How the brain develops: a new way to shed light on cognition

A new study introduces a new neurocomputational model of the human brain that could shed light on how the brain develops complex cognitive abilities and advance neural artificial intelligence research. Published Sept. 19, the study 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.

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

• the first sensorimotor level explores how the brain's inner activity learns patterns from perception and associates them with action;
• the cognitive level examines how the brain contextually combines those patterns;
• lastly, the conscious level considers how the brain dissociates from the outside world and manipulates learned patterns (via memory) no longer accessible to perception.

The team’s research gives clues into the core mechanisms underlying cognition thanks to the model’s focus on the interplay between two fundamental types of learning: Hebbian learning, which is associated with statistical regularity (i.e., repetition) – or as neuropsychologist Donald Hebb has put it, “neurons that fire together, wire together” – and reinforcement learning, associated with reward and the dopamine neurotransmitter.

The model solves three tasks of increasing complexity across those levels, from visual recognition to cognitive manipulation of conscious percepts. Each time, the team introduced a new core mechanism to enable 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 balanced excitatory/inhibitory ratio of neurons.