Neuroscience 2007, the 37th annual meeting of the Society for Neuroscience  2007.11.3-7

Program#/Poster#: 396.22/MM1

Title: Learning IT-like topographically organized parts-based representation by topographic non-negative matrix factorization

Location: San Diego Convention Center: Halls B-H
Presentation Start/End Time: Monday, Nov 05, 2007, 9:00 AM -10:00 AM

Authors: *K. HOSODA1, M. WATANABE2, H. WERSING3, E. KOERNER3, H. TSUJINO4, H. TAMURA5, I. FUJITA5;
2Dept. of Quantum Engin. and Systems Sci., 1Univ. of Tokyo, Tokyo, Japan; 3HONDA Res. Inst. Europe GmbH, Offenbach, Germany; 4HONDA Res. Inst. Japan Co., Ltd, Saitama, Japan; 5Grad. Sch. of Frontier Biosci., Osaka Univ., Osaka, Japan

Visual processing for shape recognition is achieved by the ventral visual stream, where neurons show preference to visual features such as edges, curvatures, and T-junctions. The geometrical complexity of optimal stimulus features increases gradually with the cortical level. In the inferotemporal (IT) cortex, a majority of individual neurons respond optimally to fairly complex features. On a greater spatial scale, optical imaging studies have indicated two interesting features of IT population activity. First, a single object elicits multiple patches of activity across the cortex, which appears to represent component features of the object (Tsunoda et al. 2001). Second, patches of activity continuously shift their position when an object is systematically transformed (Wang et al. 1998).
In this study, we attempted to construct a theoretical model which explains the above mentioned features of IT neural representation, parts-based coding and topographic organization. In our model, we extended the non-negative matrix factorization (NMF) method developed by Lee & Seung, (1999), a basis decomposition method which represents input patterns by additive combinations of non-negative basis functions. Our extension was to incorporate a neighborhood function as follows. While the original NMF is formulated as V~=WH where V, W, and H are input, basis function, and coefficient matrices respectively, the extended model is formulated as V~=WMH where the matrix M represents neighborhood relations among basis functions, such as Gaussian function. Hereafter we refer to our model as the topographic non-negative matrix factorization (TNMF) model.
To investigate the properties of TNMF, we incorporated a hierarchical model which mimicked the early levels of the ventral pathway extracting visual features with increasing specificity and invariance based on alternating feature detection and integration processes (Wersing & K¨orner, 2003). After training the hierarchical model using real objects, the representation of the final layer was used as inputs to the TNMF model. Simulation results showed that the TNMF model attained the two goal properties, topographic organization and parts-based representation. We then compared responses of model neurons in various layers and monkey IT neurons to 64 semi-complex objects. Similarity of stimulus preference with actual IT neurons (pairwise activity correlation of 64 objects for the IT neuron and the best-fit model neuron) is higher for the output of the TNMF model than for the earlier layers of the model.

Support: MEXT grant of Japan 17022015
MEXT grant of Japan 1702205
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