Computer simulation of cerebellar information processing

By Javier F. Medina, M. Mauk. Nature Neuroscience, 2000

347 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/a336272643427742d91d835d3e90a3afa82cd566

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Citations in the corpus

Synaptic and cellular properties of the feed-forward inhibitory circuit within the input layer of the cerebellar cortex (2008, background): “Spike timing is also important in motor systems because encoding temporal sequences is essential for coordinating movements (Ivry and Keele, 1989; Medina and Mauk, 2000).”
Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition (2015, background): “This classical excitation–inhibition sequence forms the basis of a variety of contemporary cerebellar models (7, 9, 18, 19).”
Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice (2015, neutral): “This anatomical arrangement of excitatory and inhibitory synaptic inputs in the IO is the foundation for current models of cerebellar learning based on the “comparator” hypothesis 15,18,19 .”
Timing rules for synaptic plasticity matched to behavioral function (2016, neutral): “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).”
Purkinje cells directly inhibit granule cells in specialized regions of the cerebellar cortex (2016, neutral): “The canonical cerebellar circuit can sustain activity in the presence of constant excitation (Medina and Mauk, 2000), yet many vestibular functions require activity that persists for seconds to tens of seconds beyond a transient input (Robinson, 1974), which is challenging for a feedforward circuit.”
ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, background): “…in high-dimensional space, and these patterns or trajectories can in turn be used for pattern classification or pattern generation through linear readout units (Buonomano and Maass, 2009; Buonomano and Merzenich, 1995; Jaeger, 2001; Jaeger et al., 2007; Maass et al., 2002; Medina and Mauk, 2000).”
At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters (2015, background): “However, it was not usually made clear how these functions could be generated by a plausible neuronal network, raising concerns that the approach was unrealistic [11] and excessively ‘top-down’ [14].”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, background): “…thereby 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,…”
Timing in the absence of clocks: encoding time in neural network states. (2007, background): “These include climbing firing rate models (Durstewitz, 2003; Reutimann et al., 2004), multiple oscillator models (Miall, 1989; Matell and Meck, 2004), and those based on ongoing network dynamics (Medina and Mauk, 2000; Buonomano, 2005).”