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- W2272999 abstract "This chapter discusses the dynamics and VLSI implementation of self-organizing networks. Present artificial neural models encounter severe degradation in economy as larger, more complex perceptual and cognitive problems are approached. In particular, several fundamental scaling problems arise that need to be overcome before artificial neural technology can provide real-time solutions to the large, complex problems solved by biological systems. The high dimension of many data representations severely impact the cost of neural classifiers through three factors. The first factor, of primary importance for hardware implementation, is the scaling of VLSI implementation costs with increased node fan-in. The second scaling problem concerns learning time. For neural algorithms, the number of links and nodes, and thus the number of free parameters, grows with increasing input dimension. Third, recent advances in learning theory suggest that the required number of training examples may be expected to grow linearly with the number of network links." @default.
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- W2272999 title "Dynamics and VLSI Implementation of Self–Organizing Networks**This work was supported in part by the Semiconductor Research Corporation contract no. 86-10-097, and by the Office of Naval Research, ONR contract no. N00014 88 K 0329, and ONR contract no. N00014-90-J-1349." @default.
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- W2272999 doi "https://doi.org/10.1016/b978-0-444-88400-8.50026-1" @default.
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