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Complexity of Boolean Computationsfor a Spiking Neuron
| Content Provider | Semantic Scholar |
|---|---|
| Author | Schmitty, Michael |
| Copyright Year | 2007 |
| Abstract | We investigate the computational power of a model for a spiking neuron in the Boolean domain by comparing it with traditional neuron models such as threshold gates (or McCulloch-Pitts neurons) and sigma-pi units (or polynomial threshold gates). In particular, we estimate the number of gates required to simulate a spiking neuron by a disjunction of threshold gates and we establish tight bounds for this threshold number. Furthermore , we analyze the degree of the polynomials that a sigma-pi unit must use for the simulation of a spiking neuron. We show that this degree cannot be bounded by any xed value. Our results give evidence that the use of continuous time as a computational resource endows single-cell models with substantially larger computational capabilities. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.lmi.ruhr-uni-bochum.de/mschmitt/boolspikeicann.ps.gz |
| Alternate Webpage(s) | http://www.ruhr-uni-bochum.de/lmi/mschmitt/boolspikeicann.ps.gz |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
