Editing Cerebellum (section)

== Function ==
The strongest clues to the function of the cerebellum have come from examining the consequences of damage to it. Animals and humans with cerebellar dysfunction show, above all, problems with motor control, on the same side of the body as the damaged part of the cerebellum. They continue to be able to generate motor activity but lose precision, producing erratic, uncoordinated, or incorrectly timed movements. A standard test of cerebellar function is to reach with the tip of the finger for a target at arm's length: A healthy person will move the fingertip in a rapid straight trajectory, whereas a person with cerebellar damage will reach slowly and erratically, with many mid-course corrections. Deficits in non-motor functions are more difficult to detect. Thus, the general conclusion reached decades ago is that the basic function of the cerebellum is to calibrate the detailed form of a movement, not to initiate movements or to decide which movements to execute.<ref name=Ghez/>

Prior to the 1990s the function of the cerebellum was almost universally believed to be purely motor-related, but research now points to an expanded role of cerebellar connectivity beyond basic motoric functions.<ref>{{cite journal | last = Boonstra | first = Jackson Tyler | title = The Cerebellar Connectome | journal = Behav Brain Res | volume = | pages = 115457 | date = 2025-01-28 | pmid = 39884319 | doi = 10.1016/j.bbr.2025.115457 | doi-access = free }}</ref> [[Functional imaging]] studies have shown cerebellar activation in relation to language, attention, and mental imagery; correlation studies have shown interactions between the cerebellum and non-motor areas of the cerebral cortex; and a variety of non-motor symptoms have been recognized in people with damage that appears to be confined to the cerebellum.<ref>{{cite book | vauthors = Rapp B |title=The Handbook of Cognitive Neuropsychology: What Deficits Reveal about the Human Mind |year=2001 |publisher=Psychology Press |isbn=978-1-84169-044-5 |page=481 }}</ref><ref name=Doya>{{cite journal | vauthors = Doya K | title = Complementary roles of basal ganglia and cerebellum in learning and motor control | journal = Current Opinion in Neurobiology | volume = 10 | issue = 6 | pages = 732–9 | date = December 2000 | pmid = 11240282 | doi = 10.1016/S0959-4388(00)00153-7 | s2cid = 10962570 }}</ref> In particular, the [[cerebellar cognitive affective syndrome]] or Schmahmann's syndrome<ref>{{cite journal | vauthors = Manto M, Mariën P | title = Schmahmann's syndrome - identification of the third cornerstone of clinical ataxiology | journal = Cerebellum & Ataxias | volume = 2 | pages = 2 | date = 2015 | pmid = 26331045 | pmc = 4552302 | doi = 10.1186/s40673-015-0023-1 | doi-access = free }}</ref> has been described in adults<ref>{{cite journal | vauthors = Schmahmann JD, Sherman JC | title = The cerebellar cognitive affective syndrome | journal = Brain | volume = 121 | issue = 4 | pages = 561–79 | date = April 1998 | pmid = 9577385 | doi = 10.1093/brain/121.4.561 | doi-access = free }}</ref> and children.<ref>{{cite journal | vauthors = Levisohn L, Cronin-Golomb A, Schmahmann JD | title = Neuropsychological consequences of cerebellar tumour resection in children: cerebellar cognitive affective syndrome in a paediatric population | journal = Brain | volume = 123 | issue = 5 | pages = 1041–50 | date = May 2000 | pmid = 10775548 | doi = 10.1093/brain/123.5.1041 | doi-access = free }}</ref> Estimates based on functional mapping of the cerebellum using [[Functional magnetic resonance imaging|functional MRI]] suggest that more than half of the cerebellar cortex is interconnected with association zones of the cerebral cortex.<ref>{{cite journal | vauthors = Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT | title = The organization of the human cerebellum estimated by intrinsic functional connectivity | journal = Journal of Neurophysiology | volume = 106 | issue = 5 | pages = 2322–45 | date = November 2011 | pmid = 21795627 | pmc = 3214121 | doi = 10.1152/jn.00339.2011 }}</ref>

