New Neurocomputational Brain Model Could Advance AI Research

A new study out of the University of Montreal introduces a new neurocomputational model of the human brain. This new model provides deeper insight into how the brain develops complex cognitive abilities, and it could advance neural artificial intelligence (AI) research. 

The study was published on September 19 in the journal Proceedings of the National Academy of Sciences (PNAS). 

It was carried out by an international group of researchers from the Institut Pasteur and Sorbonne Université in Paris, the CHU Sainte-Justine, Mila — Quebec Artificial Intelligence Institute, and Université de Montréal. 

Neural Development

The study describes neural development over three hierarchical levels of information processing: 

• Sensorimotor Level: Explores how the brain’s inner activity learns patterns from perception and associates them with action.
  
• Cognitive Level: Examines how the brain contextually combines those patterns.

• Conscious Level: Considers how the brain dissociates from the outside world and manipulates learned patterns (via memory) no longer accessible to perception. 

The new research provides deeper insight into the core mechanisms underlying cognition due to the model’s focus on the interplay between two fundamental types of learning. The first is Hebbian learning, which is associated with statistical regularity, such as repetition. The second is reinforcement learning, which is associated with reward and the dopamine neurotransmitter. 

The newly developed model solves three tasks of increasing complexity across the levels, and the team introduced a new core mechanism each time, which helped it progress. 

The results highlighted two fundamental mechanisms for the multilevel development of cognitive abilities in biological neural networks: 

• Synaptic Epigenesis: Hebbian learning takes place at the local scale while reinforcement learning takes place at the global scale.

• Self-Organized Dynamics: Spontaneous activity and balanced excitatory/inhibitory ratio of neurons. 

Next-Gen AI and Artificial Consciousness

Guillaume Duman is a team member and an assistant professor of computational psychiatry at UdeM, as well as a principal investigator at the CHU Sainte-Justine Research Center. 

“Our  model demonstrates how the neuro-AI convergence highlights biological mechanisms and cognitive architectures that can fuel the development of the next generation of artificial intelligence and even ultimately lead to artificial consciousness,” Dumas says. 

To reach this, Dumas says they may have to integrate the social dimensions of cognition. The team is now looking at integrating biological and social dimensions, and they have already created the first simulation of two whole brains in interaction. 

The team believes that by anchoring future computational models in biological and social realities, they will gain further insight into the core mechanisms underlying cognition. They also believe it will provide a bridge between AI and the human brain.