Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control.

By Nicolas Schweighofer, Kenji Doya, F. Lay. 2001

131 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Sensory Systems

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “As Marr-Albus theory was extended to other brain regions, such ‘ultra-sparse’ population coding formed the core of our current understanding of pattern separation, associative learning, and memory storage (Marr, 1971; Kanerva, 1988; Tsodyks et al., 1988; Rolls and Treves, 1990; Tyrrell and Willshaw,”
Silencing the majority of cerebellar granule cells uncovers their essential role in motor learning and consolidation. (2013, unknown): “, consolidation, should be affected as a consequence of a reduced capacity for effective plasticity inside the granular layer (Andreescu et al., 2011; Schweighofer et al., 2001; Seja et al., 2012).”
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).”
Properties of Somatosensory Synaptic Integration in Cerebellar Granule Cells In Vivo (2006, background): “…sparse coding in granule cells have heavily influenced current models about the function of the cerebellar granule layer (Marr, 1969; Albus, 1971; Tyrrell and Willshaw, 1992; Houk et al., 1996; Schweighofer et al., 1998, 2001; Wolpert et al., 1998; Chadderton et al., 2004; Semyanov et al., 2004).”
Optimal degrees of synaptic connectivity (2017, unknown): “As described above, the modifications of mossy-fiber to cerebellar granule-cell synaptic transmission that have been identified in vitro and in vivo may underlie an unsupervised learning process (Schweighofer et al., 2001).”
The cerebellar microcircuit as an adaptive filter: experimental and computational evidence (2010, background): “A further problem is that a simplified granular layer could not learn the efficient input codes that have been assumed to be necessary for individual sensorimotor problem”