AIMC Topic: Purkinje Cells

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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

PLoS computational biology Mar 17, 2025
Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, wit...
Purkinje Cells Cerebellum Reward Reinforcement, Psychology Animals Male Mice Mice, Inbred C57BL Supervised Machine Learning Computational Biology Algorithms Models, Neurological
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A deep learning strategy to identify cell types across species from high-density extracellular recordings.

Cell Feb 28, 2025
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals an...
Deep Learning Mice Rats Mice, Inbred C57BL Animals Male Optogenetics Neurons Purkinje Cells Cerebellum Interneurons
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TAG-SPARK: Empowering High-Speed Volumetric Imaging With Deep Learning and Spatial Redundancy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 16, 2024
Two-photon high-speed fluorescence calcium imaging stands as a mainstream technique in neuroscience for capturing neural activities with high spatiotemporal resolution. However, challenges arise from the inherent tradeoff between acquisition speed an...
Mice Calcium Signal-To-Noise Ratio Animals Deep Learning Imaging, Three-Dimensional Purkinje Cells Algorithms
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Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

PLoS computational biology Feb 10, 2021
The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented thro...
Normal Distribution Synaptic Transmission Interneurons Cerebellar Cortex Purkinje Cells Excitatory Postsynaptic Potentials Cerebellum Synapses Neural Pathways Models, Neurological Action Potentials Neurons Humans Neural Networks, Computer Signal Transduction Neuronal Plasticity Computer Simulation Animals
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Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology Oct 28, 2020
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...
Machine Learning Learning Humans Cerebellar Nuclei Cerebellum Conditioning, Classical Motor Activity Animals Purkinje Cells Eye Movements Action Potentials
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Using deep neural networks to detect complex spikes of cerebellar Purkinje cells.

Journal of neurophysiology May 6, 2020
One of the most powerful excitatory synapses in the brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivar...
Action Potentials Macaca mulatta Electrophysiological Phenomena Purkinje Cells Animals Male Deep Learning
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DeTerm: Software for automatic detection of neuronal dendritic branch terminals via an artificial neural network.

Genes to cells : devoted to molecular & cellular mechanisms May 28, 2019
Dendrites of neurons receive and process synaptic or sensory inputs. The Drosophila class IV dendritic arborization (da) neuron is an established model system to explore molecular mechanisms of dendrite morphogenesis. The total number of dendritic br...
Neural Networks, Computer Software Animals Neurogenesis Larva Purkinje Cells Drosophila Mice Neurons Dendrites Cerebellum Drosophila Proteins
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A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

IEEE transactions on neural networks and learning systems Aug 26, 2014
Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simu...
Nerve Fibers Computer Graphics Neural Networks, Computer Reproducibility of Results Microcomputers Computers, Hybrid Algorithms Benchmarking Purkinje Cells Cerebellum Computer Simulation
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Implementing feature binding through dendritic networks of a single neuron.

Neural networks : the official journal of the International Neural Network Society Sep 1, 2025
A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and comput...
Neural Networks, Computer Computer Simulation Synapses Models, Neurological Animals Nerve Net Dendrites Action Potentials Purkinje Cells Neurons
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Practical method of cell segmentation in electron microscope image stack using deep convolutional neural network☆.

Microscopy (Oxford, England) Aug 6, 2019
Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a ...
Image Processing, Computer-Assisted Microscopy, Electron Purkinje Cells Neural Networks, Computer Machine Learning Algorithms
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