One thing that has always fascinated us is our mind. It is the only thing that we can be certain of existing, yet we do not know what it is. This is in contrast to things outside the mind, which we cannot be certain that they really exist – they may be just illusions – yet, apparently, we know what they are. We also have a lot of information about them. For example, we can tell that an apple is a material object – a fruit with a fairly round shape, red/green color, sweet smell, delectable taste, and a lot of nutrients. Even something that is immaterial, such as an electromagnetic wave, we can tell that it has the dual nature of being a wave and a particle, travels at the speed c regardless of measuring frames, can dislodge electrons from atoms, etc.; we can even write formulas to describe its properties. Moreover, for both things, we can answer the questions of why and how they occur. We cannot do such things to the mind. For the mind, what we know is only that it is non-material, can do various mental activities, such as sensing stimuli, thinking, and executing motor commands, and has some observable functional properties, such as being private, subjective, and representational [1-6]. But we do not know what its exact nature is, why it occurs, how it occurs, and why it is that, even if it is us, we cannot easily answer these questions. This theory attempts to answer these questions with scientific evidence and finds that they can be answered by analyzing the physical properties of the mind, qualia, and consciousness.
However, before the attempt to solve this puzzle can begin, it is to be noted that many specific terms will need to be used in the process but that some of them are ambiguous and can have varied meanings in the literature. To avoid misunderstandings, these terms will be defined what they mean in this theory at appropriate points. All the definitions such defined are intended to be the working definitions just for use in this theory. Thus, caution should be exercised if these terms are compared with the same terms, which may have different meanings, in the literature. Some of the terms will be used from the beginning and throughout the theory and will be defined in this chapter as follows.
The mind is a non-material entity that exists in an animal with a nervous system and that functions to
– sense signals from its environment (such as light, sound, and tactile stimuli), from its own body (such as proprioceptive stimuli, vestibular stimuli, and pain from internal organs), and from within itself (such as emotion, thought, and memory);
– operate (such as integrate, store, and retrieve) signals, resulting in various mental processes, both conscious (such as solving problems, remembering things, and experiencing emotions) and unconscious (such as unconscious control of muscle tone and balance, unconscious control of breathing, and unconscious control of sweat secretion); and
– send signals between its parts (such as between the sensory perception parts, the emotion part, and the consciousness part) and to its effectors (such as striated muscles, smooth muscles, and glands) to control its own body functions and/or to respond to its environment.
An immature mind is a non-material entity that exists in an animal with a nervous system and that can function to do some, but not all, of the listed activities above because the animal is still in a developing stage, such as a fetus.
A partially-functioning mind is a non-material entity that exists in an animal with a nervous system and that can function to do some, but not all, of the listed activities above because the animal is being in a sleep stage, suppressed by a pharmacologic or toxic agent, or affected by a pathologic condition such as a cerebral concussion, brain tumor, stroke, dementing disorder, or congenital brain defect.
This theory is about the mind as defined above. The eventual conclusions, implications, predictions, and other statements that are valid for this kind of mind are also valid for an immature mind and a partially-functioning mind, excluding the non-functioning part(s) of that mind, unless stated otherwise.
To be noted here is that this theory is about the mind as specifically defined – a non-material entity that exists in an animal with a nervous system and that functions to do the activities listed above. The definition does not include possibly-existing, non-material entities that function to do the above-listed activities but reside in
– animals that do not have a nervous system, such as sponges and Trichoplax
– other kinds of living organisms, such as bacteria, fungi, and plants, or
– non-living things, such as a rock, a computer, a robot, a weather system, and the dynamic photosphere of the sun.
Thus, even if sponges, bacteria, plants, computers, robots, or some other entities above function to sense signals from the environment, operate signals, and send signals between their parts and to their effectors [7-13] and even if it is possible that there exist non-material entities in them that function to do these activities, these possibly-existing, non-material entities are not the kind of mind that will be discussed in this theory. The conclusions, implications, predictions, and other statements that are valid for the mind as specifically defined above are thus unproven to be valid for these possible entities.
