Neurons Brain Activity Consciousness Thought

A new brain model could pave the way for conscious AI

Neurons Brain activity Consciousness Thought

Mila and IVADO researchers present a new neurocomputational model of the human brain that could bridge the gap in understanding AI and the biological mechanisms underlying mental disorders.

A new model of the human brain.

A new study presents a new neurocomputational model of the human brain, which could shed light on how the brain develops complex cognitive skills and advance research on neural artificial intelligence. An international team of scientists from the Institut Pasteur and Sorbonne University in Paris, CHU Sainte-Justine, Mila – Quebec Institute for Artificial Intelligence and the University of Montreal conducted the study.

Guillaume Dumas

William Dumas. Credit: Stéphane Dedelis, Chu Sainte-Justine

The model, which appeared on the cover of the newspaper Proceedings of the National Academy of Sciences of the United States of America (PNAS), describes neural development on three hierarchical levels of information processing:

  • the first sensory-motor level explores how the brain’s internal activity learns patterns from perception and associates them with action;
  • the cognitive level examines how the brain contextually combines these patterns;
  • finally, the conscious level considers how the brain dissociates itself from the outside world and manipulates learned patterns (via memory) that are no longer accessible to perception.

The model’s focus on the interplay between two fundamental types of learning – Hebbian learning, associated with statistical regularity (i.e. with reward and the dopaminergic neurotransmitter), provides insight into the fundamental mechanisms underlying the cognition.

The model solves three tasks of increasing complexity at all these levels, from visual recognition to the cognitive manipulation of conscious percepts. Each time, the team introduced a new core mechanic to allow it to progress.

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

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

“Our model demonstrates how neuro-AI convergence highlights the biological mechanisms and cognitive architectures that can fuel the development of the next generation of artificial intelligence and even ultimately lead to artificial consciousness,” said Guillaume Dumas, member of the team, assistant professor of computational psychiatry at the University of Montreal and principal investigator at the CHU Sainte-Justine Research Center.

Reaching this stage may require integrating the social dimension of cognition, he added. Researchers are now seeking to integrate the biological and social dimensions at play in human cognition. The team has already launched the first simulation of two interacting whole brains.

Grounding future computational models in biological and social realities will not only continue to shed light on the fundamental mechanisms underlying cognition, the team says, but will also help provide a unique bridge to artificial intelligence towards the only known system endowed with an advanced social consciousness: the human. brain.

Reference: “Multilevel development of cognitive abilities in an artificial neural network” by Konstantin Volzhenin, Jean-Pierre Changeux and Guillaume Dumas, September 19, 2022, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2201304119


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