Patrick C. Connor

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Biological systems are capable of learning that certain stimuli are valuable while ignoring the many that are not, and thus perform feature selection. In machine learning, one effective feature selection approach is the least absolute shrinkage and selection operator (LASSO) form of regularization, which is equivalent to assuming a Laplacian prior(More)
When retrospective revaluation phenomena (e.g., unovershadowing: AB+, then A-, then test B) were discovered, simple elemental models were at a disadvantage because they could not explain such phenomena. Extensions of these models and novel models appealed to within-compound associations to accommodate these new data. Here, we present an elemental, neural(More)
Reinforcement learning treats each input, feature, or stimulus as having a positive or negative reward value. Some stimuli, however, negate or inhibit the values of certain other predictors (excitors) when presented with them, but are otherwise neutral. We show that both linear and non-linear value-function approximators assign inhibitory features a strong(More)
The field of Reinforcement Learning (RL) in machine learning relates significantly to the domains of classical and instrumental conditioning in psychology, which give an understanding of biology's approach to RL. In recent years, there has been a thrust to correlate some machine learning RL algorithms with brain structure and function , a benefit to both(More)
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