Artificial Deep Neural Networks (DNNs) have made breakthrough progress in recent years based on loose structural similarity to the brain. Dramatic advances in the past few years have reshaped what is possible and potentially distinctive about AI. Yet, how seemingly intelligent performance is achieved remains fundamentally distinct from the human brain.

This editorial project focuses on the interface of neuro-inspired computation, launched in conjunction with the third Neuro-inspired Computation course held at the University of Tokyo International Research Center for Neurointelligence (July 2-5, 2025).

Neurointelligence spans broad areas of mutual interest to both communities, like network architectures, intrinsic dynamics, plasticity & criticality, multi-agent (social) learning, neuromodulation or reinforcement learning.

All course participants are strongly encouraged to submit their work to the Research Topic, while the project is also open to non-conference attendees who would be interested.

As biological and artificial intelligence have much to learn from each other, the collection of papers aims to capture the expanding global excitement in this innovative research frontier where neuronal network function in nature and in silico converge.

We welcome all article types, including research reports, method papers, perspectives, reviews, and case reports related to studying the aspects of structure, function, and development of both biological and artificial neural networks. Toward this aim, we welcome articles addressing, but not limited to:

• network architectures
• intrinsic dynamics
• E-I balance
• homeostasis
• plasticity & criticality
• multi-agent (social) learning
• reinforcement learning
• neuromodulation
• cognitive architecture
• developmental robotics
• human-AI collaboration

Are you interested in contributing a manuscript to this Research Topic?

Neuro-inspired Computation 2025