D2. Mental process
A mental process is the mind’s part that functions to do a certain activity listed above, that is, to sense, operate, or send signals. It can be a conscious mental process (that is, the mind can be aware of it consciously), such as the final-stage visual perception mental process, the emotion mental process, and the volitional movement mental process, or an unconscious mental process (that is, the mind cannot be aware of it consciously), such as the early-stage visual perception mental process, the mental process that controls muscle tone and balance, and the mental process that controls breathing.
D3. Mental phenomenon
A mental phenomenon is a phenomenon that occurs in the mind. Mental phenomena that are consciously experienceable, such as a vision, a sound, an emotion, a thought, and a memory that occur in the mind, are the main mental phenomena that will be studied in this theory because they have observable and testable characteristics, such as the vision has color, brightness, shape, and velocity as its observable and testable characteristics and the sound has pitch, timbre, and loudness as its observable and testable characteristics.
D4. Neural circuit
A neural circuit is a functional group of neurons that are connected together in some specific pattern to process signals in its circuit [14-16], which is its principal function, such as to perceive visual sensation signals, to integrate various signals to form a decision, or to synthesize signals to control a motor movement. Anatomically, a neural circuit may not be just a single group of connected neurons in one location but may be a network of scattered groups of connected neurons in different areas, such as the neural circuit of consciousness [17-25]. However, to be a certain neural circuit, all the groups of the circuit must be connected and function together to perform a certain neural function.
A normal functional neural circuit is usually a complex 3-dimensional circuit and always has connections with other neural circuits and/or its sensor(s) and/or its effector(s) so that it can send/receive signals to/from them. At present, there is a lot of evidence that, under a normal condition, a certain neural circuit is not a multi-functional circuit that performs various neural functions alternately. Instead, a certain neural circuit mostly, if not exclusively, performs only a certain function , such as perceiving visual sensation, thinking, or generating emotion. These specific neural circuits reside in different, specific brain areas, such as visual perception neural circuits are in the visual cortex, thinking neural circuits are in the frontal cortex, and emotion neural circuits are in the amygdala. Currently, more than a hundred distinct functional brain areas can be identified by several methods [26-44].
D5. Neural process
A neural process is the signal-processing process of a neural circuit. It is the neural circuit’s part that performs the neural circuit’s principal function – to process signals in the circuit.
How a neural circuit processes signals or how a neural process functions, can be briefly summarized as follows. When a neural circuit is processing signals, there are electrical and/or electrochemical signals circulating in its circuit. Generally, each of its neurons will receive signals from thousands of other neurons at its post-synaptic junctions. Excitatory post-synaptic potentials (EPSPs) or inhibitory post-synaptic potentials (IPSPs) will occur at these post-synaptic junctions and spread via the dendrites to the neuron’s soma and to the initial segment of the axon. The EPSPs and IPSPs will summate while they travel and, when reaching the initial segment of the axon, will or will not result in an action potential, depending on the summation of the EPSPs and IPSPs. (To be noted is that this summation of the EPSPs and IPSPs is the integration of the input signals that that neuron receives). If an action potential occurs, it will travel down the axon to the neuron’s pre-synaptic junctions, and the signals will be sent to thousands of other neurons through these pre-synaptic junctions [45,46]. This whole process takes place in each of millions of neurons in the circuit and results in a circulation of signals among its neurons in some specific pattern depending on the circuit’s anatomy and physiology. In this manner, the signals will be processed from neuron to neuron in some specific ways while they circulate in the circuit. The end result will depend on what the function of the neural circuit is. For example, the end result can be a processed signal to be sent to other neural circuits for further processing, a final sensory perception, or a final executing signal to command its effector.
A neural process is not an instantaneous process; it takes some time to complete the process and generate the whole function. For example, it takes some time (usually in milliseconds [45,47-49]) for a visual perception neural process to create a perception of a face in the brain after receiving the visual signals [50-53].
D6. Signaling pattern (SP)
A signaling pattern (SP) is the pattern of signaling that a neural circuit sends to another neural circuit to convey its signals.