Kenji Doya has argued that the cerebellum's function is best understood not in terms of the behaviors it affects, but the neural computations it performs; the cerebellum consists of a large number of more or less independent modules, all with the same geometrically regular internal structure, and therefore all, it is presumed, performing the same computation. If the input and output connections of a module are with motor areas (as many are), then the module will be involved in motor behavior; but, if the connections are with areas involved in non-motor cognition, the module will show other types of behavioral correlates. Thus the cerebellum has been implicated in the regulation of many differing functional traits such as affection, emotion including emotional body language perception<ref>{{Cite book |url=https://www.worldcat.org/oclc/1338132789 |title=The emotional cerebellum |date=2022 |publisher=Springer |others=Michael Adamaszek, Mario Manto, Dennis J. L. G. Schutter |isbn=978-3-030-99550-8 |location=Cham, Switzerland |oclc=1338132789}}</ref> and behavior.<ref>{{cite journal | vauthors = Hernáez-Goñi P, Tirapu-Ustárroz J, Iglesias-Fernández L, Luna-Lario P | title = Participación del cerebelo en la regulación del afecto, la emoción y la conducta | language = es | journal = Revista de Neurología | volume = 51 | issue = 10 | pages = 597–609 | date = November 2010 | pmid = 21069639 | trans-title = The role of the cerebellum in the regulation of affection, emotion and behavior | doi = 10.33588/rn.5110.2010394 }}</ref><ref>{{cite journal | vauthors = Turner BM, Paradiso S, Marvel CL, Pierson R, Boles Ponto LL, Hichwa RD, Robinson RG | title = The cerebellum and emotional experience | journal = Neuropsychologia | volume = 45 | issue = 6 | pages = 1331–41 | date = March 2007 | pmid = 17123557 | pmc = 1868674 | doi = 10.1016/j.neuropsychologia.2006.09.023 }}</ref> The cerebellum, Doya proposes, is best understood as predictive action selection based on "internal models" of the environment or a device for [[supervised learning]], in contrast to the [[basal ganglia]], which perform [[reinforcement learning]], and the [[cerebral cortex]], which performs [[unsupervised learning]].<ref name=Doya/><ref>{{cite journal | vauthors = Doya K | title = What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? | journal = Neural Networks | volume = 12 | issue = 7–8 | pages = 961–974 | date = October 1999 | pmid = 12662639 | doi = 10.1016/S0893-6080(99)00046-5 }}</ref> Three decades of brain research have led to the proposal that the cerebellum generates optimized mental models and interacts closely with the cerebral cortex, where updated internal models are experienced as creative intuition ("a ha") in working memory.<ref>{{Cite book|title=The new revolution in psychology and the neurosciences| vauthors = Manto M, Marvel C, Vandervert L|publisher=Springer Nature|year=2022|isbn= 9783031060922|location=Switzerland}}</ref> 

===Principles===
The comparative simplicity and regularity of the cerebellar anatomy led to an early hope that it might imply a similar simplicity of computational function, as expressed in one of the first books on cerebellar electrophysiology, ''The Cerebellum as a Neuronal Machine'' by [[John Eccles (neurophysiologist)|John C. Eccles]], [[Masao Ito]], and [[János Szentágothai]].<ref>{{cite book|title=The Cerebellum as a Neuronal Machine |url=https://archive.org/details/cerebellumasneur0000eccl |url-access=registration |publisher=Springer-Verlag |year=1967 |vauthors=Eccles JC, Ito M, Szentágothai J }}</ref> Although a full understanding of cerebellar function has remained elusive, at least four principles have been identified as important: (1) feedforward processing, (2) divergence and convergence, (3) modularity, and (4) plasticity.

