Researchers have developed a new neurocomputational model that could lead to advanced neural artificial intelligence research.
Brain models are required to understand the details in the neural activity. These neural activities are necessary to understand as they are used to develop a computational network for creating an artificial intelligence system. Computational neuroscience bridges the gap between human intelligence and AI by creating theoretical models of the human brain for interdisciplinary studies on its functions, including vision, motion, sensory control, and learning.
The study that 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 model’s emphasis on the interaction between two fundamental types of learning—Hebbian learning, associated with statistical regularity (i.e., repetition) and reinforcement learning, associated with reward and the dopamine neurotransmitter, provides insights into the fundamental mechanisms underlying cognition. The model solves three tasks from visual recognition to cognitive manipulation of conscious percepts. With increasing complexity, researchers 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.
“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,” said team member Guillaume Dumas, an assistant professor of computational psychiatry at the University of Montreal, and a principal investigator at the CHU Sainte-Justine Research Centre.
Reference: “Multilevel development of cognitive abilities in an artificial neural network” by Konstantin Volzhenin, Jean-Pierre Changeux and Guillaume Dumas, 19 September 2022, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2201304119
