Research laboratories around the world have sought the location of human memory.
The research followed several leads – a lead-related to the branched inputs of nerve cells, called dendrites.
The growth of the branch was aided by a protein called cypin.
Some memory deficiencies have been linked to deficits in cypin. Therefore, one possibility was that nerve cells developed new branches to store memory.
New additions may represent more memory. But, human memory is immense.
When the size and scale of human memory were considered, the idea of branches, though microscopic, growing to add memories sounded dangerously cancerous.
LTP was another possibility.
High-frequency stimulation of a neuron’s dendrites was known to improve the sensitivity of synaptic nerve junctions.
This activity was seen as “retained” by the cell through greater sensitivity to specific entries. Neurochemicals in synaptic connections were also known to increase this sensitivity.
But, while the process increased memory, LTP failed to offer a global hypothesis about how memory could be stored. The hippocampus has also been mentioned in connection with memory research.
Damage to this organ, a component of a region of the brain called the limbic system, has been known to make patients forget about events in progress within seconds. But incidents from childhood and early adulthood were still remembered.
The memory had disappeared a few years before the event that caused damage to the hippocampus.
The patient still retained older memories, even without the hippocampus.
The organ did not store these memories. It could play a role, but the actual storage of representation remained enigmatic.
In the end, all science knew was that memory resided in the entire system and that a particular organ helped in the formation of memories. However, the answer to the retention puzzle had faced them for years.
This happened when science recognized the use of combinatorial coding by nerve cells in the olfactory system. Combinatorial coding seemed confusing and complicated.
But, in the context of nerve cells, combinatorial coding only means that a nerve cell acknowledges combinations.
It was combinatorial coding, which allowed nerve cells in the reptilian brains to recognize odors and make crucial decisions in life from the beginning of history.
This sensory power was developed in animals to a remarkable degree. Research has shown that dogs can record fragrance parameters and then distinguish between millions of competing smells.
The animals were able to detect a human scent on a glass slide that received a slight fingerprint and was left outdoors for up to two weeks.
They could quickly sniff out a person’s footprints and accurately determine which way the person was walking.
The animal’s nose could detect the relative difference in odor intensity between prints just a few feet away, to determine the direction of a trail.
Registering and recognizing ABD and DEF allowed animals to record and remember a single smell to differentiate it from millions of other scents.
Memories inherited from millions of scents decided whether the food was edible or not, or whether a trail was fatal.
The system had newly recorded and inherited memories, which allowed them to recognize scents in the environment.
Although these remarkable odor recognition skills have been known for a long time, it was only in the late 1990s that science discovered combinatorial coding.
A Nobel Prize was awarded for the discovery of the use of combinatorial coding by the olfactory system in 2004. The olfactory system used coding to allow a relatively small number of olfactory receptors to recognize different odors.
Science has found that specific combinations can fire to trigger recollection. In the experiment, the scientists reported that even small changes in the chemical structure activated different sequences of receptors.
But science failed to recognize the real meaning of combinatorial coding when it looked for the location of human memory.
If nerve cells remember combinations, then this could be the location of the galactic nervous system memory. Combinatorial coding can provide immense intelligence to the nervous system.
The wonder of nature was the enormous scale, scope, and sensitivity of its reporting systems.
The mind had this vast army of scouts, reporting millions of small sensations – the warmth of the sun and the hardness of the rock.
When your impulses were received in the cortex, you were in pain. In the previous example, with combinatorial coding, a cell could fire for ABD and be inhibited for ABP.
If the nerve cell is reporting the pain recognized inputs from its neighbors, it could also respond to adjoining problems and fire to notice sympathetic pain. He could respond to the touch and inhibit his message of despair.
Therefore, pain can be context-sensitive. Memories inherited in combinatorial codes can allow the system to recognize and respond to patterns in context.
Combinatorial coding can explain the mind as a pattern recognition mechanism. But science worked on the assumption that neurons in the brain did not recognize, but did calculations.
The search for a mathematical formula that could simulate calculations of the mind continues. But, if you took on pattern recognition, you just came out of the mathematical maze.
Unfortunately, pattern recognition was a formidable task for computers. Disease diagnosis was a typical pattern recognition problem. The obstacle was that different diseases had many shared symptoms.
Each sign pointed to various diseases. In the usual search, the first disease selected with the first manifestation presented may not have the second trait.
Therefore, forward and backward research followed an exponential expansion path as the database grew in size. This made the process absurdly time-consuming – theoretically, even years when searching through extensive databases.
In light of such an impregnable problem, science has not evaluated pattern recognition as a practical process for the nervous system.
In a feat never achieved by computers before, AI could diagnose disease almost instantly. AI used elimination to restrict the possibilities of arriving at the correct answer. In essence, AI did not calculate but used elimination to recognize patterns.
AI acted with the speed of a simple recalculation in a spreadsheet, to recognize a disease, identify case law, or diagnose the problems of a complex machine.
He did this holistically and almost instantly, through logical and straightforward steps. AI proved that holistic, immediate, and real-time pattern recognition was effective. AI provided a clue to the secret of intuition.
The mind was a recognition machine, which instantly recognized the context of its continually changing environment. The system triggered awareness when specific classes of events were identified.
The process was reached by memories of inherited nerve cells accumulated over millions of years.
Memories allowed the mind to recognize events. Similar inherited memories in nerve cells helped the mind to trigger characteristics when the events were identified.
Besides, cellular memories caused disturbances to initiate actions. Actions were sequences of muscle movements. Even impulse sequences can be remembered by nerve cells.
That’s how we were motivated.
Then the circuit closed. Half a second for 100 billion nerve cells to use context to eliminate irrelevance and provide motor output. The time between the shadow and the scream.
Walter Freeman, the famous neurobiologist, defined the critical difficulty for science in understanding the mind.
“Cognitive guys think it’s just impossible to keep playing everything you have in computing every time. But that’s what the brain does. Consciousness is bringing your whole story to bear on your next step, your next breath, your next moment .”
The mind was holistic. He assessed all of his knowledge for the next activity.
However, because its database is extensive, AI logic could yield instant pattern recognition. Since this logic was robust and practical, intuition could also be an instant pattern recognition process.
Intuition could then enable the mind to instantly recognize an infinite variety of objects and events to trigger motor responses.
Each moment lived could assess the context of a dynamic multisensory world and its vast memories. These representations can be stored in the combinatorial codes of the nerve cells.
The Nobel Prize should not have been awarded for the discovery of combinatorial coding, but the discovery of human memory.
The evidence that his memory is trainable can be found in ancient studies and modern business. Recollection enhancement techniques have been around for thousands of years and are serving significant companies today.
These old memory systems were adapted for use in modern businesses today. Corporations, governments, and organizations have already started using these memory systems in their standard day-to-day work and business environments.
In today’s world, information is an essential product in the open market. Losing it or losing control over it can be the end of a business. Here is a list of some examples where memory systems can earn or save money.
Without memory resources, an executive spends about 30 minutes a day looking for things on his desk.
Many deals take place off the record, usually ending with a handshake. It can be disastrous for a business to forget an important point discussed during an agreement.
He builds relationships and builds connections with customers, remembering more about them.
Increases performance, comprehension, and productivity.
Knowing your results from the inside out gives you instant credibility. Successful companies already undoubtedly have qualified personalities for their jobs.
But professional skills and education are no guarantee of a good and reliable memory.
The skills of mindfulness techniques are not taught in school systems, not even at the college and university level.
A good memory may be possible as a result of developing specific skills in mnemonics, which is the actual science of memory.