Brain

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A Brain on Art

The brain is the center of the nervous system in all vertebrate, and the majority of invertebrate, animals. Some primitive animals such as jellyfish and starfish have a decentralized nervous system without a brain, while sponges lack any nervous system at all. In vertebrates, the brain is located in the head, protected by the skull and close to the primary sensory apparatus of vision, hearing, balance, taste, and smell.

For lessons on the topic of the Brain, follow this link.

Brains can be extremely complex. The human brain contains roughly 100 billion neurons, linked with up to 10,000 synaptic connections each. These neurons communicate with one another by means of long protoplasmic fibers called axons, which carry trains of signal pulses called action potentials to distant parts of the brain or body and target them to specific recipient cells.

From a philosophical point of view, it might be said that the most important function of the brain is to serve as the physical structure underlying the mind. From a biological point of view, though, the most important function is to generate behaviors that promote the welfare of an animal. Brains control behavior either by activating muscles, or by causing secretion of chemicals such as hormones. Even single-celled organisms may be capable of extracting information from the environment and acting in response to it. Sponges, which lack a central nervous system, are capable of coordinated body contractions and even locomotion. In vertebrates, the spinal cord by itself contains neural circuitry capable of generating reflex responses as well as simple motor patterns such as swimming or walking. However, sophisticated control of behavior on the basis of complex sensory input requires the information-integrating capabilities of a centralized brain.

Despite rapid scientific progress, much about how brains work remains a mystery. The operations of individual neurons and synapses are now understood in considerable detail, but the way they cooperate in ensembles of thousands or millions has been very difficult to decipher. Methods of observation such as EEG recording and neuroimaging tell us that brain operations are highly organized, but these methods do not have the resolution to reveal the activity of individual neurons. Thus, even the most fundamental principles of neural network computation may to a large extent remain for future investigators to discover.

Macroscopic structure

The brain is the most complex biological structure known, and comparing the brains of different species on the basis of appearance is often difficult. Nevertheless, there are common principles of brain architecture that apply across a wide range of species. These are revealed mainly by three approaches. The evolutionary approach means comparing brain structures of different species, and using the principle that features found in all branches that descend from a given ancient form were probably present in the ancestor as well. The developmental approach means examining how the form of the brain changes during the progression from embyronic to adult stages. The genetic approach means analyzing gene expression in various parts of the brain across a range of species. Each approach complements and informs the other two.

The cerebral cortex is a part of the brain that most strongly distinguishes mammals from other vertebrates, primates from other mammals, and humans from other primates. In non-mammalian vertebrates, the surface of the cerebrum is lined with a comparatively simple layered structure called the pallium. In mammals, the pallium evolves into a complex 6-layered structure called neocortex. In primates, the neocortex is greatly enlarged in comparison to its size in non-primates, especially the part called the frontal lobes. In humans, this enlargement of the frontal lobes is taken to an extreme, and other parts of the cortex also become quite large and complex.

The relationship between brain size, body size and other variables has been studied across a wide range of species. Brain size increases with body size but not proportionally. Averaging across all orders of mammals, it follows a power law, with an exponent of about 0.75. This formula applies to the average brain of mammals but each family departs from it, reflecting their sophistication of behavior. For example, primates have brains 5 to 10 times as large as the formula predicts. Predators tend to have larger brains. When the mammalian brain increases in size, not all parts increase at the same rate. The larger the brain of a species, the greater the fraction taken up by the cortex.

Brain and mind

Understanding the relationship between the physical brain and the functional mind is a challenging problem both philosophically and scientifically. The most straightforward scientific evidence that there is a strong relationship between the physical brain matter and the mind is the impact physical alterations to the brain, such as injury and drug use, have on the mind.

The mind-body problem is one of the central issues in the history of philosophy, which asks us to consider if the correlation between the physical brain and the mind are identical, partially distinct, or related in some unknown way. There are three major schools of thought concerning the answer: dualism, materialism, and idealism. Dualism holds that the mind exists independently of the brain; materialism holds that mental phenomena are identical to neuronal phenomena; and idealism holds that only mental substances and phenomena exist. In addition to the philosophical questions, the relationship between mind and brain involves a number of scientific questions, including understanding the relationship between thought and brain activity, the mechanisms by which drugs influence thought, and the neural correlates of consciousness.

