The Original Version – Introduction & Definitions

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 exactly what its nature 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 we know what their nature is and have a lot of information about them. For example, we can tell that an apple is a material object – a fruit with fairly round shape, red/green color, sweet smell, delectable taste, and nutritious. Even something that is immaterial, such as electromagnetic wave, we can tell that it has the dual nature of being a wave and a particle and that it can cause electrical charges to move, induce a current in a wire, dislodge electrons from atoms, etc.; we can even write formulas to describe its properties. Moreover, for both examples, we can answer the questions of why and how they occur. We cannot do such things to the mind. Superficially, it seems that what we can talk about its nature is that it is immaterial, can do various mental activities, and has some observable functional properties, such as being private, subjective, and representational [1-6]. Other than that, we cannot definitely tell 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 the answers exist in the physical properties of the mind, which will be discussed in the following chapters.

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 different meanings in the literature. To avoid confusion, 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.

 

D1. Mind

A mind is a non-material entity that exists in an animal with a nervous system and that can

– sense signals from its environment (such as light, sound, and tactile stimuli), from its own body (such as proprioception, vestibular stimuli, and pain from internal organs), and from within itself (such as emotion, thought, and memory);

– process (such as integrate, store, and retrieve) signals, resulting in various mental activities, both conscious (such as solving problems, remember things, and reliving past events) and unconscious (such as unconscious control of muscle tone and balance, unconscious control of fine motor coordination, and unconscious control of sleep-wake cycle); 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 respond to its environment and to control its own body functions.

An immature mind is a non-material entity that exists in an animal with a nervous system and that can do some, but not all, of the listed functions 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 do some, but not all, of the listed functions 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 cerebral concussion, stroke, brain tumor, Alzheimer’s disease, or congenital brain defect.

This theory is about the mind as defined. 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 has the capabilities listed above. The definition does not include possibly-existing, non-material entities that have the above-listed capabilities but reside in

– animals that do not have a nervous system, such as sponges and Trichoplax [7-10],

– other kinds of living organisms, such as bacteria, fungi, and plants, or

– non-living things, such as a rock, a weather system, and the dynamic photosphere of the sun.

Thus, even if sponges, bacteria, plants, or some of the entities above can sense signals from the environment, process signals, and send signals to its effectors and even if it is possible that there exist non-material entities in them that can perform these things, 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 a mind’s part that performs a certain function listed above, that is, to sense, process, or send signals. It can be a conscious process (that is, the mind can be aware of it), such as the final-stage visual perception mental process, emotion mental process, and volitional movement mental process, or an unconscious process (that is, the mind cannot be aware of it), such as the early-stage visual perception mental process, mental processes that control muscle tone and balance, and mental processes that control of sleep-wake cycle.

D3. Non-processing mental component

A non-processing mental component is a mind’s part that does not perform any of the functions listed above (that is, to sense, process, and send signals) directly but can assist or affect mental processes in performing their functions. The non-processing mental components of the mind are long-term memories, inborn-personalities, and inborn-instincts.

Long-term memories are enduring, learned, stored information in the mind. They can be separated into explicit (declarative) memories and implicit (non-declarative) memories [11-13]. Explicit memories, which are memories about events and facts, can give signals that are information about events and facts to some mental processes in performing their functions, such as solving problems, making decisions, and planning. Implicit memories, which are learned memories about how to perform something, can express themselves unconsciously through mental processes in the forms of habits, skills, priming, simple conditioning [11-13], and acquired instincts [14-15] to help mental processes perform learned functions quickly and efficiently.

Inborn-personalities and inborn-instincts are inborn parts of the mind [15,16,17]. They are inherent in the mental processes and affect the way the mental processes function so that the mental processes behave in conformity with the personalities and the instincts.

These non-processing mental components do not exhibit themselves unless they are retrieved by mental processes (in the case of long-term memories) or they manifest themselves through mental processes’ functions (in the cases of implicit memories, personalities, and instincts). They are to be differentiated from mental processes because most of the theory will be about mental processes, not these kinds of mental components.

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, resulting in its function, such as to perceive visual sensation signals, to integrate various signals to form a decision, or to synthesize signals to control 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 [18-26].

However, to be a certain neural circuit, all the groups 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 versatile circuit that performs various neural functions. Instead, a certain neural circuit performs only a specific function, such as perceiving visual sensation, thinking, or generating emotion. These specific neural circuits reside in different, specific brain areas, such as the visual cortex, frontal lobe, or amygdala. And currently, more than a hundred distinct functional brain areas can be identified by several methods [27-44].

