NeuroCognitive Lab
Center of Neuroscience Generative AI
Our Research
NeuroCognitive Artificial Intelligence (NCAI) is a class of Generative AI systems inspired by neurophysiology and cognitive science that models intelligence as adaptive, goal-driven cognition. NCAI emphasizes learning through use, interactive refinement, and the development of emergent reasoning capabilities, mirroring how biological brains grow smarter through engagement with their environments.
Core Principles
The foundational concepts driving our research
Adaptive Learning
Systems that learn through interaction and experience, continuously improving their reasoning capabilities over time.
Goal-Driven Cognition
Intelligence modeled as purposeful, objective-oriented behavior rather than passive pattern matching.
Emergent Reasoning
Complex reasoning capabilities that emerge from the interaction of simpler cognitive processes.
Neuroplasticity-Inspired
Architectures inspired by how biological brains rewire and strengthen connections through use.
Research Areas
Key domains of investigation at the NeuroCognitive Lab
Symbolic Reasoning
Combining neural networks with symbolic AI for robust logical inference and explainable decisions.
Cognitive Architectures
Designing AI systems that mirror the modular, hierarchical structure of human cognition.
Memory Systems
Long-term and working memory mechanisms for contextual understanding and knowledge retention.
Attention Mechanisms
Advanced attention models for selective focus and resource allocation in complex reasoning tasks.
Multi-Modal Integration
Fusing information from text, images, and structured data into unified representations.
Metacognition
Systems that reason about their own reasoning, enabling self-improvement and calibrated confidence.
Collaborate With Us
Interested in partnering on cutting-edge AI research?