Through most of history many philosophers found it inconceivable that cognition could be implemented by a physical substance such as brain tissue. Philosophers such as Patricia Churchland posit that the drug-mind interaction is indicative of an intimate connection between the brain and the mind, not that the two are the same entity. Even Descartes, notable for his mechanistic philosophy which found it possible to explain reflexes and other simple behaviors in mechanistic terms, could not believe that complex thought, language in particular, could be explained by the physical brain alone.

How it is studied

Neuroscience seeks to understand the nervous system, including the brain, from a biological and computational perspective. Psychology seeks to understand behavior and the brain. Neurology refers to the medical applications of neuroscience. The brain is also the most important organ studied in psychiatry, the branch of medicine that works to study, prevent, and treat mental disorders. Cognitive science seeks to unify neuroscience and psychology with other fields that concern themselves with the brain, such as computer science (artificial intelligence and similar fields) and philosophy.

Some methods of examining the brain are mainly useful in humans. This section focuses on methods that are usable across a wide range of animal species. (However, the great majority of neuroscience experiments are done using rats or mice as subjects.)

Neuroanatomy

The oldest method of studying the brain is anatomical, and until the middle of the 20th century, much of the progress in Neuroscience came from the development of better stains and better microscopes. Much critical information about synaptic function has come from study of electron microscope images of synapses. On a larger scale, neuroanatomists have invented a plethora of stains that reveal neural structure, chemistry, and connectivity. In recent years, the development of immunostaining techniques has allowed staining of neurons that express specific sets of genes.

Electrophysiology

Electrophysiology allows scientists to record the electrical activity of individual neurons or groups of neurons. There are two general approaches: intracellular and extracellular recordings.

Intracellular recording uses glass electrodes with very fine tips in order to pick up electrical signals from the interior of a neuron. This method is very sensitive, but also very delicate, and usually is carried out in vitro—i.e., in a dish of warm nutrient solution; using tissue that has been extracted from the brain of an animal.

Extracellular recording uses larger electrodes that can be used in the brains of living animals. This method cannot usually resolve the tiny electrical signals generated by individual synaptic connections, but it can pick up action potentials generated by individual neurons, as well as field potentials generated by synchronous synaptic activity in large groups of neurons. Because the brain does not contain pain receptors, it is possible using these techniques to record from animals that are awake and behaving without causing distress. The same techniques have occasionally been used to study brain activity in human patients suffering from intractable epilepsy, in cases where there was a medical necessity to implant electrodes in order to localize the brain area responsible for seizures.

Lesion studies

In humans, the effects of strokes and other types of brain damage have been a key source of information about brain function. Because there is no ability to experimentally control the nature of the damage, however, this information is often difficult to interpret. In animal studies, most commonly involving rats, it is possible to use electrodes or locally injected chemicals to produce precise patterns of damage and then examine the consequences for behavior.

Computation

A computer, in the broadest sense, is a device for storing and processing information. In an ordinary digital computer, information is represented by magnetic elements that have two possible states, often denoted 0 and 1. In a brain, information is represented both dynamically, by trains of action potentials in neurons, and statically, by the strengths of synaptic connections between neurons. In a digital computer, information is processed by a small set of "registers" that operate at speeds of billions of cycles per second. In a brain, information is processed by billions of neurons all operating simultaneously, but only at speeds around 100 cycles per second. Thus brains and digital computers are similar in that both are devices for processing information, but the ways that they do it are very different. Computational neuroscience encompasses two approaches: first, the use of computers to study the brain; second, the study of how brains perform computation. On one hand, it is possible to write a computer program to simulate the operation of a group of neurons by making use of systems of equations that describe their electrochemical activity: such simulations are known as biologically realistic neural networks. On the other hand, it is possible to study algorithms for neural computation by simulating, or mathematically analyzing, the operations of simplified "units" that have some of the properties of neurons but abstract out much of their biological complexity.

Most programs for digital computers rely on long sequences of operations executed in a specific order, and therefore could not be "ported" into a brain without becoming extremely slow. Computer scientists, however, have found that some types of problems lend themselves naturally to algorithms that can efficiently be executed by brainlike networks of processing elements. One very important problem that falls into this group is object recognition. On a digital computer, the seemingly simple task of recognizing a face in a photo turns out to be tremendously difficult, and even the best current programs don't do it very well. The human brain, however, reliably solves this problem in a fraction of a second. The process feels almost effortless, but this is only because our brains are heavily optimized for it. Other tasks that are computationally a great deal simpler, such as adding pairs of hundred-digit numbers, feel more difficult because the human brain is not adapted to execute them efficiently.

