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Alec Solway

Researcher at Virginia Tech

Publications -  17
Citations -  1495

Alec Solway is an academic researcher from Virginia Tech. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 10, co-authored 13 publications receiving 1223 citations. Previous affiliations of Alec Solway include Princeton University & University of Pennsylvania.

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Direct recordings of grid-like neuronal activity in human spatial navigation

TL;DR: Recording neuronal activity from neurosurgical patients performing a virtual-navigation task, it is identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.
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Neural Activity in Human Hippocampal Formation Reveals the Spatial Context of Retrieved Memories

TL;DR: Recording from neurosurgical patients playing a virtual reality memory game found that the recall of events was indeed associated with reinstatement of the place-firing of neurons activated as the subjects navigated through the environment and place-responsive cell activity was reinstated during episodic memory retrieval.
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A Neural Signature of Hierarchical Reinforcement Learning

TL;DR: It is proposed that the computations supporting hierarchical behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical domains, and leveraged a distinctive prediction arising from HRL.
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Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates

TL;DR: The basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model.
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Optimal behavioral hierarchy.

TL;DR: This work provides a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks, and presents results from four behavioral experiments suggesting that human learners spontaneously discover optimal action hierarchies.