Hierarchical Brain

An explanation of the human brain

First published 1st February 2024. This is version 1.5 published 2nd March 2024.
Three pages are not yet published: sleep, memory and an index.
Copyright © 2024 Email info@hierarchicalbrain.com

Warning - the conclusions of this website may be disturbing for some people without a stable mental disposition or with a religious conviction.

Afferent processing

Afferent processing is my name for the processing of data by the human brain. The data can be from the external or internal senses or from the internal processes of the brain. All external incoming signals are processed by the brain because that is all it can do to try to learn about the outside world, and processing internal data about the body and the brain helps it to learn about and control itself. Evolution has created a brain that processes data in order to learn and therefore to survive. The end result is what can be described as a model of my world in my brain, which includes my body and my brain itself.

My description of afferent processing spans four contiguous levels of description in my hierarchical structure of seven levels, with the most important intermediate emergent concept being the symbol schema, the representation of a concept in the brain.

The processing of data creates or strengthens afferent connections to create symbol schemas and the connections between them, which together make up the model of my world, but it also creates or strengthens important efferent connections in the reverse direction, back towards where the data came from, which are vital for higher-level functions. I have created a number of afferent processing examples that show how this processing could take place based on the known capabilities of neurons and synapses.

Contents of this page
Naming - details on my name for afferent processing.
The science of data processing - a brief history of the science of data processing in the brain and other names used.
Overview of my proposals - overview of afferent processing.
Detail of my proposals - details of afferent processing.
Conclusions - some final thoughts.
References - references and footnotes.


The science of data processing and other names used

Overview of my proposals

Detail of my proposals


References For information on references, see structure of this website - references

