AIMC Topic: Reward

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Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

Biological psychiatry. Cognitive neuroscience and neuroimaging Oct 22, 2019
BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven ap...
Machine Learning Female Young Adult Adult Humans Reward Dopamine Antidepressive Agents, Second-Generation Amisulpride Male Depressive Disorder, Major Depression Double-Blind Method
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Putting a bug in ML: The moth olfactory network learns to read MNIST.

Neural networks : the official journal of the International Neural Network Society Jun 4, 2019
We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The moth olfactory network is among the simples...
Nerve Net Machine Learning Moths Brain Computer Simulation Animals Support Vector Machine Reward Smell Neural Networks, Computer
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A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

Neural computation May 21, 2019
Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised l...
Humans Neural Networks, Computer Computer Simulation Reward Nerve Net Reinforcement, Psychology Neuronal Plasticity Models, Neurological Neurons
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Mental labour.

Nature human behaviour Sep 3, 2018
Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of scientific study, mental effort has remained rather elusive. Cognitive psychology has provided some too...
Reward Humans Economics, Behavioral Cognition Work Decision Making
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Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience Jul 13, 2018
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...
Avoidance Learning Association Learning Signal Detection, Psychological Conditioning, Operant Environment Reinforcement, Psychology Conditioning, Classical Neuropil Afferent Pathways Mushroom Bodies Olfactory Receptor Neurons Reward Odorants Synapses Signal-To-Noise Ratio Drosophila melanogaster Olfactory Perception Nerve Net Animals Deep Learning Models, Neurological
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Abdominal-Waving Control of Tethered Bumblebees Based on Sarsa With Transformed Reward.

IEEE transactions on cybernetics Jun 22, 2018
Cyborg insects have attracted great attention as the flight performance they have is incomparable by micro aerial vehicles and play a critical role in supporting extensive applications. Approaches to construct cyborg insects consist of two major issu...
Cybernetics Reward Markov Chains Bees Machine Learning Algorithms Animals
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A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

eNeuro Apr 24, 2018
Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is...
Neuronal Plasticity Reward Synapses Connectome Nerve Net Neural Networks, Computer Computer Simulation Humans Animals Models, Theoretical
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Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

Computational intelligence and neuroscience Aug 14, 2017
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically captur...
Translations Machine Learning Reinforcement, Psychology Feedback Reward
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Global and local excitation and inhibition shape the dynamics of the cortico-striatal-thalamo-cortical pathway.

Scientific reports Aug 8, 2017
The cortico-striatal-thalamo-cortical (CSTC) pathway is a brain circuit that controls movement execution, habit formation and reward. Hyperactivity in the CSTC pathway is involved in obsessive compulsive disorder (OCD), a neuropsychiatric disorder ch...
gamma-Aminobutyric Acid Thalamus Synaptic Transmission Humans GABAergic Neurons Cortical Excitability Neural Networks, Computer Neural Inhibition Receptors, Dopamine Glutamic Acid Rodentia Cerebral Cortex Species Specificity Connectome Corpus Striatum Animals Reward Synapses
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Decision making under uncertainty in a spiking neural network model of the basal ganglia.

Journal of integrative neuroscience Dec 21, 2016
The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbi...
Basal Ganglia Extinction, Psychological Reversal Learning Macaca Cerebral Cortex Motor Activity Uncertainty Animals Decision Making Neuronal Plasticity Neural Networks, Computer Neurons Models, Neurological Reward Synapses
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