An SP is not a stationary 2-dimensional pattern (like a pattern of a static picture) but a brief, dynamic, 3-dimensional pattern because it takes some time to complete the SP, which involves complex signaling among millions of neurons in the 3-dimensional circuit. Because a neural circuit communicates its information with others via its electrical and/or electrochemical signals in the form of SPs [54-65], an SP that the neural circuit sends to another circuit must be the information that is to be sent. But for a neural circuit to be able to distinguish any particular information, the SP for that particular information must be unique – different from all others. For example, the SP for perceiving visual sensation must be different from that for perceiving auditory sensation. Also, the SP for perceiving a visual image of a letter “A” must be unique and different from the one for a letter “B” [66,67]. Stating otherwise, for neural circuits to communicate information between each other comprehensibly, a signaling pattern for each information must be unique and different from those for other information.
SPs are very important because every neural circuit sends/receives information to/from others in the form of SPs and thus affects/is affected by others by SPs.
D7. Signaling state (SS)
A signaling state (SS) is the pattern of signaling of a whole neural circuit, with signals circulating in its circuit in a certain pattern at any certain moment. Because the signals that are circulating in a certain signaling pattern at any certain moment is the information that is in the neural process at that moment, a signaling state is the information that is in the neural process at that moment. For example, after the primary visual perception neural process has received early-stage visual signals of a house from the lateral geniculate nucleus, it will have the signaling state that is the information of the early-stage visual perception of the house, and after the final visual perception neural process has finished the process of perceiving the vision of the house, it will have the signaling state that is the information of the final visual perception of the house.
In this theorem, for conciseness, the clause “that is the information of” will sometimes be replaced by “that signals”. Thus, the examples in the preceding paragraph can be stated as: after the primary visual perception neural process has received early-stage visual signals of a house from the lateral geniculate nucleus, it will have the signaling state that signals the early-stage visual perception of the house, and after the final visual perception neural process has finished the process of perceiving the vision of the house, it will have the signaling state that signals the final visual perception of the house.
In this theory, information is an abstract entity that describes something. For example, signals in the optic nerve are information about the visual aspect of something that one looks at – this information describes visual aspects (color, brightness, shape, dimension, velocity, etc.) of that thing, and signals in the auditory nerve are information about the auditory aspect of something that one hears – this information describes auditory aspects (pitch, timbre, loudness, etc.) of that thing. Things that have different descriptions thus have different information, and vice versa. By this definition, the information that is discussed in this theory is a kind of semantic information [68-71].
Information can be carried by several kinds of carriers such as electromagnetic waves, sound waves, mechanical forces, chemical substances, or specific molecules. In the nervous system, it is carried by electrical/electrochemical signals in neural circuits in the form of signaling patterns (which are information that is sent to other neural processes) and signaling states (which are information that is in the whole neural processes). Thus, signaling patterns and signaling states are information about something. For example, when the visual perception neural process has finished the process of perceiving a vision of a house, it will have the signaling state that is the information of the visual perception of the house and, when it communicates this information with other neural processes, it will send signaling patterns that are this information to other neural processes via its synapses.
Because an entity is identified by its information, or descriptions, entities that have different information, or different descriptions, are different. For example, because red and blue have different information (different descriptions), such as different wavelengths, different positions in the light spectrum, and different results when mixed with yellow, red and blue are different. Also, because a perception of the red color alone and a perception of the red color with a conscious experience of what the red color is like occurring have different information (different descriptions), the two perceptions are different*.
Because in the nervous system, information is in the form of signaling patterns and signaling states, different information has different signaling patterns and different signaling states. For example, because red and blue have different information, they have different signaling states in the neural processes and different signaling patterns when sent to other neural processes. Similarly, because a perception of the red color alone and a perception of the red color with a conscious experience of what the red color is like occurring have different information, they have different signaling states in the neural processes and different signaling patterns when sent to other neural processes*.
(*These last two examples are important examples; they will help us understand the effects of consciousness and the phenomena called qualia.)
Registered and unregistered information
In this theory, the information that is processed by neural processes is called registered information. Because it is processed by neural processes, registered information has a signaling pattern and a signaling state representing it. And because something in the nervous system happens because of it, registered information has some physical effects on the nervous system – this is different from unregistered information (discussed below). For example, visual and auditory signals that enter into the nervous system are processed by neural processes and are registered information; they have signaling patterns and signaling states representing them and thus have effects on the nervous system.
