Maass, W. (1997), On the Relevance of Time in Neural Computation and Learning. Proceedings of ALT'97, Lecture Notes in Computer Science, Springer, Berlin. Home/Search Document Details and Download
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On Computing Boolean Functions by a Spiking Neuron - Schmitt (1998) (2 citations) (Correct)
.... as a constant throughout this article. The neuron model that we consider here is a simple version of a leaky integrateand fire neuron. Further discussions of this and other neuron models can be found in the brief article of Softky and Koch [15] or in the surveys by Gerstner [2] and Maass [7, 8]. The latter also contain comprehensive lists of references to relevant literature from biophysics and neurobiology. The main simplification of our model is the embodiment of the postsynaptic potential as a rectangular pulse rather than a continuous function of a similar shape. Rectangular pulses ....
W. Maass, On the relevance of time in neural computation and learning, in: Proceedings of the 8th International Workshop on Algorithmic Learning Theory ALT'97, eds. M. Li and A. Maruoka, Lecture Notes in Artificial Intelligence, vol. 1316, Springer, Berlin, 1997, pp. 364--384.
Neural Networks with Dynamic Synapses - Fuchs (1998) Self-citation (Maass) (Correct)
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Maass, W. (1997), On the Relevance of Time in Neural Computation and Learning. Proceedings of ALT'97, Lecture Notes in Computer Science, Springer, Berlin.
Boolean Functions and Artificial Neural Networks - Martin Anthony Department (Correct)
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W. Maass. On the relevance of time in neural computation and learning. In Proceedings of the 8th International Workshop on Algorithmic Learning Theory, ALT'97 (M. Li and A. Maruoka, eds). Springer, Berlin, 1997.
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