The computational functions of brain are studied both by neuroscientists and computer scientists. There have been several attempts to build electronic computers that operate on brainlike principles, including a supercomputer called the Connection Machine, but to date none of them has achieved notable success. Brains have several advantages that are difficult to duplicate in an electronic device, including (1) the microscopic size of the processing elements, (2) the three-dimensional arrangement of connections, and (3) the fact that each neuron generates its own power (metabolically).

Genetics

Recent years have seen the first applications of genetic engineering techniques to the study of the brain. The most common subjects are mice, because the technical tools are more advanced for this species than for any other. It is now possible with relative ease to "knock out" or mutate a wide variety of genes, and then examine the effects on brain function. More sophisticated approaches are also beginning to be used: for example, using the Cre-Lox recombination method it is possible to activate or inactivate genes in specific parts of the brain, at specific times.

History of its study

Early views were divided as to whether the seat of the soul lies in the brain or heart. On one hand, it was impossible to miss the fact that awareness feels like it is localized in the head, and that blows to the head can cause unconsciousness much more easily than blows to the chest, and that shaking the head causes dizziness. On the other hand, the brain to a superficial examination seems inert, whereas the heart is constantly beating. Cessation of the heartbeat means death; strong emotions produce changes in the heartbeat; and emotional distress often produces a sensation of pain in the region of the heart ("heartache"). Aristotle favored the heart, and thought that the function of the brain is merely to cool the blood. Democritus, the inventor of the atomic theory of matter, favored a three-part soul, with intellect in the head, emotion in the heart, and lust in the vicinity of the liver. Hippocrates, the "father of medicine", was entirely in favor of the brain. In On the Sacred Disease, his account of epilepsy, he wrote:

Men ought to know that from nothing else but the brain come joys, delights, laughter and sports, and sorrows, griefs, despondency, and lamentations. ... And by the same organ we become mad and delirious, and fears and terrors assail us, some by night, and some by day, and dreams and untimely wanderings, and cares that are not suitable, and ignorance of present circumstances, desuetude, and unskilfulness. All these things we endure from the brain, when it is not healthy…

—Hippocrates, On the Sacred Disease[1]

The famous Roman physician Galen also advocated the importance of the brain, and theorized in some depth about how it might work. Even after physicians and philosophers had accepted the primacy of the brain, though, the idea of the heart as seat of intelligence continued to survive in popular idioms, such as "learning something by heart".[2] Galen did a masterful job of tracing out the anatomical relationships between brain, nerves, and muscles, demonstrating that all muscles in the body are connected to the brain via a branching network of nerves. He postulated that nerves activate muscles mechanically, by carrying a mysterious substance he called pneumata psychikon, usually translated as "animal spirits". His ideas were widely known during the Middle Ages, but not much further progress came until the Renaissance, when detailed anatomical study resumed, combined with the theoretical speculations of Descartes and his followers. Descartes, like Galen, thought of the nervous system in hydraulic terms. He believed that the highest cognitive functions—language in particular—are carried out by a non-physical res cogitans, but that the majority of behaviors of humans and animals could be explained mechanically. The first real progress toward a modern understanding of nervous function, though, came from the investigations of Luigi Galvani, who discovered that a shock of static electricity applied to an exposed nerve of a dead frog could cause its leg to contract.

The ensuing history of brain research can perhaps be epitomized by a quip from Floyd Bloom: "The gains in brain are mainly in the stain". The purport of this line is that progress in brain research has come for the most part not from theoretical work, but from advances in technology. Each major advance in understanding has followed more or less directly from the development of a new method of investigation. Until the early years of the 20th century, the most important advances were literally derived from new stains. Particularly critical was the invention of the Golgi stain, which (when correctly used) stains only a small, and apparently random, fraction of neurons, but stains them in their entirety, including cell body, dendrites, and axon. Without such a stain, brain tissue under a microscope appears as an impenetrable tangle of protoplasmic fibers, in which it is impossible to determine any structure. In the hands of Camillo Golgi, and especially of the Spanish neuroanatomist Santiago Ramon y Cajal, the new stain revealed hundreds of distinct types of neurons, each with its own unique dendritic structure and pattern of connectivity.