Unregistered information, in contrast, is the information that is not processed by neural processes. Because it is not processed by neural processes, unregistered information does not have a signaling pattern or a signaling state representing it. And because nothing in the nervous system happens because of it, unregistered information does not have any physical effects on the nervous system. For example, magnetic information and radiation information in the environment or even in our bodies cannot enter into the nervous system because we do not have sensory organs to detect these kinds of information. Therefore, they are not processed by neural processes and are unregistered information; they do not have signaling patterns or signaling states representing them and thus do not have effects on the nervous system. Similarly, all other kinds of information that are not processed by neural processes are unregistered information; they do not have signaling patterns or signaling states representing them and thus do not have effects on the nervous system.
The concepts of registered information and unregistered information are important in understanding the physical effects of mental phenomena that are read by some neural processes versus the physical effects of mental phenomena that are not read by any neural process, and they will help us investigate whether consciousness and the phenomena called qualia have physical effects or not.
- De Sousa A. Towards an integrative theory of consciousness: Part 1 (neurobiological and cognitive models). Mens Sana Monogr. 2013 Jan-Dec;11(1):100-150. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653219/
- Fieser J. Chapter 3: Mind. Great Issues in Philosophy. Copyright 2008, updated 5/1/2016. https://www.utm.edu/staff/jfieser/class/120/3-mind.htm
- Jacob P. Intentionality. Zalta EN, editor. The Stanford Encyclopedia of Philosophy (Winter 2014 Edition). Retrieved 2017 Apr 20 from https://plato.stanford.edu/archives/win2014/entries/intentionality/
- Moutoussis K. The machine behind the stage: A neurobiological approach toward theoretical issues of sensory perception. Front Psychol. 2016;7:1357. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020606/
- Pernu TK. The five marks of the mental. Front Psychol. 2017;8:1084. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500963/
- O’Madagain C. Intentionality. Internet Encyclopedia of Philosophy. Retrieved 2017 Apr 20 from http://www.iep.utm.edu/intentio/
- Jorgensen EM. Animal evolution: Looking for the first nervous system. Current biology. 2014 Jul;24(14):R655–R658. http://www.cell.com/current-biology/fulltext/S0960-9822(14)00752-0
- Leys SP. Elements of a ‘nervous system’ in sponges. J Exp Biol. 2015;218:581-591. http://jeb.biologists.org/content/218/4/581.long
- Renard E, Vacelet J, Gazave E, Lapébie P, Borchiellini C, Ereskovsky AV. Origin of the neuro-sensory system: New and expected insights from sponges. Integr Zool. 2009 Sep;4(3):294-308. https://bio.spbu.ru/staff/pdf/Renard%20et_2009-NervSpon.pdf
- Smith CL, Varoqueaux F, Kittelmann M, Azzam RN, Cooper B, Winters CA, et al. Novel cell types, neurosecretory cells, and body plan of the early-diverging Metazoan Trichoplax adhaerens. Current Biology. 2014 Jul;24(14):1565–1572. http://www.cell.com/current-biology/fulltext/S0960-9822(14)00611-3
- Camilli A, Bassler BL. Bacterial small-molecule signaling pathways. Science. 2006 Feb 24;311(5764):1113-1116. doi: 10.1126/science.1121357. PMID: 16497924 PMCID: PMC2776824. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776824/