D5. Neural process

A neural process is a part of a functioning neural circuit and is the part that performs the neural circuit’s function. When a neural circuit is functioning, there are several parts in process: the signal processing process, the metabolic process, the blood circulation process, and the structural modification process. But the part that performs the neural circuit’s function is the signal processing part. Therefore, a neural process is the signal-processing part of a neural circuit; that is, a neural process is a signal processing process.

How a neural circuit processes signals or how a neural process functions, is as follows. When a neural circuit is processing signals, there are electrical or electrochemical signals circulating in its circuit. Each of its neurons will process the received signals at its post-synaptic junctions and send out the processed signals to other neurons at its pre-synaptic junctions, resulting in 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 but takes some time to complete the process and generate the whole function, such as it takes some time (usually in milliseconds [45-48]) for a visual perception neural process to create a perception of a face in the brain after seeing it [49-52].

D6. Signaling pattern (SP)

A signaling pattern (SP) is the pattern of signaling of a neural circuit when it is processing signals, as described above.

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. An SP may remain unchanged for some time – it will be stable after the neural process has reached the stable final stage and does not change to do something else. For example, an SP for a visual perception of a house will remain stable after the neural process has finished the function of creating the complete visual perception of the house as long as the person does not change his/her gaze to look at something else.

Because a neural circuit communicates its information with others via its SPs [53-64], 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 different form the one for a letter “B” [65,66]. Stating otherwise, for neural circuits to communicate information between each other comprehensibly, a signaling pattern for different information must be different.

SPs are very important because every neural circuit sends/receives information to/from others by SPs and thus affects/is affected by others by SPs.

D7. Signaling state (SS) 

A signaling state (SS) is the neural process’s state of signaling, with some specific SP occurring. When functioning, the neural process may be in the early, intermediate, or final signaling state, depending on how far the signal-processing process has progressed. At each stage, the neural process will be in the signaling state that has the SP containing the information of that stage. This kind of statement will occur frequently in this theory. So, when conciseness is required, a statement like the asterisked one will be stated in this way: the neural process will be in the signaling state that signals the neural process’s result. That is, in this theory, “that signals” in such a statement means “that has the SP containing the information of”.

For example, when the visual neural process has finished the process of perceiving a vision of a house, it will be in the signaling state that signals the visual perception of the house (= it will be in the signaling state that has the signaling pattern containing the information of the visual perception of the house) or when the auditory neural process has finished the process of perceiving a musical note C, it will be in the signaling state that signals the musical note C (= it will be in the signaling state that has the signaling pattern containing the information of the musical note C).

D8. Information

Information in this theory refers to 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 – it 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 – it describes auditory aspects (pitch, timbre, loudness, etc.) of that thing. Things that have different information thus have different descriptions, and vice versa. This definition of information makes it a kind of semantic information [67-70].

Information can be carried by several carriers such as electromagnetic waves, sound waves, mechanical forces, electric potentials, magnetic potentials, chemical substances, or specific molecules. In the nervous system, it is carried by electrical/electrochemical signals in neural circuits [48,53-64], in the form of signaling patterns. Thus, the signaling patterns are information about something, such as the color of the fruit, the direction that the sound comes from, the intensity of the pain, the recollected past event, or the current emotion.

Because an entity is identified by its information, entities that have different information are different. Equivalently, because an entity is identified by its descriptions, entities that have different descriptions are different. For example, because red and blue have different information (and different descriptions), such as different wavelengths, different positions in the light spectrum, and different results when mixed with yellow, etc., red and blue are different. Also, because the perception of the color red alone and the perception of the color red with the conscious experience of what the color red is like occurring have different information (and different descriptions), the two perceptions are different*.

Because in the nervous system, information is in the form of signaling patterns [53-64], different information has different signaling patterns. For example, because red and blue have different information, they have different signaling patterns in the nervous system. Also, because the perception of the color red alone and the perception of the color red with the conscious experience of what the color red is like occurring have different information, they have different signaling patterns in the nervous system*.

(*These last two examples are important examples; they will help us understand the nature of consciousness and the phenomena called qualia.)

