A neuronal model of classical conditioning

By A. Harry Klopf. 1988

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Citations in the corpus

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning (2015, methodology): “Watkins (1989) extended TD learning to control in the form of Q-learning and proved its convergence in the tabular case (without function approximation, Watkins & Dayan 1992), while Rummery (1995) extended TD learning to control in an on-policy form as the Sarsa(λ) algorithm.”
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type (1998, methodology): “Although the most commonly used ones are based on the statistical properties of presynaptic and postsynaptic activity (e.g., activity product, activity covariance, etc.) without considering the detailed temporal structure of the spike patterns, a number of formulations have indeed incorporated a fac”
Hebbian synapses: biophysical mechanisms and algorithms. (1990, background): “In particular the modifications are driven by what Klopf ( 1988) calls a "real time mechanism" (cf. Sutton & Barto 1 981, Donegan & Wagner 1 987), which he defines as follows (pp. 1 7-1 8): Real-time learning mechanisms emphasize the temporal association of signals: each critical event in the…”
Reinforcement Learning: An Introduction (1998, background): “There followed several other influential psychological models of classical conditioning based on temporal-di↵erence learning (e.g., Klopf, 1988; Moore et al.,”
Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context (1997, background): “Klopf’s [24,25] success at demonstrating over 15 classical conditioning phenomena in a single neuron using differential Hebbian learning (he called it “drive-reinforcement” learning), strongly suggests that such phenomena should be demonstrable in PolyWorld’s organisms.”
Time-Derivative Models of Pavlovian Reinforcement (1990, methodology): “ Klopf (1988) was the first to point out the relationships between the ˙”