# '''Feedforward processing''': The cerebellum differs from most other parts of the brain (especially the cerebral cortex) in that the signal processing is almost entirely [[feed forward (control)#Physiological feed-forward system|feedforward]]—that is, signals move unidirectionally through the system from input to output, with very little recurrent internal transmission. The small amount of recurrence that does exist consists of mutual inhibition; there are no mutually excitatory circuits. This feedforward mode of operation means that the cerebellum, in contrast to the cerebral cortex, cannot generate self-sustaining patterns of neural activity. Signals enter the circuit, are processed by each stage in sequential order, and then leave. As Eccles, Ito, and Szentágothai wrote, "This elimination in the design of all possibility of reverberatory chains of neuronal excitation is undoubtedly a great advantage in the performance of the cerebellum as a computer, because what the rest of the nervous system requires from the cerebellum is presumably not some output expressing the operation of complex reverberatory circuits in the cerebellum but rather a quick and clear response to the input of any particular set of information."<ref>''The Cerebellum as a Neuronal Machine'', p. 311</ref>
# '''Divergence and convergence''': In the human cerebellum, information from 200 million [[mossy fiber (cerebellum)|mossy fiber]] inputs is expanded to 40 billion [[cerebellum granule cell|granule cell]]s, whose [[parallel fiber]] outputs then converge onto 15 million [[Purkinje cell]]s.<ref name=SOB/> Because of the way that they are lined up longitudinally, the 1000 or so Purkinje cells belonging to a microzone may receive input from as many as 100 million parallel fibers, and focus their own output down to a group of less than 50 [[deep cerebellar nuclei|deep nuclear]] cells.<ref name=AppsGarwicz/> Thus, the cerebellar network receives a modest number of inputs, processes them very extensively through its rigorously structured internal network, and sends out the results via a very limited number of output cells.
# '''Modularity''': The cerebellar system is functionally divided into more or less independent modules, which probably number in the hundreds to thousands. All modules have a similar internal structure, but different inputs and outputs. A module (a multizonal microcompartment in the terminology of Apps and Garwicz) consists of a small cluster of neurons in the inferior olivary nucleus, a set of long narrow strips of Purkinje cells in the cerebellar cortex (microzones), and a small cluster of neurons in one of the deep cerebellar nuclei. Different modules share input from mossy fibers and parallel fibers, but in other respects they appear to function independently—the output of one module does not appear to significantly influence the activity of other modules.<ref name=AppsGarwicz/>
# '''Plasticity''': The synapses between parallel fibers and Purkinje cells, and the synapses between mossy fibers and deep nuclear cells, are both susceptible to modification of their strength. In a single cerebellar module, input from as many as a billion parallel fibers converges onto a group of less than 50 deep nuclear cells, and the influence of each parallel fiber on those nuclear cells is adjustable. This arrangement gives tremendous flexibility for fine-tuning the relationship between the cerebellar inputs and outputs.<ref name=Boyden>{{cite journal | vauthors = Boyden ES, Katoh A, Raymond JL | s2cid = 1310007 | title = Cerebellum-dependent learning: the role of multiple plasticity mechanisms | journal = Annual Review of Neuroscience | volume = 27 | pages = 581–609 | year = 2004 | pmid = 15217344 | doi = 10.1146/annurev.neuro.27.070203.144238 }}</ref>

===Learning===
There is considerable evidence that the cerebellum plays an essential role in some types of motor learning. The tasks where the cerebellum most clearly comes into play are those in which it is necessary to make fine adjustments to the way an action is performed. There has, however, been much dispute about whether learning takes place within the cerebellum itself, or whether it merely serves to provide signals that promote learning in other brain structures.<ref name="Boyden"/> Most theories that assign learning to the circuitry of the cerebellum are derived from the ideas of [[David Marr (neuroscientist)|David Marr]]<ref name=Marr>{{cite journal | vauthors = Marr D | title = A theory of cerebellar cortex | journal = Journal of Physiology | volume = 202 | issue = 2 | pages = 437–70 | date = June 1969 | pmid = 5784296 | pmc = 1351491 | doi = 10.1113/jphysiol.1969.sp008820 | author-link = David Marr (neuroscientist) }}</ref> and [[James S. Albus|James Albus]],<ref name=Albus>{{cite journal |title=A theory of cerebellar function | vauthors = Albus JS |journal=Math. Biosciences |year=1971 |volume=10 |issue=1–2 |pages=25–61 |doi=10.1016/0025-5564(71)90051-4| citeseerx = 10.1.1.14.7524 }}</ref> who postulated that [[climbing fiber]]s provide a teaching signal that induces synaptic modification in [[parallel fiber]]–[[Purkinje cell]] synapses.<ref name=Houk1996/> Marr assumed that climbing fiber input would cause synchronously activated parallel fiber inputs to be strengthened. Most subsequent cerebellar-learning models, however, have followed Albus in assuming that climbing fiber activity would be an error signal, and would cause synchronously activated parallel fiber inputs to be weakened. Some of these later models, such as the ''Adaptive Filter'' model of Fujita<ref>{{cite journal | vauthors = Fujita M | title = Adaptive filter model of the cerebellum | journal = Biological Cybernetics | volume = 45 | issue = 3 | pages = 195–206 | year = 1982 | pmid = 7171642 | doi = 10.1007/BF00336192 | s2cid = 3695770 }}</ref> made attempts to understand cerebellar function in terms of [[optimal control]] theory.

