QLS Seminar Series - Paul Bogdan
Theoretical Foundations of NeuroAI: A Modeling Framework Motivated by Living Neuronal Network Dynamics
Paul Bogdan, USC
Tuesday March 31, 12-1pm
Zoom Link:听
In Person: 550 Sherbrooke, Room 189
Abstract:听Brains build compact models of the world from just a few noisy and possibly conflicting observations. Biological brains can also predict uncanny events via memory-based analogies especially when resources are limited. The ability of biological intelligence to discover, generalize, hierarchically reason and plan, and complete a wide range of unknown heterogeneous tasks calls for a comprehensive understanding of how distributed networks of interactions among neurons, glia, and vascular systems enable animal and human cognition. Such an understanding can serve as a basis for advancing the design of artificial general intelligence (AGI). In this talk, we will discuss the challenges and potential solutions for inferring the theoretical foundations of biological intelligence and NeuroAI. To infer networks from very scarce and noisy data, we propose a new mathematical framework capable of learning the emerging causal fractal memory from biological neuronal spiking activity. This framework offers insight into the topological properties of the underlying neuronal networks and helps us predict animal behavior during cognitive tasks. We will also discuss an AI framework for mining the optical imaging of brain activity and reconstructing the weighted multifractal graph generators governing the neuronal networks from very scarce data. This network generator inference framework can reproduce a wide variety of network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome. We will discuss how network science-based AI can discover the phase transitions in complex systems and help with designing protein鈥搉anoparticle assemblies. Inspired by the multifractal formalism for detecting phase transitions in biological neuronal networks, we explore the principles of self-organization in Large Language Models (LLMs). We reveal the intricate dynamics of neuron interactions, showing how self-organization facilitates the emergence of complex patterns and intelligence within LLMs.