• Harshada Chandrakant Gore
• Ankita Maruti Gargote
• Vidya Babulal Khairkhar

Differential equations have become fundamental tools in the modeling and analysis of neural networks, providing a mathematical framework to understand learning dynamics, stability, and signal propagation. Ordinary differential equations (ODEs) describe the continuous-time behavior of neurons and learning processes, such as gradient descent, while partial differential equations (PDEs) are used to model spatial-temporal dynamics in deep learning architectures, such as convolutional neural networks (CNNs). This review explores the role of differential equations in neural networks, emphasizing their applications in learning, stability analysis, and future research directions in the field. The integration of these equations into neural network modeling is expected to enhance our understanding of complex systems and improve the performance of artificial intelligence models.

Differential equations, neural networks, mathematical modeling, learning dynamics, stability analysis, ordinary differential equations (ODEs), partial differential equations (PDEs), deep learning, convolutional neural networks (CNNs)

"Review of Differential Equations in modeling Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e833-e836, October-2024, Available :http://www.jetir.org/papers/JETIR2410494.pdf

"Review of Differential Equations in modeling Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe833-e836, October-2024, Available at : http://www.jetir.org/papers/JETIR2410494.pdf