Biologically plausible learning in neural networks with modulatory feedback

By W. Shane Grant, James Tanner, Laurent Itti. 2017

12 citations recorded by the source corpus.

Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics, Model Architectures, Neuroscience

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “Graves et al. (2017) proposed to treat the task selection problem as a stochastic policy over the tasks that maximizes the learning progress, leading to an improved efficiency in curriculum learning.”