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Abstract: We find the VC dimension of a leaky integrate-andfire neuron model. The VC dimension quantifies the ability of a function class to partition an input pattern space, and can be considered a measure of computational capacity. In this case, the function class is the class of integrate-and-fire models generated by varying the integration time constant ø and the threshold `, the input space they partition is the space of continuous-time signals, and the binary partition is specified by whether or... (Update)
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...on the number of weights and the particular type of activation being used. In particular, we continue the work described in [5] see also [18] for related work) which had obtained estimates of these quantities for architectures with identity activations. By a threshold...
...tuning the delays in network of spiking neurons. Some first results can be found in [16] 32] 33] 39] 43] Zador and Pearlmutter [57] have investigated the VC dimension of spiking neurons in terms of another class of parameters that are relevant for neural computation:...
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BibTeX entry: (Update)
Zador, A. M. and Pearlmutter, B. A. (1996). VC dimension of an integrate-and-fire neuron model. http://citeseer.ist.psu.edu/article/zador96vc.html More
@inproceedings{ zador96vc,
author = "Anthony M. Zador and Barak A. Pearlmutter",
title = "{VC} Dimension of an Integrate-and-Fire Neuron Model",
booktitle = "Computational Learing Theory",
pages = "10-18",
year = "1996",
url = "citeseer.ist.psu.edu/article/zador96vc.html" }
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