title: Biologically plausible learning in neural networks with modulatory feedback.
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.
authors.affiliation: Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA. Electronic address: wgrant@usc.edu.
authors.forename: W Shane
authors.initials: WS
authors.lastname: Grant
authors.affiliation: Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA. Electronic address: jetanner@usc.edu.
authors.forename: James
authors.initials: J
authors.lastname: Tanner
authors.affiliation: Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA; Department of Psychology and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA. Electronic address: itti@pollux.usc.edu.
authors.forename: Laurent
authors.initials: L
authors.lastname: Itti
mesh_terms: D001921:Brain / Q000502:physiology; D005246:Feedback; D006801:Humans; D007858:Learning / Q000502:physiology; D000069550:Machine Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Border ownership; Computational modeling; Feedback; Modulatory; Plasticity; Self-organization