The Science of Adaptive Intelligence: A Multidisciplinary Exploration
At Self-Stacked-Systems Co-Lab, we explore intelligence as a dynamic and evolving phenomenon, shaped by complex networks, hierarchical structures, and adaptive processes. Our research spans across cognitive neuroscience, robotics, complex systems, and human evolution, aiming to uncover the principles that govern cognition in both biological and artificial systems.
Cognitive Neuroscience: The Architecture of Thought
Understanding the human mind requires unraveling the neural mechanisms that underlie perception, learning, and decision-making, stage-by-stage. A stage-like approach is foundational to us. It enables the process of quantifying and designing self-organizing complex structures. We investigate how hierarchical processing and distributed networks enable intelligence, from low-level sensory processing to high-level cognition.
Complex Networks: Mapping Intelligence
Natural systems and artificial intelligence systems all rely on interconnected networks. Our research examines how network structures participate in the formation of cognition, how self-organization parallels the emergence of adaptation, and how transitions condensate the phenomenon of cost-effective evolution, applying network science to both biological and computational models of intelligence.
Complex Systems: The Dynamics of Adaptation
Intelligence is more than the sum of its parts—it emerges from the interaction of multiple components. By studying self-organization, non-linear dynamics, and emergent properties, we seek to understand the intricacies of complex, adaptive systems. We are curious about not only how to mimic learning, but how to discriminate the processes of learning and development so as to mimic development and evolution itself, with reverence to biological plausibility.
Robotics and Artificial Cognition: Bridging Biology and Technology
How can artificial systems replicate the flexibility and adaptability of human intelligence? Our work in robotics and computational cognition explores biologically inspired models that integrate perception, action, learning and development, pushing the boundaries of AI and autonomous systems to a new level of self-evolution.
Hierarchical Processing: From Neurons to Thought
Cognitive systems are structured hierarchically, from neural circuits to abstract reasoning. We study how information flows through different levels of organization, enabling problem-solving in a cost-effective maximally efficient stage-like manner.
Human Development and Evolution: Intelligence Across Time
To fully understand cognition, we must trace its origins. We explore how evolutionary pressures and developmental processes shape intelligence, offering insights into both natural and artificial cognitive architectures.
By integrating these perspectives, Self-Stacked-Systems Co-Lab seeks to unravel the deep mechanisms of adaptive intelligence, shaping the future of cognitive science, AI, and beyond.