- Brenner ED, Stahlberg R, Mancuso S, Vivanco J, Baluska F, Van Volkenburgh E. Plant neurobiology: an integrated view of plant signaling. Trends Plant Sci. 2006 Aug;11(8):413-419. doi: 10.1016/j.tplants.2006.06.009. PMID: 16843034. http://scholar.google.co.th/scholar_url?url=https://www.howplantswork.com/wp-content/uploads/2018/03/Plant_Neurobiology.pdf&hl=th&sa=X&scisig=AAGBfm3buWH6CeF-kkd1no8xZ3vUb9FLgg&nossl=1&oi=scholarr
- Stahlberg R. Historical Overview on Plant Neurobiology. Plant Signal Behav. 2006 Jan-Feb;1(1):6–8. PMCID: PMC2633693 PMID: 19521469. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2633693/
- Harris KD, Shepherd GMG. The neocortical circuit: Themes and variations. Nat Neurosci. 2015 Feb;18(2):170–181. DOI: 10.1038/nn.3917 PMCID: PMC4889215 NIHMSID: NIHMS787031. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889215/
- Pulvermüller F, Garagnani M, Wennekers T. Thinking in circuits: Toward neurobiological explanation in cognitive neuroscience. Biol Cybern. 2014;108(5):573–593. doi: 10.1007/s00422-014-0603-9. PMCID: PMC4228116 PMID: 24939580. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228116/
- Purves D, Augustine GJ, David Fitzpatrick D, Hall WC, Lamantia AS, McNamara JO, Williams SM, editors. Chapter 1. Neuroscience. 3rd ed. Sunderland, Massachusetts: Sinauer Associates Inc; 2004. ISBN-13: 9780878937257 ISBN-10: 0878937250. Retrieved 2017 Nov 1from https://www.hse.ru/data/2011/06/22/1215686482/Neuroscience.pdf
- Andrews-Hanna JR. The brain’s default network and its adaptive role in internal mentation. Neuroscientist. 2012 Jun;18(3):251–270. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3553600/
- Baars BJ.Global workspace theory of consciousness: Toward a cognitive neuroscience of human experience. Prog Brain Res. 2005;150:45-53. https://www.cs.helsinki.fi/u/ahyvarin/teaching/niseminar4/Baars2004.pdf
- Baars BJ, Franklin S, Ramsoy TZ. Global workspace dynamics: Cortical “Binding and propagation” enables conscious contents. Front Psychol. 2013;4:200. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3664777/
- Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011.
- Calster LV, D’Argembeau A, Salmon E, Peters F, Majerus S. Fluctuations of attentional networks and default mode network during the resting state reflect variations in cognitive states: Evidence from a novel resting-state experience sampling method. Journal of Cognitive Neuroscience. 2017 Jan;29(1):95-113. doi: 10.1162/jocn_a_01025.
- Dehaene S, Naccache L. Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition. 2001 Apr;79(1-2):1-37. https://www.jsmf.org/meetings/2003/nov/Dehaene_Cognition_2001.pdf
- Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001 Jan;98(2):676–682. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC14647/
- Sergent C, Dehaene S. Neural processes underlying conscious perception: experimental findings and a global neuronal workspace framework. J Physiol Paris. 2004 Jul-Nov;98(4-6):374-384. https://pdfs.semanticscholar.org/ae61/178a998b4e08851af8ba80e7815fd2c9e6d9.pdf
- Song X, Tang X. An extended theory of global workspace of consciousness. Progress in Natural Science. 2008 Jul;18(7):789–793. https://www.sciencedirect.com/science/article/pii/S100200710800138X
- Kanwisher N. Functional specificity in the human brain: A window into the functional architecture of the mind. Proc Natl Acad Sci U S A. 2010 Jun;107(25):11163–11170. doi: 1073/pnas.1005062107. PMCID: PMC2895137. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2895137/
- Amunts K, Zilles K. Architectonic mapping of the human brain beyond Brodmann. Neuron. 2015 Dec;88:1086-1113. http://www.cell.com/neuron/fulltext/S0896-6273(15)01072-7
- Arslan S, Ktena SI, Makropoulos A, Robinson EC, Rueckert D, Parisot S. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage. 2017 Apr 13. pii: S1053–8119(17)30302–6.