< back to The Current Introduction & Definition

References

  1. 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/
  2. 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
  3. 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/
  4. 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/
  5. Pernu TK. The five marks of the mental. Front Psychol. 2017;8:1084. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500963/
  6. O’Madagain C. Intentionality. Internet Encyclopedia of Philosophy. Retrieved 2017 Apr 20 from http://www.iep.utm.edu/intentio/
  7. 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
  8. 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
  9. 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
  10. 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
  11. Camina E, Güell F. The neuroanatomical, neurophysiological and psychological basis of memory: Current models and their origins. Front Pharmacol. 2017;8:438. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5491610/
  12. Purves D. Chapter 30: Memory. 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; 2004:733-753. ISBN-13: 9780878937257 ISBN-10: 0878937250. Retrieved 2017 Nov 01from https://www.hse.ru/data/2011/06/22/1215686482/Neuroscience.pdf
  13. Squire LR, Dede AJO. Conscious and unconscious memory systems. Cold Spring Harb Perspect Biol. 2015 Mar;7(3):a021667. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355270/
  14. Blumberg MS. Development evolving: The origins and meanings of instinct. Wiley Interdiscip Rev Cogn Sci. 2017 Jan;8(1-2):10.1002/wcs.1371. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5182125/?log$=activity
  15. LaMantia AS. Chapter 23: Modification of brain circuits as a result of experience. 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; 2004:557-574. ISBN-13: 9780878937257 ISBN-10: 0878937250. Retrieved 2017 Nov 01from https://www.hse.ru/data/2011/06/22/1215686482/Neuroscience.pdf
  16. Root CM, Denny CA, Hen R, Axel R. The participation of cortical amygdala in innate, odor-driven behavior. Nature. 2014 Nov;515(7526):269–273. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231015/
  17. Zha X, Xu X. Dissecting the hypothalamic pathways that underlie innate behaviors. Neurosci Bull. 2015 Dec;31(6):629–648. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563731/
  18. 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/
  19. 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
  20. 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/
  21. 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.
  22. 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.
  23. 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
  24. 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/
  25. 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
  26. 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
  27. 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
  28. 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. 
  29. 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/
  30. 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/
  31. 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/
  32. 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/
  33. 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/
  34. 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
  35. 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
  36. 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/
  37. 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/
  38. 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/
  39. 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/
  40. 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/
  41. 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/
  42. 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/
  43. 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/
  44. Zilles K, Amunts K. Receptor mapping: Architecture of the human cerebral cortex. Curr Opin Neurol. 2009 Aug;22(4):331-339.
  45. 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
  46. Monk T, Paulin MG. Predation and the origin of neurones. Brain Behav Evol. 2014;84:246-261. https://www.karger.com/Article/FullText/368177
  47. Ponulak F, Kasinski A. Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiol Exp (Wars). 2011;71(4):409-33.  http://www.ane.pl/linkout.php?pii=7146
  48. 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
  49. 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/
  50. 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
  51. 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
  52. 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/
  53. 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):572-83. http://www.cell.com/neuron/fulltext/S0896-6273(12)00709-X
  54. 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
  55. Doetsch GS. Patterns in the brain. Neuronal population coding in the somatosensory system. Physiol Behav. 2000 Apr;69(1-2):187-201.
  56. deCharms RC1, Zador A. Neural representation and the cortical code. Annu Rev Neurosci. 2000;23:613-47. http://www.cnbc.cmu.edu/~tai/readings/nature/zador_code.pdf
  57. Gardner B, Sporea I, Grüning A. Encoding spike patterns in multilayer apiking neural networks. arXiv.org. 2015. 2015 Mar 31. Retrieved 2018 Feb 16 from https://arxiv.org/pdf/1503.09129.pdf
  58. 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
  59. 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
  60. 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/
  61. Masuda N, Aihara K. Dual coding hypotheses for neural information representation. Math Biosci. 2007 Jun;207(2):312-321.
  62. Ponulak F, Kasinski A. Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiol Exp (Wars). 2011;71(4):409-33. http://www.ane.pl/linkout.php?pii=7146
  63. Rolls ET, Treves A. The neuronal encoding of information in the brain. Prog Neurobiol. 2011 Nov;95(3):448-90.
  64. Sanger TD. Neural population codes. Curr Opin Neurobiol. 2003 Apr;13(2):238-49.
  65. 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
  66. Tong F, Pratte MS. Decoding patterns of human brain activity. Annu Rev Psychol. 2012;63:483-509. https://pdfs.semanticscholar.org/c272/bd3ad307796d17d2df86befd13c668a66d0a.pdf
  67. 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
  68. Zhong Y. A theory of semantic information. Proceedings. 2017;1,129. http://www.mdpi.com/2504-3900/1/3/129/pdf
  69. 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
  70. 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

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