Algorithm of the Mind


  1. Discreteness and Continuity of the Mind.

Normally, the mind perceives the surrounding world and the internal state as a continuous process, although during the primary processing of signals they are split into discrete components. This is the essence of the mind as the signals transduction from continuous waves into a discrete code and back into continuous representations of these signals. The chapter proposes hypotheses on how the brain manages to do it and what algorithm and mechanism it uses. Taking the visual modality of perception as an example it shows how this marvel of biotechnology performs continuous-discrete-continuous transformations. Our brain does the job so well that to this day neuroscientists are divided into camps that argue whether perception is discrete or continuous. The secret is simple: instead of ‘or’ we should put ‘and.’

  1. Filters of the Brain.

The chapter looks at the basic principles of signal processing and shows how the brain uses them in performing the analysis and subsequent synthesis of the signals to compile the ongoing picture of the world. The chapter contains the graphical representation of the mind’s algorithm and details each signal-filtering process step. It offers an intuitive visual and mathematical model of what functions various brain filters perform in sampling, quantizing, modulating, evaluating and integrating signals of the outer and inner world. This approach to the nervous system elements offers the outline of a new brain map focusing on the technological chain of signal transduction.

  1. The Time Machine of the Mind.

The chapter is devoted to two fundamental concepts that have been at the center of philosophy and physics for ages: space and time. The views on this matter can be divided into two categories. Substantialism says that space is a kind of global container in which all objects are located, and time is a container of events. Relationalism says that space is relations between objects, and time is the dynamics of their states. TTT proceeds from the essentially relationistic hypothesis that space is how our brain measures objects and their relations, and time is a measurement of dynamics. The chapter describes how our brain creates the ‘nows’ of time and the algorithm that binds them into the past, present and future as an integrated timeline. It also contains examples of how these hypostases of time interact and change depending upon the state of the brain.

  1. Encoder and Decoder Rolled into One.

In any system of interacting elements, the question arises about creating and transmitting information, about the language and means of communication. In an artificial system, a programmer defines this language. The chapter shows how living systems solve the internal coding problem without any external programmer using the self-learning algorithm where the encoder and decoder are combined in one operationally closed loop. But the question about the code of the system inevitably arises. This question has haunted neuroscience for a century. There are many proposed versions of the neuronal code. But they have major flaws: internal contradictions and discrepancies with empirical evidence. The chapter contains the hypothesis about the neural code and takes a new look at its nature.

  1. Hybrid Analog-Digital Brain.

The mainstream neuroscience models of the neural code consider it to consist of identical neural spikes and just provide different suggestions about their counting. Thus, the spikes are considered discrete symbols of a digital code. Unfortunately, this approach did not lead to the deciphering of the code despite the efforts of generations of researchers. Perhaps this is the result of the fallacy of the original idea. TTT takes a different view of the mind as a signal-encoding process and of the brain as the encoding device. The chapter considers the advantages and limitations of both digital and analog computing. It proposes the hypothesis that the brain exploits both ways of coding and is in essence an analog-digital device. Step by step it reveals the hybrid signal transduction paradigm and gives clear examples of technological solutions used by the brain in different perception modalities.

  1. Symphonic Neural Code Hypothesis.

Some mainstream models of neural code are technologically absurd and contradict the realities of brain efficiency and speed. Some cover just part of the observed phenomena and fail at explaining the others. The chapter explains these shortcomings and gives concrete examples from empirical research. As the way out of a conceptual impasse that has lasted for decades, the chapter develops the hypothesis of the Symphonic Neural Code based on the idea of the hybrid nature of neural computing. This hypothesis reveals the mystery of the high performance, speed and efficiency of the brain, which cannot be provided by coding with an average spike tempo (firing rate theory) or with just the temporal structure of a spike sequence (temporal code theory).

  1. Brain Logistics.

This chapter begins the journey into the intricacies of intra- and interneural communication.

  1. Brain Logic.

This chapter uncovers the logic of an algorithm that produces the result as a structured energy flow (encoded information).

  1. Evolution of Brain Information Technology.

Here the reader is taken for a trip into the billions of years of evolution during which living systems have been improving their signal processing technologies: internal communication channels, and methods of encoding, transmitting, decoding, storing and reproducing information. For living systems, solving this engineering problem is a survival task.

  1. Neuron as a Signal Processor.

If we proceed from the primary hypothesis that neurons are the main actors in the play that we call the mind, we need to find out how they perform the act. The chapter looks at a neuron from physiological, physical, technological and informational points of view showing how this element of the system performs the encoding-decoding function. It also contains the graphical representation and detailed description of the algorithm that the brain uses at the level of a single cell and even at the level of a single ion channel of the cell membrane.

  1. Bridges of the Mind.

Here the model begins to explore how the brain manages to bridge the discrete world of samples of the incoming signals with the continuous world of the final product (representations and reality model in general). How does the brain interpolate and combine the samples? How does it reduce interpolation errors and inevitable distortions? How does it walk the tightrope between oversampling and undersampling? The chapter contains the hypotheses that look at the issue from the physical perspective (what mechanisms are used) and technological (what algorithms are used). It offers intuitive verbal and graphic descriptions and mathematical modeling of the process.

  1. Brain Tunings.

This chapter is devoted mainly to the part of the technological chain of the brain that stands in the middle of the signal processing between sampling and integrating. It is about the modulating part of the brain filters. The chapter shows how the brain does amplitude, frequency, width and phase modulation of the external and internal signals. After describing the whole technological chain, the study returns to the general definition of the mind given in the previous part and offers a new version that gives a more detailed picture of the process.

  1. Butterfly Algorithm.

The brain balances between tolerance and intolerance to uncertainty (information entropy). On the one hand, the mind must be ready for surprises (prediction errors as the difference between expectation and result). On the other hand, it strives to reduce surprises and create a reality model with high explanatory and predictive power. On the one hand, the mind has to decrease surprise by expanding the information field as the range of search and comparison. On the other hand, expansion is possible only through a collision with the new and uncertain. Moreover, complete certainty is impossible due to the dynamism and potential infinity of signals of the environment. The brain cannot shut off and totally exclude external or internal bodily signals so that nothing new is coming in. How does the brain solve these dilemmas? The chapter takes us through the details of algorithm configuration and settings of the brain filters that allow striking the balance.

  1. The Amazing Self-Learning Machine.

The brain is an amazing self-learning ‘machine’ that updates its model of reality whenever needed. Otherwise, we wouldn’t survive in this dynamic world. The chapter shows how the algorithm proposed within the TTT allows the brain to solve the problems of survival and adaptation based on the accumulated database and generated predictions about trends in the environment signals that are constantly evaluated against incoming data with the assessment of the difference and its effect on the state of the system. Thus, the reality model is constantly tested for efficiency, adequacy and adaptability. However, a good algorithm is part of the story. To understand how the brain processes different types of data, we need a model of technological solutions within the chains of this algorithm, and a model of a physical mechanism that allows the algorithm itself and all chains to be implemented. We need a unified concept of physics, physiology and technology of the process that we call the mind. It will be developed further in subsequent volumes of the series.