AIMC Topic:
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What Google's winning Go algorithm will do next.

Nature Mar 17, 2016
Learning Male Child Humans Artificial Intelligence Language Smartphone Diagnosis Games, Recreational
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Spiking neurons can discover predictive features by aggregate-label learning.

Science (New York, N.Y.) Mar 4, 2016
The brain routinely discovers sensory clues that predict opportunities or dangers. However, it is unclear how neural learning processes can bridge the typically long delays between sensory clues and behavioral outcomes. Here, I introduce a learning c...
Membrane Potentials Concept Formation Reinforcement, Psychology Formative Feedback Learning Speech Models, Neurological Unsupervised Machine Learning Supervised Machine Learning Neurons Humans Neural Networks, Computer Brain
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Using goal-driven deep learning models to understand sensory cortex.

Nature neuroscience Mar 1, 2016
Fueled by innovation in the computer vision and artificial intelligence communities, recent developments in computational neuroscience have used goal-driven hierarchical convolutional neural networks (HCNNs) to make strides in modeling neural single-...
Humans Neural Networks, Computer Goals Learning Animals Visual Cortex Models, Neurological Somatosensory Cortex
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IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond.

IEEE transactions on neural networks and learning systems Jan 1, 2016
Learning Periodicals as Topic Information Storage and Retrieval Humans Neural Networks, Computer
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Fully parallel write/read in resistive synaptic array for accelerating on-chip learning.

Nanotechnology Nov 13, 2015
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with ...
Computing Methodologies Models, Theoretical Unsupervised Machine Learning Semiconductors Synapses Neural Networks, Computer Electric Impedance Learning Pattern Recognition, Automated
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A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex.

The Journal of neuroscience : the official journal of the Society for Neuroscience Sep 16, 2015
UNLABELLED: Many actions performed by animals and humans depend on an ability to learn, estimate, and produce temporal intervals of behavioral relevance. Exemplifying such learning of cued expectancies is the observation of reward-timing activity in ...
Membrane Potentials Reinforcement, Psychology Visual Cortex Neuronal Plasticity Computer Simulation Models, Neurological Learning Neurons Reward Animals Neural Networks, Computer Nerve Net Synaptic Transmission
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[Dancing with artificial intelligence].

Revue medicale suisse Jun 24, 2015
Humans Mental Processes Artificial Intelligence Social Adjustment Algorithms Social Change Learning
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Technique evolution, learning curve, and outcomes of 200 robot-assisted gastric bypass procedures: a 5-year experience.

Obesity surgery Jun 1, 2015
BACKGROUND: We evaluate our 5-year experience, evolution of technique, and clinical outcomes with robot-assisted RYGB.
Middle Aged Treatment Outcome Operative Time Robotics Reoperation Laparoscopy Female Humans Learning Curve Gastric Bypass Adult Retrospective Studies Learning Male
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Consensus-based distributed cooperative learning from closed-loop neural control systems.

IEEE transactions on neural networks and learning systems Feb 1, 2015
In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural net...
Consensus Algorithms Neural Networks, Computer Nonlinear Dynamics Learning Artificial Intelligence Uncertainty Communication
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Learning feature representations with a cost-relevant sparse autoencoder.

International journal of neural systems Feb 1, 2015
There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpo...
Concept Formation Visual Perception Learning Neurons Attention Humans Artificial Intelligence Algorithms
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