Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model
Abstract Neuromorphic hardware facilitates the fast and energy-efficient implementation of neural network-based artificial intelligence, making it particularly effective for addressing brain-inspired robotic challenges. The advancement of neuromorphic algorithms can continue to evolve in accordance...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01876-5 |
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| author | Mehdi Nadiri Andabili Soheila Nazari Tohid Moosazadeh |
| author_facet | Mehdi Nadiri Andabili Soheila Nazari Tohid Moosazadeh |
| author_sort | Mehdi Nadiri Andabili |
| collection | DOAJ |
| description | Abstract Neuromorphic hardware facilitates the fast and energy-efficient implementation of neural network-based artificial intelligence, making it particularly effective for addressing brain-inspired robotic challenges. The advancement of neuromorphic algorithms can continue to evolve in accordance with the principles of neural computing and the architectures of neural networks that draw inspiration from biological neural systems. In this perspective, we proposed a modified Izhikevich model that imitates the biological behaviors of the original neuron model using the Coordinate Rotation Digital Computer (CORDIC) algorithm. By employing adder and shifter operations to remove multipliers, the proposed method presents an effective digital hardware implementation of the Izhikevich model. The CORDIC-based Izhikevich model can accurately replicate the biological behaviors of the original model, according to error analysis and dynamic evaluations. Furthermore, the major goal of this work is to identify stable equilibrium points, chaotic regimes, and transitions between various dynamical states by investigating the dynamic behaviors of the proposed Izhikevich model under varying parameters. For this reason, the transitions from periodic to chaotic behavior are defined by applying numerical analyses, which include bifurcation diagrams and the maximum Lyapunov exponent. The potential for hardware implementation with high speed is the proposed model’s superiority over the original model, while it has a high compatibility level. In order to verify the effectiveness of the suggested hardware in comparison to previous studies, four cost functions are introduced based on operation frequency, power, and errors. Applying this method to the Spartan6 board can increase the speed of the proposed model by approximately 3.18 times compared to the original model. Therefore, the suggested hardware, whose features include low error rates, acceptable power consumption, and frequency capabilities, exhibits efficiency and impact in a variety of applications, such as modeling learning processes in the nervous system that are based on nonlinear and chaotic behaviors. |
| format | Article |
| id | doaj-art-82c7689cab2b4357b96ed0d35ecff21e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-82c7689cab2b4357b96ed0d35ecff21e2025-08-20T03:53:58ZengNature PortfolioScientific Reports2045-23222025-05-0115111910.1038/s41598-025-01876-5Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron modelMehdi Nadiri Andabili0Soheila Nazari1Tohid Moosazadeh2Department of Electrical Engineering, Central Tehran Branch, Islamic Azad UniversityFaculty of Electrical Engineering, Shahid Beheshti UniversityDepartment of Electrical Engineering, Central Tehran Branch, Islamic Azad UniversityAbstract Neuromorphic hardware facilitates the fast and energy-efficient implementation of neural network-based artificial intelligence, making it particularly effective for addressing brain-inspired robotic challenges. The advancement of neuromorphic algorithms can continue to evolve in accordance with the principles of neural computing and the architectures of neural networks that draw inspiration from biological neural systems. In this perspective, we proposed a modified Izhikevich model that imitates the biological behaviors of the original neuron model using the Coordinate Rotation Digital Computer (CORDIC) algorithm. By employing adder and shifter operations to remove multipliers, the proposed method presents an effective digital hardware implementation of the Izhikevich model. The CORDIC-based Izhikevich model can accurately replicate the biological behaviors of the original model, according to error analysis and dynamic evaluations. Furthermore, the major goal of this work is to identify stable equilibrium points, chaotic regimes, and transitions between various dynamical states by investigating the dynamic behaviors of the proposed Izhikevich model under varying parameters. For this reason, the transitions from periodic to chaotic behavior are defined by applying numerical analyses, which include bifurcation diagrams and the maximum Lyapunov exponent. The potential for hardware implementation with high speed is the proposed model’s superiority over the original model, while it has a high compatibility level. In order to verify the effectiveness of the suggested hardware in comparison to previous studies, four cost functions are introduced based on operation frequency, power, and errors. Applying this method to the Spartan6 board can increase the speed of the proposed model by approximately 3.18 times compared to the original model. Therefore, the suggested hardware, whose features include low error rates, acceptable power consumption, and frequency capabilities, exhibits efficiency and impact in a variety of applications, such as modeling learning processes in the nervous system that are based on nonlinear and chaotic behaviors.https://doi.org/10.1038/s41598-025-01876-5CORDIC algorithmHopf bifurcation- bifurcation diagramMaximum Lyapunov exponentError analysis- digital implementation |
| spellingShingle | Mehdi Nadiri Andabili Soheila Nazari Tohid Moosazadeh Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model Scientific Reports CORDIC algorithm Hopf bifurcation- bifurcation diagram Maximum Lyapunov exponent Error analysis- digital implementation |
| title | Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model |
| title_full | Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model |
| title_fullStr | Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model |
| title_full_unstemmed | Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model |
| title_short | Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model |
| title_sort | chaotic dynamics analysis and digital hardware design of the izhikevich neuron model |
| topic | CORDIC algorithm Hopf bifurcation- bifurcation diagram Maximum Lyapunov exponent Error analysis- digital implementation |
| url | https://doi.org/10.1038/s41598-025-01876-5 |
| work_keys_str_mv | AT mehdinadiriandabili chaoticdynamicsanalysisanddigitalhardwaredesignoftheizhikevichneuronmodel AT soheilanazari chaoticdynamicsanalysisanddigitalhardwaredesignoftheizhikevichneuronmodel AT tohidmoosazadeh chaoticdynamicsanalysisanddigitalhardwaredesignoftheizhikevichneuronmodel |