Abstract
A major research problem in the area of unsupervised learning is the understanding of neuronal selectivity, and its role in the formation of cortical maps. Kohonen devised a self-organizing map algorithm to investigate this problem, which achieved partial success in replicating biological observations. However, a problem in using Kohonen’s approach is that it does not address the stability-plasticity dilemma, as the learning rate decreases monotonically.
In this paper, we propose a solution to cortical map formation which tackles the stability-plasticity problem, where the map maintains stability while enabling plasticity in the presence of changing input statistics. We adapt the parameterless SOM (Berglund and Sitte 2006) and also modify Kohonen’s original approach to allow local competition in a larger cortex, where multiple winners can exist.
The learning rate and neighborhood size of the modified Kohonen’s method are set automatically based on the error between the local winner’s weight vector and its input. We used input images consisting of lines of random orientation to train the system in an unsupervised manner. Our model shows large scale topographic organization of orientation across the cortex, which compares favorably with cortical maps measured in visual area V1 in primates. Furthermore, we demonstrate the plasticity of this map by showing that the map reorganizes when the input statistics are chanaged.
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References
Carpenter, G.A., Grossberg, S.: The ART of Adaptive Pattern Recognition by a Self-organizing Neural Network. Computer 21(3), 77–88 (1988)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge (2001)
Miikkulainen, R., Bednar, J.A., Choe, Y., Sirosh, J.: Computational Maps in the Visual Cortex. Springer, Berlin (2005)
Obermayer, K., Blasdel, G.: Geometry of Orientation and Ocular Dominance Columns in Monkey Striate Cortex. J. Neuroscience 13, 4114–4129 (1993)
Carreira-Perpinan, M.A., Lister, R.J., Goodhill, G.J.: A Computational Model for the Development of Multiple Maps in Primary Visual Cortex. Cerebral Cortex 15, 1222–1233 (2005)
Kohonen, T.: The Self-organizing Map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Bednar, J.A.: Learning to See: Genetic and Environmental Influences on Visual Development. PhD thesis, Department of Computer Sciences, The University of Texas at Austin, Technical Report AI-TR-02-294 (2002)
Berglund, E., Sitte, J.: The Parameterless Self-organizing Map Algorithm. IEEE Trans. Neural Networks 17(3), 305–316 (2006)
Erwin, E., Obermayer, K., Schulten, K.: Models of Orientation and Ocular Dominance Columns in the Visual Cortex: A Critical Comparison. Neural Computation 7(3), 425–468 (1995)
Hubel, D.H., Wiesel, T.N., Levay, S.: Plasticity of Ocular Dominance Columns in Monkey Striate Cortex. Phil. Trans. R. Soc. Lond. B 278, 377–409 (1977)
Buonomano, D.V., Merzenich, M.M.: Cortical Plasticity: From Synapses to Maps. Annual Review of Neuroscience 21, 149–186 (1998)
Hyvärinen, A., Hoyer, P.O., Hurri, J.: Extensions of ICA as Models of Natural Images and Visual Processing. In: ICA 2003, Nara, Japan, pp. 963–974 (2003)
Sharma, J., Angelucci, A., Sur, M.: Induction of Visual Orientation Modules in Auditory Cortex. Nature 404, 841–847 (2000)
Wallis, G.: Using Spatio-temporal Correlations to Learn Invariant Object Recognition. Neural Networks, 1513–1519 (1996)
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Rao, A.R., Cecchi, G., Peck, C., Kozloski, J. (2007). Emergence of Topographic Cortical Maps in a Parameterless Local Competition Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_66
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DOI: https://doi.org/10.1007/978-3-540-72393-6_66
Publisher Name: Springer, Berlin, Heidelberg
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