Technologies of the Mind


1. Technology as the Creation of Meaning.

The Mind’s technology (τέχνη – skill; λόγος – meaning) is the skill to perform the function of creating meanings about the world. Any technology has a specific function, i.e., activity with a purpose. The teleology (τέλος – goal) of the signal transduction process that we call the Mind is to form a reality model for adaptation to the environment and the implementation of purposeful activity.

Teleological Transduction Theory is about how the Mind models the world. Building such a concept faces a difficult task: the Mind has to model itself. This fundamental epistemological problem has led many to take the view that it is unsolvable in principle. What can be a way out? We may build analogous models of complex and unclear systems using known and less complex ones based on the similarity of a functional set or part of it.

What technologies come to mind when we think of the Mind’s technology? If we look at it as the signal transduction process, the obvious analogy would be artificial signal processing devices. The technology may be more or less complicated, but this is just a difference in level. Simple technology can serve a similar function, and methods can be the same, but with a smaller range than a complex technology. Also, the substrates that perform similar function may be radically different. The analogy does not mean identity between the analogous system and the target system (the object of research).

Being to a large extent, an analogous model, the TTT takes what our Mind created as external tools for the signal transduction and looks at them with the aim of modeling the Mind itself. This chapter contains examples of some modern artificial devices and algorithms that they use to process signals, create their representations, compare the result with the database, and put it on the ‘shelf’ of the overall model. It is the essence of the living Mind’s job, so artificial technologies may provide insight into how it works. The chapter also offers hypotheses about the physical nature of the representations created by our brain and of a mechanism that allows it to combine a vast number of code patterns while keeping them differentiated.

2. Signal Reconstruction Technology.

The Mind is not a perceptual mirror that just reflects data solving a direct problem (measurement of signals’ parameters), but an active function that solves the inverse problem of signal reconstruction, creating representations based on data. The chapter shows how the universal algorithm proposed in the previous volume elegantly solves the direct and inverse problems. Taking a visual modality as an example, it gives a detailed physiological account of the technology implementation. Similar schemes for the rest of the perceptive systems are located in the appendices to the book.

3. Technologies for Overcoming Physical Limitations.

The title of the chapter may be misleading, so it needs clarification. For some, it may sound as if it is about going beyond the body’s physical boundaries, about the transcendence of the Mind (Soul, Psyche). Many religious concepts and even theories that call themselves scientific consider the Mind as something immaterial, capable of existing by itself outside of any physical carrier. TTT, on the contrary, takes a physical and technological approach to the Mind.

This chapter is actually about how living systems solve an obvious technological problem: the inclusion of a potentially infinite world of signals into a limited volume of channels for their reception and processing, as well as storage in a limited volume of a substrate as a carrier of encoded information. In general, the nervous system’s entire evolution can be called a process of overcoming the above limitations.

The chapter contains descriptions of solutions and provides examples of how the physics of brain technologies are manifested in the physiology of specific nervous system elements. It also gives a mathematical model of the Mind’s algorithm showing how it performs the creation of a reality model with constant updating based on incoming data and the removal of part of the accumulated information to prevent possible overload of signal processing and memory systems. It also offers a hypothesis about the technological functioning of memory.

4. The Mystery of Motion Control Technology.

The chapter goes back to the question of the goal of all these technological solutions of the Mind. Building a model of reality is not an end to itself. The final teleology is to act based on this model. The nervous system specializes in controlling the body, organizing purposeful movement, and manipulating objects of the environment. But here comes the inevitable question: how does it perform the function? It is a technological question that has physical and physiological aspects.

The standard scheme used in most models can be called ‘puppet on the strings.’ In a nutshell, the picture of how the nervous system controls the body in such a paradigm looks like this: there are communication strings (neural circuits) through which commands are transmitted from top to bottom, and reports are received from bottom to top. It is only part of the truth. The fundamental question is how the commands and responses are formed and transmitted.

It would seem that everything is simple: there is a spike and its propagation, an electrical impulse and its transmission through wires. However, our ‘wires’ are physically not fast enough to transmit complex information throughout the body in an almost instant online mode (in the time frame of one or two spikes) if we assume that control goes as a linear transmission of impulses from top to bottom and from bottom to top. It is a physical fact that has long been known, but mainstream models just ignore it and keep on talking about ‘spike trains’ that manage the body. So, the old model contradicts reality, but the problem is that there is no other model.

