Adaptive filter model of the cerebellum

By M. Fujita. 1982

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “The evolutionarily conserved, highly regular structure of the cerebellar cortex has encouraged much speculation as to how the computations required for these functions are achieved (Figure 2A; Braitenberg, 1961; Eccles et al., 1967; Marr, 1969; Albus, 1971; Fujita, 1982; Pellionisz and Llinas, 1982;”
A computational model of four regions of the cerebellum based on feedback-error learning (2004, background): “This is equivalent to Fujita's equations (2.20) and (2.21) ( Fujita 1982a ).”
Timing rules for synaptic plasticity matched to behavioral function (2016, unknown): “In addition, it suggests that there is sufficient temporal precision in the patterns of activation of the parallel fibers to provide a substrate for the temporally precise plasticity (Fujita, 1982; Medina and Mauk, 2000; Yamazaki and Tanaka, 2009; but see Johansson et al., 2014).”
Are mushroom bodies cerebellum-like structures? (2011, methodology): “An adaptive filter model for the function of the cerebellum was first proposed by Fujita (Fujita,1982a,1982b).”
Computational principles of sensorimotor control that minimize uncertainty and variability (2007, background): “The efficacy of these synapses appears to be modulated by input from climbing fibres, originating in the inferior olive, via long-term depression (Marr, 1969; Albus, 1971; Fujita, 1982; Ito, 2001).”
Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, unknown): “Discrepancies with Dimensionality Expansion Theory and Potential Resolutions At a basic level, GrC activity that is dominated by low dimensional, task-encoding L5 dynamics in expert mice differs from frameworks emphasizing GrC dimensionality expansion (Albus, 1971; Fujita, 1982; Marr, 1969).”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, background): “…enabling downstream cerebellar circuits to perform more effective associative learning (Albus, 1971; D’Angelo and De Zeeuw, 2009; Marr, 1969; Medina and Mauk, 2000; Schweighofer et al., 2001; Tyrrell and Willshaw, 1992), adaptive filtering (Fujita, 1982), and binary addressing (Kanerva, 1988).”
At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters (2015, background): “Thus, cerebellar output—and hence granular-layer output—is more tightly constrained in motorcontrol tasks resembling the vestibulo-ocular reflex than in eyeblink conditioning [3].”
Consensus Paper: The Cerebellum's Role in Movement and Cognition (2013, methodology): “Two types of computational model of the cerebellum have been developed: network models (such as the Simple Perceptron [228] and adaptive filters [229, 230] and control system models (such as the adaptive control [231] and modelbased control [193]).”
A hierarchical neural-network model for control and learning of voluntary movement (2004, methodology): “Although the Marr-Albus model of the cerebellum as a spatial pattern discriminator does not give a sufficient account of the processing of temporal patterns, which is essential for the control of movement, Fujita (1982a) expanded the Marr-Albus model and proposed an adaptive filter model of the ce”
Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior (2017, background): “Later work instead postulated that groups of granule neurons encode elementary features of the mossy-fiber inputs, allowing sensorimotor signals to be efficiently represented through a combination of basis functions or ‘filters’ (Sejnowski, 1977; Fujita, 1982; Dean and Porrill, 2008).”