  1. ^ The Organization of Behavior - Hebb 1949
    downloadable here or see GoogleScholar.
    This classic work uses the terms afferent and efferent frequently (there are 80 occurrences of the word “afferent” and 16 of “efferent” in the book), although not all of these refer to pathways inside the brain. See, for example, the last paragraph of page xvi: “We know a good deal about the afferent pathways to the cortex, about the efferent pathways from it, and about many structures linking the two.”
  2. ^ ^ The Cognitive Neurosciences ed. Gazzaniga Fourth Edition MIT Press USA 2009
    Page 315, first paragraph: “If one includes all the 250 million neurons in V1, the factor increases to 180, and for the whole visual cortex (nearly 800 million neurons), the number of neurons is almost 600 times the number of LGN neurons. ... It is estimated that in the foveal region, the density of neurons in V1 per unit solid angle of the visual field is 10,000 times the density of input fibers from the LGN. ... The number of neurons available for computations on the input from the fovea is truly phenomenal.”
    Page 317, fourth paragraph: “...in the foveal region of V1, there are some 10,000 cortical neurons per input fiber from the LGN.” V1 is the first and primary processing area for visual input. LGN is the Lateral geniculate nucleus, the area of the thalamus that sends data to the visual cortex.
  3. ^ Ibid. The Cognitive Neurosciences
    Pages 316-8 discusses the subtle connection between “correlation”, which is the same as what I call coincidence detection, and “symmetry”. For example, a frisbee is still recognised as a frisbee even if it is seen in a different place (displacement symmetry) viewed from a different angle (rotational symmetry), in different lighting (contrast, shadow or colour symmetry), between the two eyes (binocular or stereoscopic symmetry) or even using different senses (feeling rather than seeing, say). “Invariance” is another way of describing this.
  4. ^ ^ On Intelligence and Google Book preview - On Intelligence - Jeff Hawkins with Sandra Blakeslee, St. Martin’s Press, New York 2004
    See some references to ‘feedback’ and, for example, pages 113-114: “Moreover, there are as many if not more feedback connections in visual cortex as there are feedforward connections. For many years most scientists ignored these feedback connections. If your understanding of the brain focused on how the cortex took input, processed it, and then acted on it, you didn’t need feedback. All you needed were feedforward connections leading from sensory to motor sections of the cortex. But when you begin to realize that the cortex’s core function is to make predictions, then you have to put feedback into the model; the brain has to send information flowing back toward the region that first receives the inputs. Prediction requires a comparison between what is happening and what you expect to happen. What is actually happening flows up, and what you expect to happen flows down. The same feedforward-feedback process is occurring in all your cortical areas involving all your senses.”
  5. ^ Google Book preview - Surfing Uncertainty: Prediction, Action, and the Embodied Mind - Andy Clark, Oxford University Press 2016
    e.g. pages 143-146 or some references to ‘feedforward’
  6. ^ Whatever next? Predictive brains, situated agents, and the future of cognitive science - Andy Clark 2013
    doi: 10.1017/S0140525X12000477 downloadable here or see GoogleScholar.
    See for example note 8 on page 22: “I have adopted the neuroanatomist practice of labeling connections simply as 'backward' and 'forward' so as to avoid the functional implications of the labels 'feedback' and 'feedforward.' This is important in the context of predictive processing models, since it is now the forward connections that are really providing (by conveying prediction error) feedback on the downward-flowing predictions...”
  7. ^ ^ View from the top: Hierarchies and Reverse Hierarchies in the Visual System - Hochstein and Ahissar 2002
    doi: 10.1016/S0896-6273(02)01091-7 downloadable here or see GoogleScholar.
    Text accompanying Figure 1 on page 792: “Classically, the visual system was seen as a hierarchy of cortical areas and cell types. Neurons of low-level areas (V1, V2) receive visual input and represent simple features such as lines or edges of specific orientation and location. Their outputs are integrated and processed by successive cortical levels (V3, V4, medial-temporal area MT), which gradually generalize over spatial parameters and specialize to represent global features. Finally, further levels (inferotemporal area IT, prefrontal area PF, etc.) integrate their outputs to represent abstract forms, objects, and categories. The function of feedback connections was unknown. Reverse Hierarchy Theory proposes that the above forward hierarchy acts implicitly, with explicit perception beginning at high-level cortex, representing the gist of the scene on the basis of a first order approximate integration of low-level input. Later, explicit perception returns to lower areas via the feedback connections, to integrate into conscious vision with scrutiny the detailed information available there. Thus, initial perception is based on spread attention (large receptive fields), guessing at details, and making binding or conjunction errors. Later vision incorporates details, overcoming such blindnesses.”
