ArticlePLoS computational biology2012
Storage of correlated patterns in standard and bistable Purkinje cell models.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Locally balanced inhibition allows for robust learning of input-output associations in feedforward networks with Hebbian plasticity.Journal of computational neuroscience · 2026Article
- Parallel synapses with transmission nonlinearities enhance neuronal classification capacity.PLoS computational biology · 2025Article
- Purkinje cell models: past, present and future.Frontiers in computational neuroscience · 2024Review
- Article
- Discrimination and learning of temporal input sequences in a cerebellar Purkinje cell model.Frontiers in cellular neuroscience · 2023Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Free recall scaling laws and short-term memory effects in a latching attractor network.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- Article
- Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Stabilizing patterns in time: Neural network approach.PLoS computational biology · 2017Article
- Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Toward a Neurocentric View of Learning.Neuron · 2017Review
- Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise.Scientific reports · 2017Article
- Long Pauses in Cerebellar Interneurons in Anesthetized Animals.Cerebellum (London, England) · 2017Article
- Article
- Purkinje cell intrinsic excitability increases after synaptic long term depression.Journal of neurophysiology · 2016Article
- Inverse Stochastic Resonance in Cerebellar Purkinje Cells.PLoS computational biology · 2016Article
- Is cortical connectivity optimized for storing information?Nature neuroscience · 2016Article
- A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.PLoS computational biology · 2015Article
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3 authors.
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Abstract
The cerebellum has long been considered to undergo supervised learning, with climbing fibers acting as a 'teaching' or 'error' signal. Purkinje cells (PCs), the sole output of the cerebellar cortex, have been considered as analogs of perceptrons storing input/output associations. In support of this hypothesis, a recent study found that the distribution of synaptic weights of a perceptron at maximal capacity is in striking agreement with experimental data in adult rats. However, the calculation was performed using random uncorrelated inputs and outputs. This is a clearly unrealistic assumption since sensory inputs and motor outputs carry a substantial degree of temporal correlations. In this paper, we consider a binary output neuron with a large number of inputs, which is required to store associations between temporally correlated sequences of binary inputs and outputs, modelled as Markov chains. Storage capacity is found to increase with both input and output correlations, and diverges in the limit where both go to unity. We also investigate the capacity of a bistable output unit, since PCs have been shown to be bistable in some experimental conditions. Bistability is shown to enhance storage capacity whenever the output correlation is stronger than the input correlation. Distribution of synaptic weights at maximal capacity is shown to be independent on correlations, and is also unaffected by the presence of bistability.
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