] also reported a minor 2 dB signal enhancement due to focusing.

When a wide-band noise is applied, the listener who can focus on the signal CB (in *const*) will suppress noise from non-signal CBs, as evidenced by the probe-signal data just discussed, but a listener who attends to all possible signal frequencies (in *var*) cannot suppress noise, as all CBs potentially contain a signal. Thus, the detection mechanism in *var* will sum more noise than that in *const*, and the detectability of the signal (d') in *var* will fall below that in *const*.

Note that noise suppression may be total, exemplifying ‘exclusion’ in the terms of Lu and Dosher [22], or partial, exemplifying ‘attenuation’, as in Treisman [23]. As Green [1] pointed out, given the wide range of frequencies he employed, excluding all the noise in *var* predicts a 10 dB loss relative to *const*, not the 3 dB loss he obtained. Green’s suggested explanation for this discrepancy was that listeners fail to focus completely on the signal CB even in *const*, so noise from non-signal CBs is attenuated rather than excluded.

One aim of the current research was to test Green's suggestion by validly pre-cuing the signal frequency. Frequency *var* versus *const* was crossed with validly cueing versus not cuing, a *var*/*const* × *valid cue/no cue* design adopted from Richards and Neff [24]. Validly cuing the signal frequency helps the listener focus on the signal frequency [2, 10, 16, 25], so any uncertainty about the signal frequency should be reduced, perhaps even eliminated, by cuing.

Note: Richards and Neff [24] had crossed frequency certainty with cuing, as in the current study. They used an 'informational mask' consisting of a multitone array of tones all outside the signal CB. In *const*, the signal was always 1000 Hz, and the mean benefit of a valid pre-cue was 6.5 dB compared to no cue. In *var*, the signal was a random one of five tones, and the benefit of a valid pre-cue of the same frequency as the upcoming signal averaged 13.5 dB. They argued that attention can be focused within 50 ms, as longer cue-signal ISIs hardly increased the effectiveness of the cue. They did not measure slopes, but their cue effect (in dB) was encouragingly large. However, a multitone masker encourages attention to focus on non-signal CBs, as shown by their additional finding that pre-cuing