The neuroscientists get a new tool, the 1400 terabyte model of human brains.

"Six layers of excitatory neurons color-coded by depth. Credit: Google Research and Lichtman Lab" (SciteechDaily, Harvard and Google Neuroscience Breakthrough: Intricately Detailed 1,400 Terabyte 3D Brain Map)

Harvard and Google created the first comprehensive model of human brains. The new computer model consists of 1400 terabytes of data. That thing would be the model. That consists comprehensive dataset about axons and their connections. And that model is the path to the new models or the human brain's digital twins. 

The digital twin of human brains can mean the AI-based digital model. That consists of data about the blood vessels and neural connections. However, the more advanced models can simulate electric and chemical interactions in the human brain. 

This project was impossible without AI. That can collect the dataset for that model. The human brain is one of the most complicated structures and interactions between neurotransmitters, axons, and the electrochemical interaction between neurons. Making a model of those interactions is an impressive achievement. 

The human brain's digital twin is a useful tool to research neural disorders and how to deal with that thing. The new comprehensive model of the human brain offers new tools for neurosurgeons in practice and simulations about the effect of certain operations and medical treatments, and the system can introduce what effect certain blood vessels have in the brain. 

The structure of the brain and neural connections make it possible that the brain never gets stuck. Even if one route is locked or stuck, brains can pass that point. And that means that even if we think of some problem, brains can still watch the environment. 

But the most interesting thing. That this system can make, is the artificial intelligence that acts like the human brain. The AI can create the needed number of databases and their connections. The modern AI can handle database structure with 200 billion connections. The number of human neurons is a little mystery because neurons can create virtual neurons using their connections. Those virtual neurons act like one neuron. And they can interact with their database. 

If researchers can model this system in the virtual entirety or software-based artificial intelligence on a large scale, that thing can create new types, of more effective AI. 

https://scitechdaily.com/harvard-and-google-neuroscience-breakthrough-intricately-detailed-1400-terabyte-3d-brain-map/

New research tells why mice are so teachable.

 

"Scientists discovered that mice display strategic behavior in learning tasks by engaging in exploratory actions that initially appear as mistakes. Through experiments, the study showed that mice test hypotheses and adjust their strategies based on the outcomes, challenging the traditional view of animal errors as mere mistakes. This insight into animal cognition not only sheds light on how mice think but also draws parallels with nonverbal human learning, paving the way for further studies on the neural basis of strategic thinking. Credit: SciTechDaily.com" (ScitechDaily, Challenging Our Views of Cognition – New Johns Hopkins Test Reveals That Mice Think Like Babies)

New research tells us that mice think like babies. This thing makes us understand why rodents are so maddening. And why they successfully avoid things like traps. That thing makes rodents so successful and harmful. This new research opens a new view of cognition. 

Rodent's brains are not very big. And they are a good example of animals that should not be successful. But their way of using their brains is effective. The effective way to think determines the success of the individual. 

The reason why children's cognitive actions are so effective is that their brains are like empty paper. And everything that they do is new. Because they have no memories their brains have fewer memory blocks or databases. They must connect, and learning means the ability to interconnect databases. In children's brains, there is more free space in memory cells, neurons must not spend as much time finding space from memory cells. 

New EEG systems. Along with AI allows the collection of information from large populations. The neuro-implated microchips allow researchers to observe neural functions deeper in brains. During those missions, the system can observe animals and humans in the natural environment. 

The new systems can also decode the EEG. And maybe, quite soon, R&D teams can see memories from computer screens. The knowledge about brain functions tells about the advancement of thinking. And why children are so productive and creative. But when we turn into adults. We lose our productivity somewhere. 

That is one of the most interesting ways to model the thinking process. The number of neurons is not the only thing that is necessary for successful thinking. Successful operation is conducted from successful thinking. We can say that thinking is the theoretical prediction for physical actions. 

The thing. What determines the success of the thinking process, is if it benefits the species. Babies think otherways than adults. They are more curious and more unprejudiced than adults. And they will test and try things more often than adults. For babies, every day is full of new things. And that thing is making babies successful in learning things. 

The same thing makes mice and probably rats sometimes maddening. Those clever rodents learn how to void traps. And that makes them successful learners. The same models can be used to make intelligent robots to learn things. The robot can try something alone, and then it can estimate if its model is successful. In those cases, there are parameters, that determine if the system has successfully done its missions. 

https://scitechdaily.com/challenging-our-views-of-cognition-new-johns-hopkins-test-reveals-that-mice-think-like-babies/