The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Lower Bounds for the Computational Power of Networks of Spiking Neurons
Abstract
We investigate the computational power of a formal model for networks of spiking neurons. It is shown that simple operations on phase differences between spike-trains provide a very powerful computational tool that can in principle be used to carry out highly complex computations on a small network of spiking neurons. We construct networks of spiking neurons that simulate arbitrary threshold circuits, Turing machines, and a certain type of random access machines with real valued inputs. We also show that relatively weak basic assumptions about the response and threshold functions of the spiking neurons are sufficient to employ them for such computations.
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Citations
Cites
Threshold circuits of bounded depth
Hajnal, A., Maass, W., Pudlák, P., Szegedy, M., & Turán, G. (1993) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(93)90001-d · Cited by 237
Self-citation
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Self-citationError-backpropagation in temporally encoded networks of spiking neurons
Bohte, S. M., Kok, J. N., & La Poutré, H. (2002) · Neurocomputing · doi:10.1016/s0925-2312(01)00658-0 · Cited by 1,021
Metadata
- Authors
- Maass, W.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1996-01
- Volume
- 8
- Issue
- 1
- Pages
- 1-40
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W262829378
- Cited by
- 226
- References
- 1
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maass, W. (1996). Lower Bounds for the Computational Power of Networks of Spiking Neurons. Neural Computation. https://doi.org/10.1162/neco.1996.8.1.1
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