- Bartels A, Zekis S. The chronoarchitecture of the cerebral cortex. Philos Trans R Soc Lond B Biol Sci. 2005 Apr 29;360(1456):733–750. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569482/
- Cohen AL, Fair DA, Dosenbach NUF, Miezin FM, Dierker D, Van Essen DC, et al. Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage. 2008 May;41(1):45–57. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705206/
- Geyer S, Weiss M, Reimann K, Lohmann G, Turner R. Microstructural parcellation of the human cerebral cortex – from Brodmann’s post-mortem map to in vivo mapping with high-field magnetic resonance imaging. Front Hum Neurosci. 2011;5: 19. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044325/
- Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Essa Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016 Aug;536(7615):171–178. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/
- James GA, Hazaroglu O, Bush KA. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magn Reson Imaging. 2016 Feb;34(2):209–218. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4837649/
- Palomero-Gallagher N, Zilles K. Cortical layers: Cyto-, myelo-, receptor- and synaptic architecture in human cortical areas. Neuroimage. 2017 Aug 12. pii: S1053-8119(17)30682-1. https://www.sciencedirect.com/science/article/pii/S1053811917306821
- Passingham RE, Stephan KE, Kötter R. The anatomical basis of functional localization in the cortex. Nat Rev Neurosci. 2002 Aug;3(8):606-616. http://library.ibp.ac.cn/html/cogsci/NRN-2002-606.pdf
- Rakic P. Evolution of the neocortex: Perspective from developmental biology. Nat Rev Neurosci. 2009 Oct;10(10):724–735. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913577/
- Shipp S. The importance of being agranular: A comparative account of visual and motor cortex. Philos Trans R Soc Lond B Biol Sci. 2005 Apr;360(1456):797–814. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569485/
- Sporns O. Cerebral cartography and connectomics. Philos Trans R Soc Lond B Biol Sci. 2015 May;370(1668):20140173. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387514/
- Tungaraza RL, Mehta SH, Haynor DR, Grabowski TJ. Anatomically informed metrics for connectivity-based cortical parcellation from diffusion MRI. IEEE J Biomed Health Inform. 2015 Jul;19(4):1375–1383. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4561620/
- Van Essen DC, Glasser MF. The Human Connectome Project: Progress and Prospects. Cerebrum. 2016 Sep-Oct;2016:cer-10-16. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5198757/
- Van Essen DC, Glasser MF, Dierker DL, Harwell J, Coalson T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb Cortex. 2012 Oct;22(10):2241–2262. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432236/
- Van Essen DC, Glasser MF. In vivo architectonics: A cortico-centric perspective. Neuroimage. 2014 Jun;93 Pt 2:157–164. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767769/
- Zilles K, Palomero-Gallagher N, Schleicher A. Transmitter receptors and functional anatomy of the cerebral cortex. J Anat. 2004 Dec;205(6):417–432. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1571403/
- Zilles K, Amunts K. Receptor mapping: Architecture of the human cerebral cortex. Curr Opin Neurol. 2009 Aug;22(4):331-339.
- Augustine GJ. Unit I Neural Signals. In: Purves D, Augustine GJ, David Fitzpatrick D, Hall WC, Lamantia AS, McNamara JO, Williams SM, editors. Neuroscience. 3rd ed. Sunderland, Massachusetts: Sinauer Associates Inc; 2004. ISBN-13: 9780878937257 ISBN-10: 0878937250. Retrieved 2017 Nov 01from https://www.hse.ru/data/2011/06/22/1215686482/Neuroscience.pdf
- Byrne JH. Introduction to neurons and neuronal networks. Neuroscience Online. The University of Texas Health Science Center at Houston (UTHealth). Retrieved 2018 Feb 14 from http://nba.uth.tmc.edu/neuroscience/s1/introduction.html
- Baars BJ, Edelman DB. Consciousness, biology and quantum hypotheses. Phys Life Rev. 2012 Sep;9(3):285–294. https://www.ncbi.nlm.nih.gov/pubmed/22925839
- Monk T, Paulin MG. Predation and the origin of neurones. Brain Behav Evol. 2014;84:246-261. https://www.karger.com/Article/FullText/368177
- Ponulak F, Kasinski A. Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiol Exp (Wars). 2011;71(4):409-433. http://www.ane.pl/linkout.php?pii=7146
- Babiloni C, Marzano N, Soricelli A, Cordone S, Millán–Calenti JC, Percio CD, Buján A. Cortical neural synchronization underlies primary visual consciousness of qualia: Evidence from event–related potentials. Front Hum Neurosci. 2016;10:310. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927634/
- Bacon-Macé N, Macé MJM, Fabre-Thorpe M,Thorpe SJ. The time course of visual processing: Backward masking and natural scene categorization. Vision Research. 2005 May;45(11):1459-1469. http://www.sciencedirect.com/science/article/pii/S0042698905000027?via%3Dihub
- Carbon CC. The first 100 milliseconds of a face: on the microgenesis of early face processing. Percept Mot Skills. 2011 Dec;113(3):859-874. 22403930. http://journals.sagepub.com/doi/pdf/10.2466/07.17.22.PMS.113.6.859-874
- Masquelier T, Albantakis L, Deco G. The timing of vision – how neural processing links to different temporal dynamics. Front Psychol. 2011 Jun;2:151. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3129241/
- Ainsworth M, Lee S, Cunningham MO, Traub RD, Kopell NJ, Whittington MA. Rates and rhythms: A synergistic view of frequency and temporal coding in neuronal networks. Neuron. 2012 Aug 23;75(4):572-583. http://www.cell.com/neuron/fulltext/S0896-6273(12)00709-X
- Bohte SM. The evidence for neural information processing with precise spike-times: A survey. Nat Comput. June 2004 Jun;3(2):195–206. https://homepages.cwi.nl/~sbohte/publication/spikeNeuronsNC.pdf
- Doetsch GS. Patterns in the brain. Neuronal population coding in the somatosensory system. Physiol Behav. 2000 Apr;69(1-2):187-201.