  8. ^ The Human Visual Cortex - Grill-Spector and Malach 2004
    doi: 10.1146/annurev.neuro.27.070203.144220 downloadable here or see GoogleScholar.
    Page 661, second paragraph, second sentence: “Here we propose an alternative framework to examine the human object recognition system as a hierarchical system. The underlying idea implies that object recognition is implemented in the brain through a series of processing stages, in which more global and invariant representations emerge up the hierarchy of the processing stream. The hierarchy is implemented through a gradual transition, from local representations that are closely tied to the retinal image to abstract representations that are closely linked to perception.”
  9. ^ On the emergence and awareness of auditory objects - Shamma 2008
    doi: 10.1371/journal.pbio.0060155 downloadable here or see GoogleScholar.
    Page 1142, second column, middle of second paragraph, to page 1143: “...for animal experiments to unravel the mysteries of complex sound perception, it is essential that they encompass the study of auditory attention, memory, and plasticity, preferably while the animals are engaged in appropriately complex behaviors... Such approaches are common in experiments with animal vision, but are still rare in auditory research. One reason for this dearth is the absence of widely accepted orderly neural representations of higher auditory features (analogous to orientation, motion, and color selectivity in vision) that can serve as assays for the neural correlates of attention and learning in the auditory cortex. There is also a lack of clear understanding of the physiology and functional organization of secondary auditory fields beyond the primary auditory cortex (analogous to the V2, V4, MT, MST, FEF, and IT areas in vision). And finally, there is still much to be discovered about the anatomical connections within the auditory fields, and between the auditory fields and structures serving higher levels of representation, such as the prefrontal cortex.”
  10. ^ Cognitive Neuroscience: The Biology of the Mind - Gazzaniga, Ivry and Mangun, Fourth Edition 2014 Norton & Company USA
    Page 164, Chapter 5 under the heading “Senses, Sensation and Perception”, first paragraph: “Perception begins with a stimulus from the environment... Sensation refers to the early processing that goes on.”
  11. ^ Statistical Learning: From Acquiring Specific Items to Forming General Rules - Aslin and Newport 2012
    doi: 10.1177/0963721412436806 downloadable here or see GoogleScholar.
    Abstract, page 1: “in this article ...we review evidence and provide a unifying perspective that argues for a single statistical-learning mechanism that accounts for both the learning of input stimuli and the generalization of learned patterns to novel instances.”
  12. ^ Stubborn predictions in primary visual cortex - Yon, Thomas, Gilbert, de Lange, Kok and Press 2022
    doi: 10.31234/osf.io/ndufg downloadable here or see GoogleScholar.
    Page 12, last paragraph of “Discussion”: “In conclusion, we have found that stimulus representations in primary visual cortex continue to be 'sharpened' in line with top-down expectations, even when the environment signals that 'expected' events are no longer likely. Evidence that our perceptual systems form such 'stubborn predictions' reveals important ways that learning and prediction in the human brain may depart from principles laid down in optimal models - possibly due to intrinsic constraints on our ability to estimate different kinds of uncertainty.”
  13. ^ An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex - Bennett 2020
    doi: 10.3389/fncir.2020.00040 downloadable here or see GoogleScholar.
    Second paragraph of Introduction, pages 1-2: “... the neocortex seems to be made up of repeating subunits called 'macrocolumns', each of which contains the same types of neurons, connectivity, and firing properties. This observation has led to the hypothesis that the neocortex is just a repeated replication of the exact same microcircuit and that there was an evolutionary benefit to this duplication. Additional support for this can be seen in rerouting studies, whereby rerouting visual input to auditory cortex seems to convert auditory cortex into a visual cortex, suggesting that the only difference between these two regions is the input they receive and not the computations they perform. This is further supported by the fact that the human neocortex increased in size by almost 3-fold over just the last 3 million years of human evolution, a time frame likely too fast for any new circuitry to emerge other than a duplication of existing circuits. This hypothesis suggests that the only difference between any two areas of the neocortex is the inputs it receives, and the location it sends its outputs - the actual computations themselves are the same.”
  14. ^ Compressionism: A Theory of Mind Based on Data Compression - Maguire, Mulhall, Maguire and Taylor 2015
    downloadable here or see GoogleScholar.
    This very interesting paper contains a discussion on how compression can lead to intelligence, but goes a lot further in its claims (see self-awareness).
    The following is a quote from an old draft page based on this paper on wikivisually which no longer exists.

