By Stephen Grossberg (auth.), Georg Dorffner, Horst Bischof, Kurt Hornik (eds.)
This booklet is predicated at the papers provided on the overseas convention on Arti?cial Neural Networks, ICANN 2001, from August 21–25, 2001 on the - enna collage of know-how, Austria. The convention is equipped via the A- trian examine Institute for Arti?cal Intelligence in cooperation with the development acceptance and snapshot Processing crew and the heart for Computational - telligence on the Vienna collage of expertise. The ICANN meetings have been initiated in 1991 and became the most important ecu assembly within the ?eld of neural networks. From approximately three hundred submitted papers, this system committee chosen 171 for book. every one paper has been reviewed by means of 3 application committee m- bers/reviewers. we wish to thank all of the contributors of this system comm- tee and the reviewers for his or her nice e?ort within the reviewing approach and supporting us to establish a scienti?c software of top quality. furthermore, we have now invited 8 audio system; 3 in their papers also are integrated within the court cases. we wish to thank the ecu Neural community Society (ENNS) for his or her aid. We recognize the ?nancial help of Austrian airways, A- trian technological know-how starting place (FWF) lower than the agreement SFB 010, Austrian Society ¨ for Arti?cial Intelligence (OGAI), financial institution Austria, and the Vienna conference Bureau. we wish to specific our honest due to A. Flexer, W. Horn, okay. Hraby, F. Leisch, C. Schittenkopf, and A. Weingessel. The convention and the court cases wouldn't have been attainable with no their huge, immense contri- tion.
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Extra info for Artificial Neural Networks — ICANN 2001: International Conference Vienna, Austria, August 21–25, 2001 Proceedings
A neural network with m inputs, n weights, and o linear outputs is usually regarded as a mapping R m → R o from an input pattern x to the corresponding output y, for a given vector w of weights. Here we formalize such a network instead as a mapping N : R n → R o from weights to outputs (for given inputs), and write y = N (w). To extend this formalism to networks with nonlinear outputs, we deﬁne the output nonlinearity M : R o → R o and write z = M(y) = M(N (w)). For networks with linear outputs, M is the identity.
For the ﬁrst time, this was measured in an open-loop paradigm, achieved by speciﬁcally eliminating the cortico-thalamic feedback loop from within an otherwise intact cortex. 1) The visual world at the level of a single cortical cell is anything but constant. Receptive ﬁelds of cortical cells very often encounter new situations due to fast (saccadic) eye movements, which occur at a rate of up to 5 Hz, and/or due to object motion in the viewed scene. This process becomes more complicated by the fact that the visual activity reaches higher visual areas only after a certain delay, the visual latency, which is heavily contrast dependent: activity from dark objects arrive much later than from bright objects.
In a series of older studies, we had suggested that, instead of interfering with perception, visual latency might actually be used to segment images into their dark and bright parts and thereby help in process of object recognition. The naturally arising delay between bright-elicited and dark-elicited activity is enough to drive such a process. In fact, image segmentation can be accelerated and improved rather dramatically in technical systems when latencies are combined with a spin-relaxation model for image segmentation.
Artificial Neural Networks — ICANN 2001: International Conference Vienna, Austria, August 21–25, 2001 Proceedings by Stephen Grossberg (auth.), Georg Dorffner, Horst Bischof, Kurt Hornik (eds.)