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      Computational Principles of Supervised Learning in the Cerebellum

      1 , 2

      Annual Review of Neuroscience

      Annual Reviews

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          Abstract

          Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

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          Most cited references 169

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          A theory of cerebellar cortex.

           D. W. M. Marr (1969)
          1. A detailed theory of cerebellar cortex is proposed whose consequence is that the cerebellum learns to perform motor skills. Two forms of input-output relation are described, both consistent with the cortical theory. One is suitable for learning movements (actions), and the other for learning to maintain posture and balance (maintenance reflexes).2. It is known that the cells of the inferior olive and the cerebellar Purkinje cells have a special one-to-one relationship induced by the climbing fibre input. For learning actions, it is assumed that:(a) each olivary cell responds to a cerebral instruction for an elemental movement. Any action has a defining representation in terms of elemental movements, and this representation has a neural expression as a sequence of firing patterns in the inferior olive; and(b) in the correct state of the nervous system, a Purkinje cell can initiate the elemental movement to which its corresponding olivary cell responds.3. Whenever an olivary cell fires, it sends an impulse (via the climbing fibre input) to its corresponding Purkinje cell. This Purkinje cell is also exposed (via the mossy fibre input) to information about the context in which its olivary cell fired; and it is shown how, during rehearsal of an action, each Purkinje cell can learn to recognize such contexts. Later, when the action has been learnt, occurrence of the context alone is enough to fire the Purkinje cell, which then causes the next elemental movement. The action thus progresses as it did during rehearsal.4. It is shown that an interpretation of cerebellar cortex as a structure which allows each Purkinje cell to learn a number of contexts is consistent both with the distributions of the various types of cell, and with their known excitatory or inhibitory natures. It is demonstrated that the mossy fibre-granule cell arrangement provides the required pattern discrimination capability.5. The following predictions are made.(a) The synapses from parallel fibres to Purkinje cells are facilitated by the conjunction of presynaptic and climbing fibre (or post-synaptic) activity.(b) No other cerebellar synapses are modifiable.(c) Golgi cells are driven by the greater of the inputs from their upper and lower dendritic fields.6. For learning maintenance reflexes, 2(a) and 2(b) are replaced by2'. Each olivary cell is stimulated by one or more receptors, all of whose activities are usually reduced by the results of stimulating the corresponding Purkinje cell.7. It is shown that if (2') is satisfied, the circuit receptor --> olivary cell --> Purkinje cell --> effector may be regarded as a stabilizing reflex circuit which is activated by learned mossy fibre inputs. This type of reflex has been called a learned conditional reflex, and it is shown how such reflexes can solve problems of maintaining posture and balance.8. 5(a), and either (2) or (2') are essential to the theory: 5(b) and 5(c) are not absolutely essential, and parts of the theory could survive the disproof of either.
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            Learning to predict by the methods of temporal differences

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              A theory of cerebellar function

               James S Albus (1971)
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                Author and article information

                Journal
                Annual Review of Neuroscience
                Annu. Rev. Neurosci.
                Annual Reviews
                0147-006X
                1545-4126
                July 08 2018
                July 08 2018
                : 41
                : 1
                : 233-253
                Affiliations
                [1 ]Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305, USA;
                [2 ]Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
                Article
                10.1146/annurev-neuro-080317-061948
                6056176
                29986160
                © 2018

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