A design of an action potential generator for electrical neurons
DOI:
https://doi.org/10.31117/neuroscirn.v9i1.475Keywords:
Action Potential Generator (APG), Electrical neurons, Neuroengineering, Action potential, Electrical impulsesAbstract
Recently, in the field of neuroengineering, the Action Potential Generator (APG) is an essential component used to stimulate electrical impulses during communication among neurons. Besides, the generators are required in various applications, such as neural prosthetics, brain-computer interfaces, and neuronal behavioural studies. However, traditional methods for APG in electrical neurons often rely on intricate biological systems or complex electronic circuits, which can limit efficacy and flexibility in real-time environments. In addition, these techniques can be limited in scalability, consume high power, and present issues when combined with existing neural interfaces. As a result, the proposed design creates an efficient, flexible system by combining cutting-edge materials with flexible parts. The main advancement is the combination of flexible parts and cutting-edge materials to produce a physical action potential generator with highly biomimetic and adjustable outputs. By offering previously unheard-of control and fidelity in simulating natural neural activity for research and development, particularly as a tissue-free platform for electrode testing, the generator enables a vast array of firing patterns comparable to those of biological neurons, greatly improving the reliability of neural signal transmission. Hence, the proposed APG represents a substantial advance in neuroengineering and provides a versatile and effective solution for generating electrical signals in neurons.
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Ascoli, A., Demirkol, A. S., Messaris, I., Ntinas, V., Prousalis, D., Slesazeck, S., Mikolajick, T., Corinto, F., Bonnin, M., & Gilli, M. (2025). Edge of chaos theory unveils the first and simplest ever reported Hodgkin–Huxley neuristor. Advanced Electronic Materials, 11(8), 2400789. https://doi.org/10.1002/aelm.202400789
Chen, B., Liu, Y., Bao, H., Zhang, X., & Bao, B. (2025). Coexisting and bursting oscillations in a second-order RC-oscillator-based piecewise linear neuron circuit. Nonlinear Dynamics, 113, 17141–17159. https://doi.org/10.1007/s11071-025-11007-4
Choo, J. W., Nath, S., & Kumar, T. N. (2025). Neuromorphic computing using memristor synapses and CMOS neurons. Research Square. https://doi.org/10.21203/rs.3.rs-6324848/v1
Dambri, O. A., Azarnoush, A., Makrakis, D., Levesque, G., Witter, M., & Hafid, A. S. (2025). Design and implementation of a simulation framework for a bio–neural dust system. Modelling, 6(1), 8. https://doi.org/10.3390/modelling6010008
Deshmukh, A., Settell, M., Cheng, K., Knudsen, B., Trevathan, J., LaLuzerne, M., Blanz, S., Skubal, A., Verma, N., & Romanauski, B. (2025). Epidural spinal cord recordings (ESRs): Sources of neural-appearing artifact in stimulation evoked compound action potentials. Journal of Neural Engineering, 22(1), 016050. https://doi.org/10.1088/1741-2552/ad7f8b
Fu, S., Gao, H., Wang, S., Wang, X., Woodard, T., Wang, Z., Kong, J., Lovley, D. R., & Yao, J. (2025). Constructing artificial neurons with functional parameters comprehensively matching biological values. Nature Communications, 16(1), 8599. https://doi.org/10.1038/s41467-025-63640-7
Hu, C., Yang, Q., Liu, Y., Röddiger, T., Butkow, K.-J., Ciliberto, M., Pullin, A. L., Stuchbury-Wass, J., Hassan, M., & Mascolo, C. (2025). A survey of earable technology: Trends, tools, and the road ahead. arXiv Preprint. https://doi.org/10.48550/arXiv.2506.05720
Hussain, M. A., Grill, W. M., & Pelot, N. A. (2024). Highly efficient modeling and optimization of neural fiber responses to electrical stimulation. Nature Communications, 15(1), 7597. https://doi.org/10.1038/s41467-024-51709-8
Iqbal, M. S., Heyat, M. B. B., Parveen, S., Hayat, M. A. B., Roshanzamir, M., Alizadehsani, R., Akhtar, F., Sayeed, E., Hussain, S., & Hussein, H. S. (2024). Progress and trends in neurological disorders research based on deep learning. Computerized Medical Imaging and Graphics, 116, 102400. https://doi.org/10.1016/j.compmedimag.2024.102400
Jacak, J., & Jacak, W. (2025). Ionic plasmon-polaritons in neural signaling II: Control role of the myelin over frequency and speed of stimulus in axons. Plasmonics, 20, 11331–11347. https://doi.org/10.1007/s11468-025-03212-z
Khanday, M. A., & Khanday, F. A. (2024). A bio-inspired ferroelectric tunnel FET-based spiking neuron for high-speed energy efficient neuromorphic computing. Micro and Nanostructures, 188, 207788. https://doi.org/10.1016/j.micrna.2024.207788
Khelfaoui, H., Ibaceta-Gonzalez, C., & Angulo, M. C. (2024). Functional myelin in cognition and neurodevelopmental disorders. Cellular and Molecular Life Sciences, 81(1), 181. https://doi.org/10.1007/s00018-024-05222-2
Kim, H.-S., Baby, T., Lee, J.-H., Shin, U. S., & Kim, H.-W. (2024). Biomaterials-enabled electrical stimulation for tissue healing and regeneration. Med-X, 2(1), 7. https://doi.org/10.1007/s44258-024-00020-8
