AIMC Topic: Cerebellum
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New insights into olivo-cerebellar circuits for learning from a small training sample.
Current opinion in neurobiology
Aug 19, 2017
Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is ma...
Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design.
Psychiatry research. Neuroimaging
Apr 24, 2017
Abnormal short-range and long-range functional connectivities (FCs) have been implicated in the neurophysiology of schizophrenia. This study was conducted to examine the potential of short-range and long-range FCs for differentiating the patients fro...
Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy.
Journal of neurotrauma
Nov 21, 2016
Traumatic brain injury (TBI) may adversely affect a person's thinking, memory, personality, and behavior. While mild TBI (mTBI) diagnosis is challenging, there is a risk for long-term psychiatric, neurologic, and psychosocial problems in some patient...
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
PloS one
May 23, 2016
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate w...
A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification.
Computational intelligence and neuroscience
May 19, 2016
The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis....
Machine Learning Capabilities of a Simulated Cerebellum.
IEEE transactions on neural networks and learning systems
Jan 26, 2016
This paper describes the learning and control capabilities of a biologically constrained bottom-up model of the mammalian cerebellum. Results are presented from six tasks: 1) eyelid conditioning; 2) pendulum balancing; 3) proportional-integral-deriva...
Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System.
IEEE transactions on biomedical circuits and systems
Oct 6, 2015
The cerebellum plays a critical role for sensorimotor control and learning. However, dysmetria or delays in movements' onsets consequent to damages in cerebellum cannot be cured completely at the moment. Neuroprosthesis is an emerging technology that...
Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms.
IEEE transactions on bio-medical engineering
Oct 1, 2015
GOAL: In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction.
Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional imaging.
Human brain mapping
Aug 19, 2015
Several neurobiological factors have been found to correlate with functional recovery after brain lesions. However, predicting the individual potential of recovery remains difficult. Here we used multivariate support vector machine (SVM) classificati...
A realistic bi-hemispheric model of the cerebellum uncovers the purpose of the abundant granule cells during motor control.
Frontiers in neural circuits
May 1, 2015
The cerebellar granule cells (GCs) have been proposed to perform lossless, adaptive spatio-temporal coding of incoming sensory/motor information required by downstream cerebellar circuits to support motor learning, motor coordination, and cognition. ...
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