Neurocomputational model could shed light on cognitive skills development

A new study introduces a new neurocomputational model of the human brain that could shed light on how the brain develops complex cognitive skills and advance research on neural artificial intelligence. The study, published Sept. 19, was conducted by an international group of researchers from the Institut Pasteur and Sorbonne Université in Paris, CHU Sainte-Justine, Mila – Quebec Artificial Intelligence Institute and Université de Montréal.

The model who made the cover of the magazine Proceedings of the National Academy of Sciences of the United States of America (PNAS), describes neural development across three hierarchical levels of information processing:

  • the first sensorimotor level examines how the inner activity of the brain learns patterns from perception and associates them with action;
  • the cognitive level examines how the brain combines those patterns contextually;
  • finally, the conscious level takes into account how the brain distances itself from the outside world and manipulates learned patterns (via memory) that are no longer accessible to perception.

The team’s research provides clues to the core mechanisms underlying cognition thanks to the model’s focus on the interplay between two fundamental types of learning: Hebbic learning, which is associated with statistical regularity (i.e. repetition) — or as neuropsychologist Donald Hebb put it, “neurons firing together, connecting together” – and reinforcing learning, associated with reward and the dopamine neurotransmitter.

The model solves three tasks of increasing complexity at those levels, from visual recognition to cognitive manipulation of conscious perceptions. Each time, the team introduced a new core mechanism to progress.

The results highlight two fundamental mechanisms for the development of multi-level cognitive skills in biological neural networks:

  • synaptic epigenesis, with local-scale Hebrew learning and global reinforcement learning;
  • and self-organized dynamics, through spontaneous activity and a balanced excitatory/inhibitory ratio of neurons.

Our model shows how the convergence of neuro-AI highlights biological mechanisms and cognitive architectures that could fuel the development of the next generation of artificial intelligence and even eventually lead to artificial consciousness.”


Guillaume Dumas, team member, assistant professor of computational psychiatry at UdeM, and principal investigator at the CHU Sainte-Justine Research Center

Reaching this milestone may require integrating the social dimension of cognition, he added. The researchers are now looking at the integration of biological and social dimensions that play a role in human cognition. The team has already pioneered the first simulation of two whole brains interacting.

Anchoring future computing models in biological and social realities will not only continue to shed light on the core mechanisms underlying cognition, the team believes, but will also help build a unique bridge from artificial intelligence to the only known system with advanced social consciousness: the human brain.

Source:

Reference magazine:

Volzhenine, K., et al. (2022) Development of multilevel cognitive skills in an artificial neural network. PNAS. doi.org/10.1073/pnas.2201304119.

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