Li, Y., & Zhong, Z. (2024). Decoding the application of deep learning in neuroscience: A bibliometric analysis. Frontiers in Computational Neuroscience, 18, 1402689. https://doi.org/10.3389/fncom.2024.1402689
Liu, L., Yun, Z., Manubens-Gil, L., Chen, H., Xiong, F., Dong, H., Zeng, H., Hawrylycz, M., Ascoli, G. A., & Peng, H. (2025). Connectivity of single neurons classifies cell subtypes in mouse brains. Nature Methods, 22(4), 861–873. https://doi.org/10.1038/s41592-025-02621-6
Max, K., Sames, L., Ye, S., Steinkühler, J., & Corradi, F. (2025). Synthetic biology meets neuromorphic computing: Towards a bio-inspired olfactory perception system. Neuromorphic Computing and Engineering, 5, 034010. https://doi.org/10.1088/2634-4386/aded2d
Mohabbati, V., Sullivan, R., Yu, J., Georgius, P., Brooker, C. D., Siorek, M., McClelland, N. L., Coletti, F., Sun, X., & Franke, A. (2025). Early outcomes with a flexible ECAP based closed loop using multiplexed spinal cord stimulation waveforms—single-arm study with in-clinic randomized crossover testing. Pain Medicine, 26(11), 773–782. https://doi.org/10.1093/pm/pnaf058
Nalliboyina, K., & Ramachandran, S. (2024). An energy-efficient hybrid CMOS spiking neuron circuit design with a memristive based novel T-type artificial synapse. AEU-International Journal of Electronics and Communications, 173, 154982. https://doi.org/10.1016/j.aeue.2023.154982
Palmisano, V. F., Anguita‐Ortiz, N., Faraji, S., & Nogueira, J. J. (2024). Voltage‐gated ion channels: Structure, pharmacology and photopharmacology. ChemPhysChem, 25(16), e202400162. https://doi.org/10.1002/cphc.202400162
Parveen, S., Showkat, F., Badesra, N., Dar, M. S., Maqbool, T., & Dar, M. J. (2024). Axonal degeneration, impaired axonal transport, and synaptic dysfunction in motor neuron disorder. In A. Khan, M. A. Rather & G. M. Ashraf (Eds.), Mechanism and Genetic Susceptibility of Neurological Disorders (pp. 199-229). Springer.
Potirakis, S. M., Diakonos, F. K., & Contoyiannis, Y. F. (2025). A Spike Train Production Mechanism Based on Intermittency Dynamics. Entropy, 27(3), 267. https://doi.org/10.3390/e27030267
Raj, P. (2025). Neural networks for real-time signal processing in nano-bio-electronic devices. International Journal of Scientific Research and Engineering Development, 8(3), 1332-1337. https://doi.org/10.5281/zenodo.15577848
Schaufelberger, M. (2024). Spiking neural networks for control [Masters thesis, KTH Royal Institute of Technology]. Publications KTH.
Schneider-Mizell, C. M., Bodor, A. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C., Kapner, D., Kinn, S., & Mahalingam, G. (2025). Inhibitory specificity from a connectomic census of mouse visual cortex. Nature, 640, 448-458. https://doi.org/10.1038/s41586-024-07780-8
Shabnum, S. S., Siranjeevi, R., Raj, C. K., Nivetha, P., & Benazir, K. (2025). A comprehensive review on recent progress in carbon nanotubes for biomedical application. Environmental Quality Management, 34(3), e70040. https://doi.org/10.1002/tqem.70040
Shrivastava, A., Kumar, A., Aggarwal, L. M., Pradhan, S., Choudhary, S., Ashish, A., Kashyap, K., & Mishra, S. (2024). Evolution of bioelectric membrane potentials: Implications in cancer pathogenesis and therapeutic strategies. The Journal of Membrane Biology, 257(5-6), 281–305. https://doi.org/10.1007/s00232-024-00323-2
Stiti, C., Benrabah, M., Aouaichia, A., Oubelaid, A., Bajaj, M., Tuka, M. B., & Kara, K. (2024). Lyapunov-based neural network model predictive control using metaheuristic optimization approach. Scientific Reports, 14(1), 18760. https://doi.org/10.1038/s41598-024-69365-9
Tawade, P., & Mastrangeli, M. (2024). Integrated electrochemical and optical biosensing in organs‐on‐chip. ChemBioChem, 25(3), e202300560. https://doi.org/10.1002/cbic.202300560
Tendulkar, M., Tendulkar, R., Dhanda, P. S., Yadav, A., Jain, M., & Kaushik, P. (2024). Clinical potential of sensory neurites in the heart and their role in decision-making. Frontiers in Neuroscience, 17, 1308232. https://doi.org/10.3389/fnins.2023.1308232
Trunov, K., Kraiushkin, V., Zenkevich, A., & Khanas, A. (2025). Implementation of an artificial spiking neuron with photoreceptor functionality using gas discharge tubes. Journal of Vacuum Science & Technology A, 43(3), 033002. https://doi.org/10.1116/6.0004433
Wang, S., Wu, M., Liu, W., Liu, J., Tian, Y., & Xiao, K. (2024). Dopamine detection and integration in neuromorphic devices for applications in artificial intelligence. Device, 2(2), 100284. https://doi.org/10.1016/j.device.2024.100284
Wells, S. A., Morris, P. G., Taylor, J. D., & Nogaret, A. (2025). Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data. Frontiers in Computational Neuroscience, 19, 1458878. https://doi.org/10.3389/fncom.2025.1458878
Yousuf, M., Rochet, J. C., Singh, P., & Hussain, M. M. (2025). Advancing brain organoid electrophysiology: Minimally invasive technologies for comprehensive characterization. Advanced Materials Technologies, 10(7), 2401585. https://doi.org/10.1002/admt.202401585
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