In addition to the temporal aspect problem, there is also a spatial paradox. These ‘strings’ have one property that is striking from the point of view of the linear paradigm. If we proceed from such a management model, then nature made the wiring strangely: the bundles of wires do not go in parallel paths back and forth but converge and diverge. They converge towards the center, diverge there again, converge again on the way to the periphery, and again diverge at the periphery. They are parallel and differentiated at one stage, and at another stage, they are sequential and mixed. This applies to both motor and sensory pathways. It is a physiological fact that has been known for decades, but mainstream models seem to ‘forget’ it when it comes to describing the brain’s work. Why? Because there is no explanation of the fact from a physical and technological point of view in the old models.

How do ‘spike trains’ get so fast back and forth? How do they manage not to collide when they get on one track at the same time? How do they manage to keep identity while staying on it and after separating to their destinations? These two aspects are the brain’s motion control technology mystery that has not been resolved yet. These two aspects concern not only motor commands but any representations in all sensory modalities. The chapter describes the shortcomings of some models and prepares the ground for a new direction of thought movement in explaining how things in the brain move to get us moving.

5. The Centipede Problem.

There is another issue that needs to be resolved if we want to understand how the brain controls the body’s movement. It can be called the ‘centipede problem’ – managing a massive number of motor effectors so quickly and efficiently that it gives the impression of ‘automatism.’ The parable of a centipede that cannot move if it thinks about movement, on the one hand, is accurate since movements really should be faster than thoughts about them. But, on the other hand, it is incorrect since any action, even the quickest and involuntary (outside of conscious control), involves central processing units and does not loop in the periphery. The notion of ‘reflex arc’ exists in neurophysiology for dozens of years but does not correspond to the actual data about the brain areas involved in movements.

Organisms more complex than a centipede have a different order of effectors’ quantity: there are not hundreds, but thousands of them. How does a living system combine multiple parameters and reduce the degrees of freedom to a state of controllability? Can a linear communication system (‘puppet on strings’) control such a process? The question requires a specific technological answer. If the linear scheme seems to be unrealistic, then what is the actual algorithm of the process? The chapter contains hypotheses about the algorithm and physical mechanism that allow for motion control’s observable speed and efficiency.

6. Brain Waveguides and Antennas.

The chapter suggests a new look at brain circuits. The old idea that they are a bundle of wires that transmit electrical impulses as ‘spike trains’ is technologically simple. Still, it leads to conceptual dead-ends and contradicts the reality of the brain’s complexity, speed, and efficiency of information flows. But if we look at the axons and dendrites not as a simple power line but as an active medium for the propagation of wave patterns, then, firstly, some points become clearer, and secondly, we no longer need to ignore the facts to preserve the familiar model.

This hypothesis explains the speed, the riddle of convergence and divergence of neural pathways, and even the complex morphology of the axons and dendrites. Why would nature need such a complex structure if the task is simply to transmit an electrical signal through a wire? Nature, or rather, the process of evolution as an increase in the efficiency of performing tasks, does not complicate without necessity. The chapter explores the details of neural circuits and shows that none of these details are redundant. It offers technologically sound hypotheses about the physics of neural communication.

7. Factors of Thought Efficiency and Speed.

The chapter takes us step by step through technological issues of neural communication and shows how living systems solve this engineering task. It is a combination of factors: morphology, topology, oscillatory parameters of the elements; the use of the efficient energy transfer and interaction mechanism; good algorithm and hybrid coding scheme; the component and composite signal processing and transmission solutions; multiplexing and demultiplexing technologies.

8. The Solution to the Centipede Problem.

Finally, we come to the solution of the technical task of any living system: reducing the massive number of degrees of freedom (parameters of the signals and movement) to a state of controllability. It is not an easy task for a centipede to move all hundred legs in harmony, and it is even more difficult for an elephant to move all 40,000 muscles in its trunk. Moreover, each muscle is made up of hundreds of motor elements (fibers). The product of the number of such elements by the number of muscles participating in one simple movement is the number of degrees of freedom, and it is enormous. Managing all of them is not a miracle but the accomplishment of the Mind’s technology. But when we look at how mainstream theories model it with the ‘average firing rate of spike trains through the wires,’ it sounds like a miracle because they are not physically and technologically plausible. Their simplicity creates an illusion of an explanation and does not stand up to the complexity of the described system.

