Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural networks (ANNs). Biological networks are characterized by intricate, adaptive connections that govern behavior, cognition, and learning, while ANNs are computational models designed to mimic some of these processes for practical applications. However, a comprehensive integration of the two fields is still lacking, particularly in how artificial models can capture the complexity, flexibility, and efficiency of biological networks. Advances in computational modeling, neuroscience, and machine learning now offer a unique opportunity to bring these domains closer. By leveraging insights from biological systems, researchers can refine ANN architectures, while ANN-derived frameworks provide tools for understanding large-scale biological network dynamics.

Despite advances in understanding neural networks, a significant gap exists between the biological complexity of neural systems and the simplified structures of artificial neural networks (ANNs). Biological networks exhibit dynamic plasticity, energy efficiency, and adaptive behaviors that are not fully replicated in current ANN models. Conversely, ANNs excel in scalability and task-specific performance but lack biological plausibility. Bridging this gap is crucial for both advancing neuroscience and improving artificial intelligence systems.

This Research Topic seeks to address this challenge by integrating insights from biological and artificial network models. Contributions may explore the use of ANNs to simulate biological processes, application of biologically inspired principles to enhance ANN architectures, or computational studies on large-scale neural dynamics. By fostering interdisciplinary research, this topic aims to develop frameworks that unify the strengths of both systems, advancing our understanding of neural dynamics and their potential technological applications.

This Research Topic focuses on the integration of biological and artificial neural network models to advance our understanding of neural dynamics in systems neuroscience. Contributions should address themes such as:

- Biologically Inspired ANN Architectures: Exploring how principles like plasticity, energy efficiency, or dynamic connectivity in biological systems can improve ANN design.

- Modeling Biological Networks: Using ANNs or computational models to simulate neural processes such as learning, memory, or network adaptation in biological systems.

- Comparative Analyses: Studies contrasting biological networks and ANNs to identify shared principles or key differences in function and organization.

- Applications in Neuroscience: Leveraging ANN frameworks to study large-scale neural dynamics, disease modeling, or brain-machine interfaces.

Submissions should showcase interdisciplinary approaches, computational tools, or experimental findings that unify biological and artificial network research, offering insights into neural systems' function and adaptability.

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Unraveling Neural Network Dynamics: Integrating Artificial Neural Networks and Biological Network Models in Systems Neuroscience