Neural computation is fundamental to understanding intelligence and adaptive behavior in both biological organisms and artificial systems. From the intricate dynamics of the brain to the powerful algorithms driving modern AI, the ability to process information, learn, and make decisions arises from complex networks of interconnected units. While biological neural networks have evolved to achieve improved efficiency and robustness, artificial neural networks draw inspiration from these principles to solve challenging computational problems. The interplay between these two domains offers a rich landscape for scientific inquiry, with advances in one often illuminating pathways for progress in the other, driving our understanding of intelligence itself. This Research Topic aims to explore this fascinating intersection.

Despite significant progress in both neuroscience and artificial intelligence, a unified understanding of the underlying computational principles governing real and artificial neural networks remains a significant challenge. The problem lies in effectively translating insights from biological systems into more robust and generalizable AI, and conversely, using theoretical and empirical findings from AI to develop testable hypotheses about brain function. This Research Topic aims to address this gap by fostering interdisciplinary dialogue and presenting cutting-edge research that bridges these domains. We seek contributions that explore common computational motifs, highlight divergent strategies, and leverage the strengths of each field to advance our collective understanding of neural computation. By bringing together diverse perspectives, we hope to stimulate novel research avenues, leading to more biologically plausible AI and more computationally informed neuroscience.

This Research Topic welcomes original research, reviews, and perspective articles that explore the computational principles, mechanisms, and architectures underlying neural processing in both biological and artificial systems. We encourage submissions that seek to bridge the gap between these fields, offering mutual insights and fostering interdisciplinary collaboration.

Specific themes include, but are not limited to:

Bio-inspired computational models and algorithms for artificial intelligence.

Computational neuroscience studies of learning, memory, perception, and decision-making in real brains.

Application of machine learning and deep learning techniques to analyze complex neuroscientific datasets.

Theoretical frameworks and mathematical models of neural dynamics, plasticity, and information processing.

Comparative analyses of representational learning, robustness, and efficiency in biological and artificial neural networks.

Neuromorphic computing and hardware implementations inspired by biological neural networks.

Studies on the impact of biological constraints (e.g., energy efficiency, connectivity patterns) on artificial network design.

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Neural Computation in real and artificial neural networks