In the 20th century, progress in electronics enabled investigation of the electrical properties of nerve cells, culminating in the work by Alan Hodgkin, Andrew Huxley, and others on the biophysics of the action potential, and the work of Bernard Katz and others on the electrochemistry of the synapse. The earliest studies used special preparations, such as the "fast escape response" system of the squid, which involves a giant axon as thick as a pencil lead, and giant synapses connecting to this axon. Steady improvements in electrodes and electronics allowed ever finer levels of resolution. These studies complemented the anatomical picture with a conception of the brain as a dynamic entity. Reflecting the new understanding, in 1942 Charles Sherrington visualized the workings of the brain in action in somewhat breathless terms:

The great topmost sheet of the mass, that where hardly a light had twinkled or moved, becomes now a sparkling field of rhythmic flashing points with trains of traveling sparks hurrying hither and thither. … It is as if the Milky Way entered upon some cosmic dance. Swiftly the head mass becomes an enchanted loom where millions of flashing shuttles weave a dissolving pattern, always a meaningful pattern though never an abiding one; a shifting harmony of subpatterns.

—Sherrington, 1942, Man on his Nature[3]

References

  • Abbott, LF (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press. ISBN 9780262541855. [1].
  • Aboitiz, F; Morales D, Montiel J (2003). "The evolutionary origin of the mammalian isocortex: Towards an integrated developmental and functional approach.". Behav Brain Sci 26: 535–52. doi:10.1017/S0140525X03000128. . [2].
  • Antle, MC; Silver R (2005). "Orchestrating time: arrangements of the brain circadian clock." (PDF). Trends Neurosci 28: 145–51. doi:10.1016/j.tins.2005.01.003. [3].
  • Armstrong, E (1983). "Relative brain size and metabolism in mammals.". Science 220: 1302–4. doi:10.1126/science.6407108. PMID 6407108.
  • Balavoine, G; Adoutte A (2003). "The segmented Urbilateria: A testable scenario.". Int Comp Biology 43: 137–47. doi:10.1093/icb/43.1.137. [4].
  • Barton, RA; Harvey PH (2000). "Mosaic evolution of brain structure in mammals.". Nature 405: 1055–8. doi:10.1038/35016580. PMID 10890446.
  • Bloom, FE (1975). Schmidt FO, Worden FG, Swazey JP, Adelman G. ed. The Neurosciences, Paths of Discovery. MIT Press. ISBN 9780262230728.
  • Butler, AB (2000). "Chordate Evolution and the Origin of Craniates: An Old Brain in a New Head.". Anat Rec 261: 111–25. doi:10.1002/1097-0185(20000615)261:3<111::AID-AR6>3.0.CO;2-F. PMID 10867629. [5].
  • Calvin, WH (1996). How Brains Think. Basic Books. ISBN 9780465072781. [6].
  • Carew, TJ (2000). Behavioral Neurobiology: the Cellular Organization of Natural Behavior. Sinauer Associates. ISBN 9780878930920. [7].
  • Curchland, PS (1989). Neurophilosophy. MIT Press. ISBN 9780262530859. [8].
  • Clark, DD; Sokoloff L (1999). Siegel GJ, Agranoff BW, Albers RW, Fisher SK, Uhler MD. ed. Basic Neurochemistry: Molecular, Cellular and Medical Aspects. Philadelphia: Lippincott. pp. 637–70. ISBN 9780397518203.
  • Cooper, JR; Bloom FE, Roth RH (2003). The Biochemical Basis of Neuropharmacology. Oxford University Press US. ISBN 9780195140088. [9].
  • Dowling, JE (2001). Neurons and Networks. Harvard University Press. ISBN 9780674004627. [10].
  • Finger, S (2001). Origins of Neuroscience. Oxford University Press. ISBN 9780195146943. [11].
