There is a new 3D brain model that is open to conscious AI. This model is designed to better understand how the brain works and how it can be used to create conscious AI. The model is based on the idea that the human brain is capable of more than just simple thought processes.

Researchers from Mila and IVADO offer a new neurocomputational model of the human brain that might close the knowledge gap between the biological underpinnings of mental disorders and AI.

A fresh representation of the human brain

A new study proposes a new neurocomputational model of the human brain that could help researchers better understand how the brain creates sophisticated cognitive abilities.

  • The first sensorimotor level investigates how the brain learns patterns from perception and links them to action; 
  • the cognitive level looks at how the brain contextually combines those patterns; 
  • and finally, the conscious level investigates how the brain separates from the outside world and manipulates learned patterns (via memory) that are no longer accessible to perception.

The model’s focus

The model’s focus on the interaction between two fundamental forms of learning—Hebbian learning, linked to statistical regularity (i.e., repetition), or, in the words of neuropsychologist Donald Hebb, “neurons that fire together, wire together”—and reinforcement learning, linked to reward and the dopamine neurotransmitter, offers insights into the basic processes underlying cognition.

Three tasks with varying levels of complexity, ranging from visual identification to cognitive manipulation of conscious perceptions, are solved by the model. Every time, the team added a fresh central mechanism to help it advance.

The findings draw attention to two key pathways for the multilayer development of cognitive skills in biological neural networks:

 

  • Hebbian learning at the local level and reinforcement learning at the global level, 
  • along with spontaneous activity and a balanced excitatory/inhibitory ratio of neurons, all contribute to synaptic epigenesis.

According to 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, “our model illustrates 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.

The social aspect

He continued that incorporating the social aspect of cognition may be necessary to reach this milestone. The integration of biological and social factors influencing human cognition is a current research focus. 

The team believes that anchoring future computational models in biological and social realities will not only help provide a special bridge between artificial intelligence and the only known system with advanced social consciousness: the human brain, but will also help shed light on the fundamental mechanisms underlying cognition.

By Larry

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