- deCharms RC1, Zador A. Neural representation and the cortical code. Annu Rev Neurosci. 2000;23:613-647. http://www.cnbc.cmu.edu/~tai/readings/nature/zador_code.pdf
- Gardner B, Sporea I, Grüning A. Encoding spike patterns in multilayer spiking neural networks.arXiv.org. 2015. 2015 Mar 31. Retrieved 2018 Feb 16 from https://arxiv.org/pdf/1503.09129.pdf
- Gardner B. Learning spatio-temporally encoded pattern transformations in structured spiking neural networks [submitted for the Degree of Doctor of Philosophy from the University of Surrey. Department of Computer Science, Faculty of Engineering and Physical Sciences]. Guildford, Surrey: University of Surrey; 2016 Mar. Retrieved 2017 Feb 15 from https://pdfs.semanticscholar.org/31e6/6434a451c8955e294abd080de4de0087b263.pdf
- Gr¨uning A, Bohte SM. Spiking neural networks: Principles and challenges. ESANN 2014 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 2014 Apr 23-25, i6doc.com publ., ISBN 978-287419095-7. Retrieved 2017 Feb 16 from https://homepages.cwi.nl/~sbohte/publication/es2014-13Gruning.pdf
- Jermakowicz WJ, Casagrande VA. Neural networks a century after Cajal. Brain Res Rev. 2007 Oct;55(2):264–284. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2101763/
- Masuda N, Aihara K. Dual coding hypotheses for neural information representation. Math Biosci. 2007 Jun;207(2):312-321.
- Ponulak F, Kasinski A. Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiol Exp (Wars). 2011;71(4):409-433. http://www.ane.pl/linkout.php?pii=7146
- Rolls ET, Treves A. The neuronal encoding of information in the brain. Prog Neurobiol. 2011 Nov;95(3):448-490.
- Sanger TD. Neural population codes. Curr Opin Neurobiol. 2003 Apr;13(2):238-249.
- Haynes JD, Rees G. Decoding mental states from brain activity in humans. Nat Rev Neurosci. 2006 Jul;7(7):523-534. http://www.utdallas.edu/~otoole/HCS6330_F09/17_Haynes_decoding_NNR_06.pdf
- Tong F, Pratte MS. Decoding patterns of human brain activity. Annu Rev Psychol. 2012;63:483-509. https://pdfs.semanticscholar.org/c272/bd3ad307796d17d2df86befd13c668a66d0a.pdf
- Adriaans P. Information. Zalta EN, editor. The Stanford Encyclopedia of Philosophy (Fall 2013 Edition). Retrieved 2018 Apr 1 from https://plato.stanford.edu/archives/fall2013/entries/information
- Floridi L. Is semantic information meaningful data? Philosophy and Phenomenological Research. 2005 Mar;LXX(2):351-370. http://www.philosophyofinformation.net/wp-content/uploads/sites/67/2014/05/iimd.pdf
- Floridi L. Semantic conceptions of information. Zalta EN, editor. The Stanford Encyclopedia of Philosophy (Spring 2017 Edition). Retrieved 2018 Apr 1 from https://plato.stanford.edu/archives/spr2017/entries/information-semantic
- Zhong Y. A theory of semantic information. 2017;1,129. http://www.mdpi.com/2504-3900/1/3/129/pdf