  • Finlay, BL; Darlington RB, Nicastro N (2001). "Developmental structure in brain evolution." (PDF). Behav Brain Sci 20: 263–308. PMID 11530543. [12].
  • Gehring, WJ (2005). "New Perspectives on Eye Development and the Evolution of Eyes and Photoreceptors: The Evolution of Eyes and Brain." (Full text). J Heredity 96: 171–184. doi:10.1093/jhered/esi027. PMID 15653558. [13]. Retrieved on 26 April 2008.
  • Grillner, S; Hellgren J, Ménard A, Saitoh K, Wikström MA (2005). "Mechanisms for selection of basic motor programs—roles for the striatum and pallidum.". Trends Neurosci 28: 364–70. doi:10.1016/j.tins.2005.05.004. PMID 15935487.
  • Grillner, S; Wallén P (2002). "Cellular bases of a vertebrate locomotor system-steering, intersegmental and segmental co-ordination and sensory control.". Brain Res Brain Res Rev 40: 92–106. doi:10.1016/S0165-0173(02)00193-5. PMID 12589909.
  • Gurney, K; Prescott TJ, Wickens JR, Redgrave P (2004). "Computational models of the basal ganglia: from robots to membranes.". Trends Neurosci 27: 453–459. doi:10.1016/j.tins.2004.06.003. PMID 15271492.
  • Hart, WD (1996). Guttenplan S. ed. A Companion to the Philosophy of Mind. Blackwell. pp. 265–7.
  • Hendrickson, R (2000). The Facts on File Encyclopedia of Word and Phrase Origins. New York: Facts on File. ISBN 978-0816040889.
  • Hippocrates (400 B.C.E.). On the Sacred Disease. [14].
  • Hobert, O (2005), "Specification of the nervous system", in The C. elegans Research Community, Wormbook, doi:10.1895/wormbook.1.12.1
  • Jerison, HJ (1973). Evolution of the Brain and Intelligence. Academic Press. ISBN 9780123852502. [15].
  • Jones, EG (1985). The Thalamus. Plenum Press. ISBN 9780306418563. [16].
  • Kandel, ER; Schwartz JH, Jessel TM (2000). Principles of Neural Science. McGraw-Hill Professional. ISBN 9780838577011.
  • Kandel, ER (2007). In Search of Memory: The Emergence of a New Science of Mind. WW Norton. ISBN 9780393329377. [17].
  • Konopka, RJ; Benzer S (1971). "Clock mutants of Drosophila melanogaster.". PNAS 68: 2112–6. doi:10.1073/pnas.68.9.2112. PMID 5002428.
  • Lacey, A (1996). A Dictionary of Philosophy. Routledge.
  • Nickel, M; Vitello M, Brümmer F (2002). "Dynamics and cellular movements in the locomotion of the sponge Tethya wilhelma.". Integr Comp Biol 42: 1285.
  • Northcutt, RG (1981). "Evolution of the telencephalon in nonmammals.". Ann Rev Neurosci 4: 301–50. doi:10.1146/annurev.ne.04.030181.001505. PMID 7013637. [18].
  • Northcutt, RG (2008). "Forebrain evolution in bony fishes.". Brain Res Bull 75: 191–205. doi:10.1016/j.brainresbull.2007.10.058. PMID 18331871.
  • Parent, A; Carpenter MB (1995). Carpenter's Human Neuroanatomy. Williams & Wilkins. ISBN 9780683067521. [19].
  • Paus, T; Collins DL, Evans AC, Leonard G, Pike B, Zijdenbos A (2001). "Maturation of white matter in the human brain: a review of magnetic resonance studies.". Brain Res Bull 54: 255–66. doi:10.1016/S0361-9230(00)00434-2. PMID 11287130.
  • Piccolino, M (2002). "Fifty years of the Hodgkin-Huxley era." (PDF). Trends Neurosci 25: 552–3. PMID 12392928. [20]. Retrieved on 24 October 2008.
  • Puelles, L (2001). "Thoughts on the development, structure and evolution of the mammalian and avian telencephalic pallium.". Philos Trans R Soc Lond B Biol Sci 356: 1583–98. doi:10.1098/rstb.2001.0973. PMID 11604125. [21].
  • Purves, D; Lichtman J (1985). Principles of Neural Development. Sinauer Associates. ISBN 9780878937448. [22].
  • Raichle, M; Gusnard DA (2002). "Appraising the brain's energy budget". PNAS 99: 10237–9. doi:10.1073/pnas.172399499. PMID 12149485.
