Leaky Integrate-and-Fire Neuron Model-Based SNN Latency Estimation Using FNS

• Published in: Journal of electronic materials 2024-07, Vol.53 (7), p.3560-3568
• Main Authors: Hussain, Syed Ali, Dhanush, Karnatapu Sri Sai, Eswar, Kothuri Abhinav, Vaishnavi, Chundru, Surya, Kaveti Sujith, Prasad V, P N S B S V, Samanta, Swagata, Sanki, Pradyut Kumar
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biological effects; Brain; Characterization and Evaluation of Materials; Chemistry and Materials Science; Design parameters; Digital signal processing; Electronics and Microelectronics; Instrumentation; International Conference on Low-Energy Digital Devices and Computing 2023; Materials Science; Network latency; Neural networks; Optical and Electronic Materials; Solid State Physics; Topical Collection: Low-Energy Digital Devices and Computing 2023
• ISSN: 0361-5235; 1543-186X
• DOI: 10.1007/s11664-024-11078-w

The use of neural modeling tools is becoming increasingly common in the exploration of human brain behavior, enabling effective simulations through event-driven methodologies. As a result, years of study and advancements in the field of neurotechnology have led to the creation of several artificial neural network approaches that mimic biological neural networks. The event-driven approach provides an effective method for mimicking large-scale spiking neural networks (SNNs), by taking advantage of the brain’s sparse processing. This paper investigates SNN employing a leaky integrate-and-fire neuron model with latency estimation through FNS. A three-layer feedforward network (FFN) is constructed, incorporating design parameters from Config Wizard. Notably, our study sheds light on the impact of synchrony within a simple FFN. Through the incorporation of biologically plausible delay effects, our model offers novel insights that complement the existing literature. Neural activity is organized in CSV format files, facilitating the reconstruction of electrophysiological-like signals. FNS enables a comprehensive exploration of interactions within and between populations of spiking neurons. In the near future, we intend to use these findings in situations where this particular class of neural networks and digital signal processing (DSP) applications can be combined to create potent nonlinear DSP techniques.