The brain uses memory Lego to create new behavioral models.

“Princeton researchers found that a primate’s prefrontal cortex reuses modular “cognitive Legos” to solve related tasks, giving biological brains a flexibility that AI still lacks. The insight could help improve AI systems so they retain old skills while learning new ones. Credit: Adapted by Dan Vahaba (Princeton University), from “Brain Silhouette 2” (Littleolred, CC0 1.0, freesvg.org) and “Lego bricks” (Benjamin D. Esham, CC BY-SA 4.0, Wikimedia Commons).” (ScitechDaily, Your Brain Has a Learning Shortcut AI Can’t Copy)

Behavior is the reaction that we see from the outside. And. When an actor notices something, that something acts as a trigger. The trigger launches. A certain behavioral reaction. When we see our friends, we can say “hi”. Or, when we slip on the ice, we put our hands in a certain position, that protects our head from impact with the ground. This means that. When we see something. Hear something, or feel something that causes a reaction. Does something cause a reaction, and what type of reaction is? That depends on the memory blocks connected to that sense. If. There is no memory connection with sense. That doesn’t launch a reaction that we see as behavior. 

The ability to use complete building blocks to create new behavioral models makes human brains more effective than any AI. The AI cannot mimic that ability. Each memory block is like Lego. And. It gives flexibility and effectiveness. To handle memories. And especially the behavior or reflexes. Those are connected with those memories. 

The idea is that there is so-called macro behavior. Behavior is the thing. That. The memory block activates. The modular structure of memories. And actions. What we see from the outside gives humans incredible flexibility. The macro-behavior is like a puzzle, an entirety that includes multiple smaller bits.  Each macro-behavior module can act as a module for larger-scale macro puzzles. 

This means that the human brain handles behavior like a set of modules, which can connect and reconnect with other modules. If we think that. The human brain includes about 100 billion neurons. And each of them has one memory unit. That means there are 100 billion memory units. That gives very high flexibility and morphing ability. For those. Memory structures. The AI cannot mimic those things because that requires the ability to handle so many memory units simultaneously. 

The ability to create new behavioral modules makes human brains effective. The ability to interconnect those modules is unique. One of the reasons. The reason this thing is so unique is that the neuron is not passive. The neuron knows what kind of data it can handle. So it can tell other neurons. That. It's a neuron that processes signals that come from the eye. This means that the neuron that transmits a signal from the retina can ask the routing for data to the neuron that can handle that vision information. 

This saves energy and time because the signal doesn’t disturb other neurons. The transmitter neuron connects a mark. Like a serial number, to the data packet. This information includes data from the transmitter neuron. And if the route that neurons select is wrong. And the receiver neuron cannot handle that information. It can send that information back to the transmitter. Then the routing neurons can ask where the right receiver is. 

https://scitechdaily.com/your-brain-has-a-learning-shortcut-ai-cant-copy/

AI mimics human memory to make wise decisions.

"A recent study introduces the M2I framework, inspired by human memory, to address limitations in current large AI models such as inefficiency, high energy use, and lack of reasoning. By mimicking brain-like memory mechanisms, the research aims to create machines capable of continual learning, adaptive reasoning, and dynamic information processing." (ScitechDaily, Can AI Think Like Us? New Research Mimics Human Memory for Smarter Machines)

Human memory is like a puzzle. Every single memory unit holds one small part of the information. That the brain stores in it. The thing that makes the memory so flexible is that. There are millions of memory units. That brain can reshape and connect in millions of ways. It is possible to make this kind of memory for computers. Using the memory addresses. In those systems, we can say that memory allocation units behave like pixels in the CCD camera. 

The memory pixels that the AI can reshape and reorder make those systems powerful and flexible. But handling that memory pixel network entirety of the system requires lots of computer power. And advanced operating systems. The memory is more than image. It controls the physical and emotional reactions. When a thing like a robot carries packages the memory stores information on how it picks the merchandise up. 

A robot's memory orders it to move its limbs and react to things that the robot faces. When a robot sees something it searches match from its memory. If there is a match with even and description that description activates the action. 

Memory addresses can act like memory units or memory cells in human brains. Then, those memory addresses can make multiple connections of themselves. They can make millions of forms of the memories that the computers have. This kind of memory is like a CCD chip in the digital camera. The difference between the computer memory and the CCD chip is that the CCD chip turns the image into electric impulses. 

But, this network-based memory handles data stored in the system. The system's accuracy and flexibility depend on the memory unit or memory address's size and number of those addresses. The large number of small memory addresses or memory allocation units can create a more accurate and flexible memory entirety than the small number of large allocation units. 

When the system reshapes the image or puts those memory storages into a new order the small number of large memory allocation units makes the new memory image unsharp. The data that a large memory allocation unit stores is in fixed form. The problem with those data structures is the same as when we use a small number of large pixels in the CCD camera. 

That makes images unclear. The large number of small pixels makes those images clear. The same way the memory pixels act in the computer memory. AI thinks similar way as humans. It collects a group of data pixels and then reshapes them into a new form. 

https://scitechdaily.com/can-ai-think-like-us-new-research-mimics-human-memory-for-smarter-machines/