Biologically plausible learning in neural networks with modulatory feedback
Title | Biologically plausible learning in neural networks with modulatory feedback |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | W. S. Grant, J. Tanner, L. Itti |
Journal | Neural Networks |
Volume | 88 |
Pagination | 32-48 |
Date Published | Jan |
Type of Article | mod |
Keywords | 1544814 |
Abstract | 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. |
Citation Key | Grant_etal17nn |