ArticleProceedings of the National Academy of Sciences of the United States of America2009
The advantages of linear information processing for cerebellar computation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Intrinsic brain network dynamics modulated by neural stimulation to cerebellum.Network neuroscience (Cambridge, Mass.) · 2026Article
- Beyond synaptic plasticity: a summary of a linear model of the cerebellar locomotor computation.Frontiers in neural circuits · 2026Review
- The cerebellum converts input data into a hyper low-resolution granule cell code with spatial dimensions: a hypothesis.Royal Society open science · 2025Article
- Computational anatomy: the cerebellar microzone computation.Oxford open neuroscience · 2025Article
- Cerebellum and Aging: Update and Challenges.Aging and disease · 2024Review
- Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs.eLife · 2024Article
- Purkinje cell models: past, present and future.Frontiers in computational neuroscience · 2024Review
- Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs.bioRxiv : the preprint server for biology · 2023Article
- The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex.Cerebellum (London, England) · 2022Article
- Latency correction in sparse neuronal spike trains.Journal of neuroscience methods · 2022Article
- The Cerebellar Cortex.Annual review of neuroscience · 2022Review
- Bidirectional learning in upbound and downbound microzones of the cerebellum.Nature reviews. Neuroscience · 2021Review
- A Slow Short-Term Depression at Purkinje to Deep Cerebellar Nuclear Neuron Synapses Supports Gain-Control and Linear Encoding over Second-Long Time Windows.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Article
- Purkinje Cell Representations of Behavior: Diary of a Busy Neuron.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2019Review
- Article
- Computational Principles of Supervised Learning in the Cerebellum.Annual review of neuroscience · 2018Review
- Climbing fibers predict movement kinematics and performance errors.Journal of neurophysiology · 2017Article
- Robust transmission of rate coding in the inhibitory Purkinje cell to cerebellar nuclei pathway in awake mice.PLoS computational biology · 2017Article
- Movement Rate Is Encoded and Influenced by Widespread, Coherent Activity of Cerebellar Molecular Layer Interneurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
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2 authors.
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Abstract
Purkinje cells can encode the strength of parallel fiber inputs in their firing by using 2 fundamentally different mechanisms, either as pauses or as linear increases in firing rate. It is not clear which of these 2 encoding mechanisms is used by the cerebellum. We used the pattern-recognition capacity of Purkinje cells based on the Marr-Albus-Ito theory of cerebellar learning to evaluate the suitability of the linear algorithm for cerebellar information processing. Here, we demonstrate the simplicity and versatility of pattern recognition in Purkinje cells linearly encoding the strength of parallel fiber inputs in their firing rate. In contrast to encoding patterns with pauses, Purkinje cells using the linear algorithm could recognize a large number of both synchronous and asynchronous input patterns in the presence or absence of inhibitory synaptic transmission. Under all conditions, the number of patterns recognized by Purkinje cells using the linear algorithm was greater than that achieved by encoding information in pauses. Linear encoding of information also allows neurons of deep cerebellar nuclei to use a simple averaging mechanism to significantly increase the computational capacity of the cerebellum. We propose that the virtues of the linear encoding mechanism make it well suited for cerebellar computation.
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