    “Compressionism is the idea of representing intelligence and/or consciousness in terms of data compression. ... Compressionism proposes that the awareness of a system is something that can be quantified in terms of the data compression it carries out: conscious systems are those who [sic] data compression is so sophisticated as to be feasibly irreversible, thus forcing observers to adopt the intentional stance to make predictions about behavior.”
  15. ^ Integrator or coincidence detector? The role of the cortical neuron revisited - Konig, Engel and Singer 1996
    doi: 10.1016/S0166-2236(96)80019-1 downloadable here or see GoogleScholar.
    First page abstract: “Neurons can operate in two distinct ways, depending on the duration of the interval over which they effectively summate incoming synaptic potentials. If this interval is of the order of the mean interspike interval or longer, neurons act effectively as temporal integrators and transmit temporal patterns with only low reliability. If, by contrast, the integration interval is short compared to the interspike interval, neurons act essentially as coincidence detectors, relay preferentially synchronized input, and the temporal structure of their output is a direct function of the input pattern. Recently, interest in this distinction has been revived because experimental and theoretical results suggest that synchronous firing of neurons might play an important role for information processing in the cortex. Here, we argue that coincidence detection, rather than temporal integration, might be a prevalent operation mode of cortical neurons.”
  16. ^ Soma-axon coupling configurations that enhance neuronal coincidence detection - Goldwyn, Remme and Rinzel 2019
    doi: 10.1371/journal.pcbi.1006476 downloadable here or see GoogleScholar.
    Page 2, Introduction, second sentence: “Coincidence detection is a fundamental neural computation that allows the brain to extract information from the temporal patterns of synaptic inputs. In the cortex, neurons have biophysical specializations compatible with coincidence detection”
  17. ^ ^ Brain Mechanisms of Vision - Hubel and Wiesel 1979
    doi: 10.1038/scientificamerican0979-150 downloadable here or see GoogleScholar.
    Page 43, second paragraph: “The output from each eye is conveyed to the brain by about a million nerve fibers bundled together in the optic nerve. These fibers are the axons of the ganglion cells of the retina.”
  18. ^ ^ On Intelligence and Google Book preview - On Intelligence - Jeff Hawkins with Sandra Blakeslee, St. Martin’s Press, New York 2004
    Page 55, last paragraph: “Visual information from the outside world is sent to your brain via a million fibres in your optic nerve. ... Sounds are carried in via the thirty thousand fibres of your auditory nerve. ... Your spinal cord carries information about touch and internal sensations to your brain via another million fibres.”
  19. ^ Circular inference: mistaken belief, misplaced trust - Deneve and Jardri 2016
    doi: 10.1016/j.cobeha.2016.04.001 downloadable here or see GoogleScholar.
    Although this paper is primarily about the possible causes of psychosis, it provides a good recent overview of the understanding of the way that sense data is dealt with.
    Page 41, under the heading “In search of a neural mechanism”:
    “The brain is inherently hierarchical and progressively constructs increasingly more abstract interpretations of its environment.”
  20. ^ A Theory of How Columns in the Neocortex Enable Learning the Structure of the World - Hawkins, Ahmad and Cui 2017
    doi: 10.3389/fncir.2017.00081 downloadable here or see GoogleScholar.
    Page 12, under the heading “Hierarchy”: “The neocortex processes sensory input in a series of hierarchically arranged regions... the cortex learns multiple models of objects, both within a region and across hierarchical levels.”
  21. ^ Livewired - David Eagleman Canongate 2020
    Page 37: “When a person’s eyes are damaged, signals no longer flood in along the pathways to the occipital cortex (the portion at the back of the brain, often thought of as 'visual' cortex). And so that part of the cortex becomes no longer visual. The ships carrying visual data have stopped arriving, so the coveted territory is taken over by the competing kingdoms of sensory information. As a result, when a blind person passes her fingertips over the raised dots of a Braille poem, her occipital cortex becomes active from mere touch. If she gets a stroke that damages her occipital cortex, she’ll lose her ability to understand Braille. Her occipital cortex has been colonized by touch.”
  22. ^ Ibid. Livewired
    Page 17: “... babies will mimic an adult sticking out their tongue... fibers from your eye don't need to learn how to find their targets deep in the brain; they simply follow molecular cues and hit their goal - every time. For all this sort of hardwiring, we can thank our genes.”
  23. ^ How do neurons know? - Patricia Smith Churchland writing in Daedalus, the Journal of the American Academy of Arts & Sciences, Winter 2004
    downloadable here or see GoogleScholar.
    Page 43, bottom of first column: “Knowledge displayed at birth is obviously likely to be innate. A normal neonate rat scrambles to the warmest place, latches its mouth onto a nipple, and begins to suck. A kitten thrown into the air rights itself and lands on its feet. A human neonate will imitate a facial expression, such as an outstuck tongue. But other knowledge, such as how to weave or make fire, is obviously learned post-natally.”
  24. ^ Reorganization of Human Cerebral Cortex: The Range of Changes following Use and Injury - Elbert and Rockstroh 2004
    doi: 10.1177/1073858403262111 downloadable here or see GoogleScholar.
    This wide-ranging and interesting paper gives a number of examples of reorganisation within the brain caused by a change to inputs. Page 131 provides a “General Principles of Cortical Reorganization”: “The abolition of sensory input or deafferentation, as produced by amputation or segregation of a sensory nerve, results in the 'invasion' of adjacent cortical representations of intact parts of the sensorium into the cortical representation zone of the affected sensory part.”

Page last uploaded Sat Mar 2 02:55:43 2024 MST