Computational investigation of visually guided learning of spatially aligned auditory maps in the colliculus

  • Timo Oess Applied Cognitive Psychology, Ulm University, D-89081 Ulm, Germany
  • Marc O. Ernst Applied Cognitive Psychology, Ulm University, D-89081 Ulm, Germany
  • Heiko Neumann Institute of Neural Information Processing, Ulm University, D-89081 Ulm, Germany
Keywords: Auditory Space Map, Learning, Hebb, Map Alignment, Barn Owl, Modelling, Inferior Colliculus

Abstract

The development of spatially registered auditory maps in the external nucleus of the inferior colliculus in young owls and their maintenance in adult animals is visually guided and evolves dynamically. To investigate the underlying neural mechanisms of this process, we developed a model of stabilized neoHebbian correlative learning which is augmented by an eligibility signal and a temporal trace of activations. This 3-component learning algorithm facilitates stable, yet flexible, formation of spatially registered auditory space maps composed of conductance-based topographically organized neu- ral units. Spatially aligned maps are learned for visual and auditory input stimuli that arrive in temporal and spatial registration. The reliability of visual sensory inputs can be used to regulate the learning rate in the form of an eligibility trace. We show that by shifting visual sensory inputs at the onset of learning the topography of auditory space maps is shifted accordingly. Simulation results explain why a shift of auditory maps in mature animals is possible only if corrections are induced in small steps. We conclude that learning spatially aligned auditory maps is flexibly controlled by reliable visual sensory neurons and can be formalized by a biological plausible unsupervised learning mechanism.

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Published
2020-04-22
How to Cite
Oess, T., Ernst, M., & Neumann, H. (2020). Computational investigation of visually guided learning of spatially aligned auditory maps in the colliculus. Proceedings of the International Symposium on Auditory and Audiological Research, 7, 149-156. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2019-18
Section
2019/3. Machine listening and intelligent auditory signal processing