Reinforcement Learning and Neuromodulators: Bridging Neuroscience and Artificial Intelligence
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 45
Special Issue Editors
Interests: microbiome
Interests: reinforcement learning; spiking neural networks; information processing; large-language model
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Aims and Scope
The integration of insights from neuroscience into artificial intelligence (AI) has revolutionized our understanding of learning and decision-making. Neuromodulators such as dopamine, serotonin, and acetylcholine play a crucial role in decision-making and adaptive behavior by influencing reward-based learning, exploration, and adaptation in biological systems. Recent advances in computational neuroscience and reinforcement learning (RL) have uncovered parallels between biological mechanisms and AI models, spurring interdisciplinary research.
This Special Issue aims to bridge the gap between neuroscience and AI and invites contributions that explore the intersection of neuromodulation and RL. Potential topics include computational models of neuromodulation, biologically inspired RL algorithms, and their applications in AI, cognitive sciences, and biomedical engineering.
Key Topics: Computational models of dopamine, serotonin, acetylcholine, and other neuromodulators. Biologically inspired reinforcement learning algorithms. Neuromodulation in decision-making and its implications for artificial agents. The role of eligibility traces, prediction errors, and plasticity in retrospective and prospective learning. Applications of neuromodulatory-inspired AI in robotics, healthcare, and personalized medicine. Comparative studies of biological and artificial learning mechanisms. Experimental approaches to validate biologically inspired RL algorithms using neurophysiological data. Theoretical frameworks linking neuroscience with reinforcement learning. Potential implications for cognitive disorders and neurorehabilitation.
Dr. Incheol Seo
Dr. Hyunsu Lee
Guest Editors
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Keywords
- reinforcement learning
- neuromodulators
- neuroscience
- Artificial Intelligence (AI)
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