Learning From Machines

Overview

Explore the intersection of neuroscience and machine learning, uncovering insights into human cognition, perception, and consciousness through computational models and visual metaphors.

Syllabus

• Introduction to Machine Learning and Neuroscience
Overview of machine learning principles
Basics of neuroscience relevant to AI
• Neural Networks and the Brain
Comparison of biological neurons and artificial neurons
Structure and function of neural networks
Hebbian learning and synaptic plasticity
• Machine Learning Models of Cognition
Computational models of human learning
Memory systems and neural computation
Reinforcement learning parallels with human decision-making
• Perception and Sensory Processing
Visual processing in brains and machines
Representation and interpretation of sensory information
Convolutional neural networks and visual perception
• Consciousness and Machine Intelligence
Theories of consciousness
Potential for machine consciousness
Ethical implications of conscious machines
• Emulating Human Perception
Developing visual metaphors in machine learning
Bridging human perception and computational models
Applications in computer vision and image recognition
• Advanced Topics in Neuro-AI
Brain-inspired architectures and algorithms
Neuromorphic computing
Future directions in AI and neuroscience integration
• Project and Case Studies
Analyzing real-world applications of neuro-AI
Designing and implementing a simple neuro-inspired model
Evaluating case studies of machine learning in neuroscience research
• Conclusion and Future Perspectives
Summarizing key insights
Discussing open questions and challenges
Exploring future research pathways in neuro-AI integration

Subjects

Conference Talks