A drive-reinforcement model of single neuron function: An alternative to the Hebbian neuronal model
AIP Conference Proceedings 151 on Neural Networks for Computing
AIP Conference Proceedings 151 on Neural Networks for Computing
Technical Note: \cal Q-Learning
Machine Learning
Neural Networks - Special issue on neural control and robotics: biology and technology
Feedback control systems (4th ed.)
Feedback control systems (4th ed.)
Dopamine-dependent plasticity of corticostriatal synapses
Neural Networks - Computational models of neuromodulation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Isotropic sequence order learning
Neural Computation
Temporal Difference Model Reproduces Anticipatory Neural Activity
Neural Computation
Second Order Conditioning in the Sub-cortical Nuclei of the Limbic System
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
A spiking neural network model of an actor-critic learning agent
Neural Computation
Biomimetic approach to tacit learning based on compound control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning and Reversal Learning in the Subcortical Limbic System: A Computational Model
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Solving the distal reward problem with rare correlations
Neural Computation
Novel method for using Q-learning in small microcontrollers
Proceedings of the 51st ACM Southeast Conference
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It is a well-known fact that Hebbian learning is inherently unstable because of its self-amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and therefore the faster the synaptic growth. This unwanted weight growth is driven by the autocorrelation term of Hebbian learning where the same synapse drives its own growth. On the other hand, the cross-correlation term performs actual learning where different inputs are correlated with each other. Consequently, we would like to minimize the autocorrelation and maximize the cross-correlation. Here we show that we can achieve this with a third factor that switches on learning when the autocorrelation is minimal or zero and the cross-correlation is maximal. The biological counterpart of such a third factor is a neuromodulator that switches on learning at a certain moment in time. We show in a behavioral experiment that our three-factor learning clearly outperforms classical Hebbian learning.