Name
Abstract | ||
|---|---|---|
Adaptive synaptic neuron model involves complex activation functions. These nonlinearities lead to complicated hardware implementations, which greatly hinder neuron-based applications. To effectively solve this issue, a piecewise-linear (PWL) activation function with simplified circuit implementation is presented for the adaptive synaptic neuron model in this brief. With this neuron model, the stability evolution mechanism of the equilibrium state is analyzed and the parameter- and initial condition-related neuron dynamics are numerically explored. Afterwards, an analog circuit is designed and manually made using commercially available components. The phase trajectories captured by the hardware experiments verify the feasibility of the PWL activation function. Thus, such a PWL simplification shows superiority in emulating neuron dynamics. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSII.2021.3124666 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS |
Keywords | DocType | Volume |
Neurons, Bifurcation, Adaptation models, Mathematical models, Trajectory, Numerical models, Numerical stability, Activation function, adaptive synaptic neuron, circuit implementation, hardware experiment, neuron dynamics | Journal | 69 |
Issue | ISSN | Citations |
3 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quan Xu | 1 | 28 | 7.13 |
| Shoukui Ding | 2 | 0 | 0.34 |
| Han Bao | 3 | 23 | 8.84 |
| Mo Chen | 4 | 0 | 0.34 |
| Bocheng Bao | 5 | 119 | 19.50 |