As artificial intelligence continues to evolve, researchers are looking to nature for inspiration—specifically, the human brain. Neuro-inspired learning algorithms draw from the principles of neuroscience to develop more efficient, adaptable, and intelligent systems. This session explores how the brain's mechanisms can inform and enhance AI learning techniques.

Key topics include:

• Neuroscientific Foundations of AI: Exploring how the brain's neural networks, synaptic plasticity, and cognitive processes inspire the design of artificial neural networks and learning algorithms. How can understanding biological systems unlock more advanced learning mechanisms for AI?

• Deep Learning and Beyond: Discussing the evolution of deep learning algorithms, which are modeled after neural architectures in the brain, and how these models are progressing toward more advanced neuro-inspired designs. What are the next steps in bridging the gap between human-like cognition and AI systems?

• Learning Through Experience: Delving into algorithms that mimic biological learning processes, such as reinforcement learning, where AI learns through trial and error. How can we enhance these models to replicate more complex learning behaviors found in biological systems, such as habituation, adaptation, and generalization?

• Efficient Learning with Sparse Data: One of the strengths of biological systems is their ability to learn from limited information. This session will focus on how neuro-inspired algorithms can be designed to learn efficiently with minimal data, much like how humans learn from a few examples. What innovative approaches are being developed to address the challenge of sparse data in AI systems?

• Neuro-Inspired Algorithms in Real-World Applications: Exploring how neuro-inspired learning is being applied in areas like robotics, cognitive computing, and autonomous systems. How can these algorithms be used to improve decision-making, pattern recognition, and adaptability in real-world environments?

• Challenges and Opportunities: Discussing the challenges in scaling neuro-inspired learning algorithms for real-world applications. What are the bottlenecks in computational efficiency, data processing, and algorithmic complexity, and how can researchers overcome them?

• The Future of Neuro-Inspired AI: Looking ahead at how neuro-inspired algorithms may pave the way for next-generation AI systems. From brain-machine interfaces to more autonomous and adaptable AI, what’s on the horizon for neuro-inspired learning in AI?

This session brings together AI researchers, neuroscientists, and industry experts to explore how AI can learn in ways that are more akin to human cognition. Attendees will gain insights into cutting-edge algorithms that are pushing the boundaries of what artificial intelligence can achieve, as well as the challenges and exciting opportunities that lie ahead.