  • Reiner, A; Yamamoto K, Karten HJ (2005). "Organization and evolution of the avian forebrain". Anat Rec a Discov Mol Cell Evol Biol 287: 1080–102. PMID 16206213. [23]. Retrieved on 12 October 2008.
  • Ridley, M (2003). Nature via Nurture: Genes, Experience, and What Makes Us Human. Forth Estate. ISBN 9780060006785. [24].
  • Safi, K; Seid MA, Dechmann DKN (2005). "Bigger is not always better: when brains get smaller". Biol Lett 1: 283–6. doi:10.1098/rsbl.2005.0333. PMID 17148188.
  • Saitoh, K; Ménard A, Grillner S (2007). "Tectal control of locomotion, steering, and eye movements in lamprey.". J Neurophysiol 97: 3093–108. doi:10.1152/jn.00639.2006. PMID 17303814. [25].
  • Salas, C; Broglio C, Rodríguez F (2003). "Evolution of forebrain and spatial cognition in vertebrates: conservation across diversity". Brain Behav Evol 62: 72–82. doi:10.1159/000072438.
  • Savage, MV; Gillooly JF, Woodruff WH, West GB, Allen AP, Enquist BJ, Brown JH (2004). "The predominance of quarter-power scaling in biology". Functional Ecol 18: 257–82. doi:10.1111/j.0269-8463.2004.00856.x.
  • Schmidt-Rhaesa, A (2007). The Evolution of Organ Systems. Oxford University Press. ISBN 9780198566694. [26].
  • Searle, J (1999). Mind, Language and Society. Basic Books. ISBN 9780465045211. [27].
  • Shepherd GM (1994). Neurobiology. Oxford University Press. ISBN 9780195088434. [28].
  • Shin, HS; Bargiello TA, Clark BT, Jackson FR, Young MW (1985). "An unusual coding sequence from a Drosophila clock gene is conserved in vertebrates.". Nature 317: 445–8. doi:10.1038/317445a0. PMID 2413365.
  • Storrow, HA (1969). Outline of Clinical Psychiatry. Appleton-Century-Crofts. ISBN 9780390850751.
  • Striedter, GF (2005). Principles of Brain Evolution. Sinauer Associates. ISBN 9780878938209. [29].
  • Swaab, DF; Aminoff MJ, Boller F (2003). The Human Hypothalamus. Elsevier. ISBN 9780444513571. [30].
  • Tonegawa, S; Nakazawa K, Wilson MA (2003). "Genetic neuroscience of mammalian learning and memory". Philos Trans R Soc Lond B Biol Sci 358: 787–95. doi:10.1098/rstb.2002.1243. PMID 12740125.
  • van Hemmen, JL; Sejnowski TJ (2005). 23 Problems in Systems Neuroscience. Oxford University Press. ISBN 9780195148220. [31].
  • van Praag, H; Kemperann G, Gage FH (2000). "Neural consequences of environmental enrichment". Nat Rev Neurosci 1: 191–8. PMID 11257907.
  • Wiesel, T (1982). "Postnatal development of the visual cortex and the influence of environment" (PDF). Nature 299: 583–91. doi:10.1038/299583a0. PMID 6811951. [32].
  • White, JG; Southgate E, Thomson JN, Brenner S (1986). "The Structure of the Nervous System of the Nematode Caenorhabditis elegans". Phil Trans Roy Soc London (Biol) 314: 1–340. doi:10.1098/rstb.1986.0056.
  • Willmer P (1990). Invertebrate Relationships: Patterns in Animal Evolution. Cambridge University Press. ISBN 9780521337120. [33].
  • Wong, R (1999). "Retinal waves and visual system development.". Ann Rev Neurosci 22: 29–47. doi:10.1146/annurev.neuro.22.1.29. PMID 10202531.
  • Wyllie, E; Gupta A, Lachhwani DK (2005). The Treatment of Epilepsy: Principles and Practice. Lippincott Williams & Wilkins. ISBN 9780781749954. [34].
  • "Flybrain: An online atlas and database of the drosophila nervous system".
  • "WormBook: The online review of c. elegans biology".

External links

  1. ↑ Hippocrates, On the Sacred Disease
  2. ↑ Encyclopedia of Word and Phrase Origins
  3. ↑ Sherrington, Man on is Nature
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