What is it about?
A study published on September 19 in the journal Proceedings of the National Academy of Sciences of the United States of America (PNAS) 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. 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 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; then the cognitive level examines how the brain contextually combines those patterns; and finally, the conscious level considers how the brain dissociates from the outside world and manipulates learned patterns (via memory) that are no longer accessible to perception.
Photo by DeepMind on Unsplash
Why is it important?
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 coined, “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 further. The results highlight two fundamental mechanisms for the multilevel development of cognitive abilities in biological neural networks: 1) synaptic epigenesis, with Hebbian learning at the local scale and reinforcement learning at the global scale; and 2) self-organized dynamics, through spontaneous activity and balanced excitatory/inhibitory ratio of neurons.
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This page is a summary of: Multilevel development of cognitive abilities in an artificial neural network, Proceedings of the National Academy of Sciences, September 2022, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2201304119.
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