Editing Cerebellum (section)

===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>