The chapter considers some old and modern theories about motion control, shows where their dead-ends are, and offers a way out. The main factors of the solution of the ‘centipede problem’ were described in the hypotheses provided earlier in this volume and the previous one. Here they are just illustrated in the light of movement implementation.

9. The Universal Processor.

This chapter continues to ponder the issue of the brain’s efficiency. It focuses on the plasticity and dynamic structure of network topology. The system elements have specialization but, to a great extent, are interchangeable, especially zones of the neocortex. This is due to the largely homogenous structure of the substrate and to the physical nature of representations that allow them to be associated with the elements that create them, but also to be free from them in the sense of the possibility of changing localization. Dynamism means a readjustment of the system elements as signal processors to new patterns. The cortex is a universal processor, and the chapter shows breathtaking examples of its high plasticity.

10. Solving the Hard Problem of Consciousness.

Here we go back again to the centuries-old debate about the nature of the Mind. If we say that the Mind is immaterial, we do not have any issue: we cannot study something nontangible. If we say that the Mind is material, we have to show how it physically works. Easier said than done. That is why one of the modern philosophers, David Chalmers, called it ‘the hard problem of consciousness.’

Another philosopher, Joseph Levine, wrote: “While I think this materialist response is right in the end, it does not suffice to put the mind-body problem to rest. Even if conceivability considerations do not establish that the mind is in fact distinct from the body, or that mental properties are metaphysically irreducible to physical properties, still they do demonstrate that we lack an explanation of the mental in terms of the physical.”

According to Levine, materialistic theories of consciousness so far could not explain the existence of qualia (representations, subjective experience) from the physical point of view, so the explanatory gap remains. He is right: one cannot simply say that the connection between brain activity and the presence of consciousness is a kind of ‘law of nature.’ It is necessary to show why this connection exists, what it is based on, and its mechanism. If a theory cannot reveal the mechanism of representations, it has an explanatory gap. The chapter summarizes how the proposed TTT model covers the gap and proceeds to develop hypotheses about what representations are physically and how they are produced by the brain technologically.

11. Physics, Physiology and Technology of Memory.

As a continuation of building conceptual bridges to cover explanatory gaps, this chapter takes the issue of memory. Representations are not only created by the brain but stored by it. But is it some kind of storage where things ‘lie’ and wait to be ‘picked up’? Is our memory analogous to artificial technologies, where there is a specific block that stores code patterns? Or is there some other technological solution that would explain the systemic character of our memory?

Extraction from our memory can proceed in any way: sequentially, parallel, cross-wise (associative), and ‘jumping’ (spontaneous transitions). With all the physical restrictions, the depth seems inexhaustible. It manifests itself both in the possibility of voluntarily extracting the required representation (thought, image, sensation, movement) and in the involuntary emergence of what seemed irrevocably gone.

Our memory is very stable and dynamic at the same time. A truly systemic pathology is required for a substantial violation of the process to occur. Even in rare cases of complete anterograde and retrograde amnesia, there is an inability to form new and reproduce old representations at an explicit level, but the implicit sensory-motor memory is preserved. A global stop occurs only with the complete destruction of the substrate (death). It is impossible to ‘take out’ a memory block and stop its work because there is no such block. There is no ‘hard drive’ or ‘flash drive’ to provide local storage of ‘files.’ Memory is a system function and a system process.

This formulation is too general and not new at all. To say that memory is spread and not local simply states a fact that has been known for many decades. A physical explanation of all these ‘magical’ properties of our memory is required. The chapter contains the hypotheses on how our memory works. It shows what processes are capable of creating the observable capacity, speed and multi-level complexity of our memory. It solves the riddle of how representations with all the intricate details of the parameter space are formed and reproduced almost instantly. There is no need to accumulate and read the average spike rate or successive spike bits along a linear chain. Information can be generated, written, and read within one system clock cycle of milliseconds.

The main properties of memory created by this technology are the combination of differentiation and integration, sequential and simultaneous processing, layering and associativity, flexibility, dynamism, speed, efficiency, resistance to local damage of the carrier. Properties of our memory are built in the very physics of the process that the chapter considers.