» Six reasons why Progressive Learning in Synthetic Neuro Anatomy is a sound theory of brain development.

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Six reasons why Progressive Learning in Synthetic Neuro Anatomy is a sound theory of brain development.

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1/ Biological neural cells are covered in thousands of synapses, synapses are memory. Therefore memory is distributed throughout the brain. The values in synapses are not there by coincidence.

The human brain consists of many sections. We don’t have just one brain, but many modules that function more or less independent of each other. The cortex is a thin, wrinkled layer that shows on the outside of the brain. It is only a few millimeters thick and it has a homogeneous structure. Yet, different parts of the cortex perform different functions. The cortex contains the visual cortex, the auditory cortex, the motor cortex, the prefrontal cortex and many other areas that perform functions that are vastly different from each other. Yet, if we put those sections under a microscope we see the same structures of interconnected neural cells.

2/ Neural structures look the same everywhere in the brain, in visual processing, speech processing and in the motor cortex.

The only way that the same structure can perform two or more different functions is when an overlaying layer of ‘programming’ takes place. A computer can perform many different functions. The structure and the hardware is the same, yet its functions vary between calculating a spreadsheet to controlling an aileron in an aircraft. It is the same in the brain; cells are ‘programmed’ through values in synapses. The mechanism that creates these values is STDP-BCM learning. Synaptic Time Dependent Plasticity is a feedback timing mechanism that exists in neural cells and which works the same way in the Synthetic Neuro-Anatomy chip. Specific patterns that are relevant to an image or a sound are matched. Each repetition of a specific pattern causes the values that are typical of that pattern to be reinforced.

3/ Neuron physiology supports the theory. Neural cells do not have microprocessors but work by rules that are well defined in Neuro-physiology

Neuro-physiology documents the mechanisms of the cells in the brain. Some cells have as many as 200,000 synapses. The cell behaves like a signal processor, and the more memory a cell has, the more complex the waveform patterns are that it can recognize. A feedback path from the post-synaptic cell increases or decreases the values in synaptic registers to make the cell perform its recognition function. Feedback drives this learning function in well-documented ways. Cells learn by repetition and by intensity. The brain is not a control centre, but a huge collection of neural cells that are structured to learn to control the body. This learning does not stop until the organism dies.

4/ When tested in a FPGA chip, the artificial neurons acquired a function by learning. In the same way that neurons in the brain acquire a function by learning.

The functions of ten neural cells were emulated with a high level of precision in a Field Programmable Gate Array chip. FPGA chips are used to test chip designs before the final design is produced. In this way the theory was put to the test. The chip was exposed to sounds from a signal generator . The output of the signal generator was connected to an artificial cochlea (a spectrum analyzer) and the device quickly learned to recognize ten sounds. It recognized the same ten training patterns when it was exposed to human speech. It did this without any programming or human intervention, proving that the technology works. This provides an example of the innate knowledge that is provided by DNA. Simple, low level knowledge allows the neurons to learn more complex tasks, but sufficient synapses need to be available to accommodate that knowledge.

5/ DNA builds a framework for learning. It primes cells to acquire function from the sensory organ that they directly or indirectly connect to.
DNA primes the values in synaptic registers to make learning possible. DNA does not contain enough information ‘bits’ to program all of the brain, so only a rudimentary framework is put into place at birth. When experimenting with the FPGA chip that contains 10 neurons and 240 synapses, it became obvious that some priming is necessary. When the registers start out with random values learning takes place in a haphazard manner, but when the registers are primed with cascading numbers learning is quicker and more organized.
6/ When a device performs the same function as a biological cell, and that device is connected in the same manner as the biological cells are, then the circuit will perform the same function
Emulation is a process of copying the function of one system on another system. The greater the differences between the two systems, the more difficult it is to perform this function and the greater the complexity is of the emulation program. The brain’s architecture is totally different of a computer. But the Synthetic neuro-anatomy is a digital technology that is designed to emulate the function of a neural cell to a very high degree of accuracy. Putting 10,000 of such cells on a a chip, interconnected in the same way as biological cells, will result in a device that works exactly like the biological model.

Conclusion
No other theory of general brain function makes sense. Those who profess that the brain is hard-wired to perform control functions can not explain how this takes place or how the brain works and ignore learning completely. They waste hundreds millions of research dollars. This ‘institutionalized’ thinking is prevalent in organizations that are stuck in tradition. This is the big. well established dragon that we have to fight.

Ref 1. “Regional differences in synaptogenesis in human cerebral cortex”, Peter R. Huttenlocher*, Arun S. Dabholkar. Journal of Comparative Neurology, Volume 387, Issue 2, pages 167–178, 20 October 1997

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