title: Adaptive learning in a compartmental model of visual cortex-how feedback enables stable category learning and refinement.
abstract: The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations.
authors.affiliation: Institute of Neural Information Processing, Ulm University Ulm, Germany.
authors.forename: Georg
authors.initials: G
authors.lastname: Layher
authors.affiliation: Department of Computer Science, University of Tübingen Tübingen, Germany.
authors.forename: Fabian
authors.initials: F
authors.lastname: Schrodt
authors.affiliation: Department of Computer Science, University of Tübingen Tübingen, Germany.
authors.forename: Martin V
authors.initials: MV
authors.lastname: Butz
authors.affiliation: Institute of Neural Information Processing, Ulm University Ulm, Germany.
authors.forename: Heiko
authors.initials: H
authors.lastname: Neumann
keywords: category learning; feedforward and feedback processing; neural model; subcategory learning; unsupervised learning