AIMC Topic: Reward

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Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Neural networks : the official journal of the International Neural Network Society May 11, 2020
Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. How...
Machine Learning Humans Brain Models, Neurological Reward
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Real-time sensory-motor integration of hippocampal place cell replay and prefrontal sequence learning in simulated and physical rat robots for novel path optimization.

Biological cybernetics Feb 24, 2020
An open problem in the cognitive dimensions of navigation concerns how previous exploratory experience is reorganized in order to allow the creation of novel efficient navigation trajectories. This behavior is revealed in the "traveling salesrat prob...
Algorithms Computer Simulation Hippocampus Spatial Navigation Place Cells Models, Neurological Ventral Striatum Robotics Reward Learning Animals Rats
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Modeling uncertainty-seeking behavior mediated by cholinergic influence on dopamine.

Neural networks : the official journal of the International Neural Network Society Feb 6, 2020
Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory system known for its major role in reinforcement learning and decision-making. In this paper, we pr...
Neural Networks, Computer Acetylcholine Animals Dopamine Uncertainty Humans Models, Neurological Decision Making Reward Dopaminergic Neurons Cholinergic Neurons
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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...
Male Depressive Disorder, Major Depression Female Young Adult Adult Antidepressive Agents, Second-Generation Amisulpride Double-Blind Method Reward Dopamine Humans Machine Learning
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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...
Support Vector Machine Reward Smell Nerve Net Moths Brain Computer Simulation Animals Neural Networks, Computer Machine Learning
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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...
Neural Networks, Computer Reinforcement, Psychology Computer Simulation Neuronal Plasticity Humans Reward Nerve Net 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...
Nerve Net Environment Reinforcement, Psychology Animals Association Learning Signal Detection, Psychological Deep Learning Conditioning, Classical Models, Neurological Olfactory Receptor Neurons Avoidance Learning Neuropil Signal-To-Noise Ratio Reward Conditioning, Operant Drosophila melanogaster Odorants Afferent Pathways Olfactory Perception Synapses Mushroom Bodies
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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...
Machine Learning Algorithms Cybernetics Animals Reward Markov Chains Bees
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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...
Synapses Connectome Nerve Net Humans Neural Networks, Computer Neuronal Plasticity Animals Computer Simulation Reward Models, Theoretical
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