AIMC Topic: Cerebellum
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A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing.
Journal of the Royal Society, Interface
Jan 27, 2021
The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing ta...
Human-scale Brain Simulation via Supercomputer: A Case Study on the Cerebellum.
Neuroscience
Jan 20, 2021
Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models compose...
Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.
Scientific reports
Oct 29, 2020
Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and ...
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...
Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.
Analytical chemistry
Jun 25, 2020
The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, c...
Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.
NeuroImage
May 11, 2020
The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a w...
A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans.
Medical physics
Jan 6, 2020
PURPOSE: The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans.
Convergent Temperature Representations in Artificial and Biological Neural Networks.
Neuron
Jul 31, 2019
Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into...
Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.
Human brain mapping
May 30, 2019
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be...
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...
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