Unsupervised learning of object identities and their parts in a hierarchical visual memory

Field: Neuroscience (Life Sciences)

Event Abstract Back to Event Unsupervised learning of object identities and their parts in a hierarchical visual memory Jenia Jitsev1, 2* and Christoph Von Der Malsburg1 1 Frankfurt Institute for Advanced Studies, Germany 2 Goethe University, Germany Visual cortex is thought to utilize parts-based representation to encode natural visual objects, decomposing them into constituent components along hierarchically organized visual pathway. Substantial psychophysical and neurophysiological evidence suggests that visual system may use two different coding strategies to signal the relations between the components. First, the relations can be explicitly conveyed by conjunction, or configuration, sensitive neurons from higher visual areas. Second, the relations can be signaled by dynamic assemblies of co-activated part-specific units, which can be constructed on demand to encode a novel object or to recall an already familiar one from the memory as a composition of its constituent parts. We target the question what neural mechanisms are required to guide the self-organization of a memory structure that supports the two different coding strategies. The model we propose is based on two consecutive, reciprocally interconnected layers of distributed cortical modules, or columns, which in turn contain subunits receiving common excitatory afferents and bounded by common lateral inhibition, which is modulated by excitatory and inhibitory rhythms in the gamma range. The lateral inhibition within the column results in activity dynamics with a strong competitive character, casting the column a winner-take-all-like decision unit [1]. On the slow time scale, the structure formation is guided by activity-dependent bidirectional plasticity and homeostatic regulation of unit's activity.In the initial state, the connectivity between and within the layers is homogeneous, all types of synapses - bottom-up, lateral and top-down - being excitatory and plastic. A data set containing natural human face images is used to provide visual input to the network. During incremental, open-end unsupervised learning, the lower layer of the system is exposed to the Gabor filter banks extracted from local points on the face images [2]. The system is able to develop synaptic structure capturing local features and their relations on the lower level as well as the global identity of the person at the higher level of processing, improving gradually its recognition performance with learning time. The symbols for person identities emerging on the higher memory layer are grounded on the semantics of parts-based representations emerging on the lower layer. Activation of such an identity symbol leads to reactivation of the constituent distributed parts via established top-down connections, providing explicit representation of symbol's low level configuration. The memory system shows impressive recognition performance on the original and alternative face views, underpinning its functionality. Experience-driven, unsupervised structure formation instantiated here is thus able to form a basic memory domain with hierarchical organization and contextual generative support, opening a promising direction for further research. Acknowledgments:This work was supported by the EU project DAISY, FP6-2005-015803. References 1. LÃ¼cke, J., 2005. Dynamics of cortical columns - sensitive decision making. In: Proc. ICANN. LNCS 3696. Springer, pp. 25-30.2. L. Wiskott, J.-M. Fellous, N. Krueger, C. von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Trans. on Pattern Analysis and Machine Intelligence 19 (7) (1997) 775-779. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Abstracts Citation: Jitsev J and Von Der Malsburg C (2009). Unsupervised learning of object identities and their parts in a hierarchical visual memory. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.125 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Aug 2009; Published Online: 27 Aug 2009. * Correspondence: Jenia Jitsev, Frankfurt Institute for Advanced Studies, Frankfurt, Germany, jitsev@fias.uni-frankfurt.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jenia Jitsev Christoph Von Der Malsburg Google Jenia Jitsev Christoph Von Der Malsburg Google Scholar Jenia Jitsev Christoph Von Der Malsburg PubMed Jenia Jitsev Christoph Von Der Malsburg Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.