The idea that climbing fiber activity functions as an error signal has been examined in many experimental studies, with some supporting it but others casting doubt.<ref name=Simpson/> In a pioneering study by Gilbert and Thach from 1977, Purkinje cells from monkeys learning a reaching task showed increased complex spike activity—which is known to reliably indicate activity of the cell's climbing fiber input—during periods when performance was poor.<ref>{{cite journal | vauthors = Gilbert PF, Thach WT | title = Purkinje cell activity during motor learning | journal = Brain Research | volume = 128 | issue = 2 | pages = 309–28 | date = June 1977 | pmid = 194656 | doi = 10.1016/0006-8993(77)90997-0 | s2cid = 40799652 }}</ref> Several studies of motor learning in cats observed complex spike activity when there was a mismatch between an intended movement and the movement that was actually executed. Studies of the [[vestibulo-ocular reflex]] (which stabilizes the visual image on the retina when the head turns) found that climbing fiber activity indicated "retinal slip", although not in a very straightforward way.<ref name=Simpson/>

One of the most extensively studied cerebellar learning tasks is the [[eyeblink conditioning]] paradigm, in which a neutral conditioned stimulus (CS) such as a tone or a light is repeatedly paired with an unconditioned stimulus (US), such as an air puff, that elicits a blink response. After such repeated presentations of the CS and US, the CS will eventually elicit a blink before the US, a conditioned response or CR. Experiments showed that lesions localized either to a specific part of the interposed nucleus (one of the deep cerebellar nuclei) or to a few specific points in the cerebellar cortex would abolish learning of a conditionally timed blink response. If cerebellar outputs are pharmacologically inactivated while leaving the inputs and intracellular circuits intact, learning takes place even while the animal fails to show any response, whereas, if intracerebellar circuits are disrupted, no learning takes place—these facts taken together make a strong case that the learning, indeed, occurs inside the cerebellum.<ref>{{cite journal | vauthors = Christian KM, Thompson RF | title = Neural substrates of eyeblink conditioning: acquisition and retention | journal = Learning & Memory | volume = 10 | issue = 6 | pages = 427–55 | year = 2003 | pmid = 14657256 | doi = 10.1101/lm.59603 | doi-access = free }}</ref>

===Theories and computational models===
[[File:Model of Cerebellar Perceptron.jpg|thumb|right|Model of a cerebellar perceptron, as formulated by [[James Albus]] ]]
The large base of knowledge about the anatomical structure and behavioral functions of the cerebellum have made it a fertile ground for theorizing—there are perhaps more theories of the function of the cerebellum than of any other part of the brain. The most basic distinction among them is between "learning theories" and "performance theories"—that is, theories that make use of [[synaptic plasticity]] within the cerebellum to account for its role in learning, versus theories that account for aspects of ongoing behavior on the basis of cerebellar signal processing. Several theories of both types have been formulated as [[mathematical model]]s and simulated using computers.<ref name=Houk1996>{{cite journal |title=Models of the cerebellum and motor learning |journal=Behav. Brain Sci. |volume=19 |pages=368–383 |year=1996 |vauthors=Houk JC, Buckingham JT, Barto AG |doi=10.1017/S0140525X00081474 |issue=3 |url=http://www-anw.cs.umass.edu/pubs/1995_96/houk_bb_BBS96.pdf |url-status=dead |archive-url=https://web.archive.org/web/20170809050735/http://www-anw.cs.umass.edu/pubs/1995_96/houk_bb_BBS96.pdf |archive-date=2017-08-09 |citeseerx=10.1.1.118.2997 }}</ref>

