Isotropic sequence order learning
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
Stabilising hebbian learning with a third factor in a food retrieval task
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Isotropic sequence order learning (ISO-learning) and its variations, input correlation only learning (ICO-learning) and ISO three-factor learning (ISO3-learning) are unsupervised neural algorithms to learn temporal differences. As robotic software operates mainly in discrete time domain, a discretization of ISO-learning is needed to apply classical conditioning to reactive robot controllers. Discretization of ISO-learning is achieved by modifications to original rules: weights sign restriction, to adequate ISO-learning devices outputs to the usually predefined kinds of connections (excitatory/inhibitory) used in neural networks, and decay term in learning rate for weights stabilization. Discrete ISO-learning devices are included into neural networks used to learn simple obstacle avoidance in the reactive control of two real robots.