AI and Natural Learning Systems: Bi-Directional Insights

The intersection of Artificial Intelligence (AI) and natural learning systems presents a unique opportunity to deepen our understanding of both fields, and of intelligence generally. AI has advanced to the point where its learning systems—such as machine learning, deep learning, neural networks, and reinforcement learning—are capable of simulating complex behaviors and cognitive processes. This confluence of AI and neuroscience offers a new frontier for research, where insights can flow bidirectionally. Studying AI systems can reveal principles that govern how humans and other natural systems learn, adapt, and process information. In return, techniques from neuroscience and psychology can be applied to understand the learning abilities of AI models relative to biological agents, and to inform the development of more sophisticated, adaptive AI agents.

This Research Topic aims to explore the diverse ways AI can be applied to study natural learning systems and, conversely, how the biological principles of learning can guide advancements in AI. Our objective is to bring together interdisciplinary research that spans the domains of AI, neuroscience, cognitive science, and computational modeling. We are particularly interested in studies that leverage AI to simulate, model, and interpret cognitive processes, brain functions, or neurological disorders, as well as research that applies knowledge from neuroscience and cognitive science to better understand and improve AI.

Scope and Focus Areas: This collection seeks papers that address fundamental challenges in studying intelligence—whether in biological systems or AI. These topics include but are not limited to:

-How AI learning systems can inform our understanding of natural cognition, decision-making, memory, and learning.
-Explorations of what current AI systems are “missing”, relative to biological agents
-The simulation of cognitive disorders using AI, and how this modeling can offer insights into brain dysfunction and health.
-Applications of AI in analyzing and interpreting neuroimaging data, neural activity, and behavioral data.
-The development of hybrid models that merge biological and artificial principles to explore intelligence.
-Suggestions from cognitive sciences of how current AI systems could be made more resilient and adaptive.
-High-level, “big picture” questions about intelligence that span multiple disciplines and are difficult to address in isolated fields.
-AI models that reveal new insights into the adaptive nature of human cognition, such as neuroplasticity and resilience.
- AI-based tools for diagnosing, predicting, or treating mental health conditions and cognitive disorders.
-Computational models that simulate neural networks, brain function, and cognitive processes in a biologically plausible way.
-The use of AI to bridge gaps between neuroscience, psychology, computing, and cognitive science in the study of intelligence.

Article Types: We welcome a range of article types that contribute to the topic of AI and natural learning systems, including:

-Original Research: Studies that present novel findings or innovations in the field.
-Reviews: Articles that synthesize current knowledge and trends at the intersection of AI, neuroscience, and cognitive science.
-Methodological Papers: Contributions that describe new computational or AI-based methods for studying intelligence.
-Perspective and Opinion Papers: Articles that offer forward-looking ideas, proposals, or challenges for the interdisciplinary study of intelligence.

This Research Topic seeks to foster a deeper understanding of intelligence by bringing together research from diverse fields. We encourage submissions that explore both biological and artificial intelligence to push the boundaries of current knowledge.