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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Folowosele, F. Hamilton, T.J. Etienne-Cummings, R. |
| Copyright Year | 1990 |
| Abstract | There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model-a generalized model of the leaky integrate-and-fire neuron with adaptive threshold-that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties-tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability-are demonstrated in this model. |
| Sponsorship | IEEE Computational Intelligence Society |
| Page Count | 13 |
| File Size | 1023054 |
| Starting Page | 1915 |
| Ending Page | 1927 |
| File Format | |
| ISSN | 10459227 |
| Volume Number | 22 |
| Issue Number | 12 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Adaptation models Biological system modeling Mathematical model Semiconductor device modeling Silicon spiking neurons Neuromorphic engineering neuron modeling silicon neurons |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Computer Science Applications Software |
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