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DESCRIPTION:Theoretical Foundations of NeuroAI: A Modeling Framework Motiva
 ted by Living Neuronal Network Dynamics\n\nPaul Bogdan\, USC\n	Tuesday Marc
 h 31\, 12-1pm\n	Zoom Link: https://mcgill.zoom.us/j/87078928687\n	In Person:
  550 Sherbrooke\, Room 189\n	\n	Abstract: Brains build compact models of the
  world from just a few noisy and possibly conflicting observations. Biolog
 ical brains can also predict uncanny events via memory-based analogies esp
 ecially 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 unde
 rstanding of how distributed networks of interactions among neurons\, glia
 \, and vascular systems enable animal and human cognition. Such an underst
 anding can serve as a basis for advancing the design of artificial general
  intelligence (AGI). In this talk\, we will discuss the challenges and pot
 ential solutions for inferring the theoretical foundations of biological i
 ntelligence and NeuroAI. To infer networks from very scarce and noisy data
 \, we propose a new mathematical framework capable of learning the emergin
 g causal fractal memory from biological neuronal spiking activity. This fr
 amework offers insight into the topological properties of the underlying n
 euronal networks and helps us predict animal behavior during cognitive tas
 ks. We will also discuss an AI framework for mining the optical imaging of
  brain activity and reconstructing the weighted multifractal graph generat
 ors governing the neuronal networks from very scarce data. This network ge
 nerator inference framework can reproduce a wide variety of network proper
 ties\, differentiate varying structures in brain networks and chromosomal 
 interactions\, and detect topologically associating domain regions in conf
 ormation maps of the human genome. We will discuss how network science-bas
 ed AI can discover the phase transitions in complex systems and help with 
 designing protein–nanoparticle assemblies. Inspired by the multifractal fo
 rmalism for detecting phase transitions in biological neuronal networks\, 
 we explore the principles of self-organization in Large Language Models (L
 LMs). We reveal the intricate dynamics of neuron interactions\, showing ho
 w self-organization facilitates the emergence of complex patterns and inte
 lligence within LLMs.\n
DTSTART:20260331T160000Z
DTEND:20260331T170000Z
SUMMARY:QLS Seminar Series - Paul Bogdan
URL:/arts/channels/event/qls-seminar-series-paul-bogda
 n-371273
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