Perhaps the earliest "performance" theory was the "delay line" hypothesis of [[Valentino Braitenberg]]. The original theory put forth by Braitenberg and Roger Atwood in 1958 proposed that slow propagation of signals along parallel fibers imposes predictable delays that allow the cerebellum to detect time relationships within a certain window.<ref>{{cite journal | vauthors = Braitenberg V, Atwood RP | title = Morphological observations on the cerebellar cortex | journal = Journal of Comparative Neurology | volume = 109 | issue = 1 | pages = 1–33 | date = February 1958 | pmid = 13563670 | doi = 10.1002/cne.901090102 | s2cid = 8989536 }}</ref> Experimental data did not support the original form of the theory, but Braitenberg continued to argue for modified versions.<ref>{{cite journal | vauthors = Braitenberg V, Heck D, Sultan F | title = The detection and generation of sequences as a key to cerebellar function: experiments and theory | journal = Behavioral and Brain Sciences | volume = 20 | issue = 2 | pages = 229–45; discussion 245–77 | date = June 1997 | pmid = 10096998 | doi = 10.1017/s0140525x9700143x | s2cid = 36802745 }}</ref> The hypothesis that the cerebellum functions essentially as a timing system has also been advocated by [[Richard Ivry]].<ref>{{cite journal | vauthors = Ivry RB, Spencer RM, Zelaznik HN, Diedrichsen J | title = The cerebellum and event timing | journal = Annals of the New York Academy of Sciences | volume = 978 | issue = 1 | pages = 302–17 | date = December 2002 | pmid = 12582062 | doi = 10.1111/j.1749-6632.2002.tb07576.x | bibcode = 2002NYASA.978..302I | s2cid = 27237058 | doi-access = free }}</ref> Another influential "performance" theory is the [[Tensor network theory]] of Pellionisz and [[Rodolfo Llinás|Llinás]], which provided an advanced mathematical formulation of the idea that the fundamental computation performed by the cerebellum is to transform sensory into motor coordinates.<ref>{{cite journal | vauthors = Pellionisz A, Llinás R | title = Space-time representation in the brain. The cerebellum as a predictive space-time metric tensor | journal = Neuroscience | volume = 7 | issue = 12 | pages = 2949–70 | year = 1982 | pmid = 7162624 | doi = 10.1016/0306-4522(82)90224-X | s2cid = 20520737 }}</ref>

Theories in the "learning" category almost all derive from publications by Marr and Albus. Marr's 1969 paper proposed that the cerebellum is a device for learning to associate elemental movements encoded by climbing fibers with mossy fiber inputs that encode the sensory context.<ref name=Marr/> Albus proposed in 1971 that a cerebellar Purkinje cell functions as a [[perceptron]], a neurally inspired abstract learning device.<ref name=Albus/> The most basic difference between the Marr and Albus theories is that Marr assumed that climbing fiber activity would cause parallel fiber synapses to be strengthened, whereas Albus proposed that they would be weakened. Albus also formulated his version as a [[software|software algorithm]] he called a CMAC (Cerebellar Model Articulation Controller), which has been tested in a number of applications.<ref>{{cite journal |title=CMAC: Reconsidering an old neural network |vauthors=Horváth G |journal=Intelligent Control Systems and Signal Processing |year=2003 |url=http://www.mit.bme.hu/~horvath/papers/CMAC_reconsidered.pdf |access-date=2009-12-24 |archive-date=2020-05-20 |archive-url=https://web.archive.org/web/20200520042015/http://home.mit.bme.hu/~horvath/papers/CMAC_reconsidered.pdf |url-status=dead }}</ref>