Experts explain, in case you know anyone who bought that line:
Yuri Danilov: Again, it is a separate discussion, extremely painful for many but it is something that is happening right now. Remember, I talked today about our technological development morphing how our understanding of the brain works. And the attempt to make a parallel between the brain and a computer is a result of our evolution, if you wish. Because… in the Seventies … it was a transistor and everybody thought it was very simple. They thought that each neuron is a transistor.
Robert J. Marks: Yes.
Yuri Danilov: Then it was, “Each neuron is a microchip.”
Robert J. Marks: Yes.
Yuri Danilov: Then each neuron is a microprocessor.
Robert J. Marks: Yes.
Yuri Danilov: Right now people are saying, each synoptical connection is a microprocessor. So if it’s a microprocessor, you have 1012 neurons, each neuron has 105 synapses, so you have … you can compute how many parallel processing units you have in the brain if each synapse is a microprocessor.
But as soon as you assume that each neuron is a microprocessor, you assume that there is a programmer. There is no programmer in the brain; there are no algorithms in the brain.“Why the brain is not at all like a computer” at Mind Matters News
Way more to come.
Seeing the brain as a computer is an easy misconception rather than an informative image.
Here are excerpts and links from the two earlier podcasts featuring Robert J. Marks and Yuri Danilov:
Do we actually remember everything? Neuroscience evidence suggests that our real problem isn’t with remembering things but finding our memories when we need them. One of a pioneer neurosurgeon’s cases featured a patient who could, unaccountably, speak ancient Greek. The explanation was not occult but it was surely remarkable for what it shows about memory.
Aging brains need exercise, not sofas for neurons. Biomedical engineer Yuri Danilov reassures seniors, we do not lose neurons as we age. (This is Part 1 of Yuri Danilo’s discussion with Robert J. Marks.)
Further reading on neuroplasticity and the realistic hope for the healing of brain injuries:
How the Injured Brain Heals Itself: Our Amazing Neuroplasticity Jonathan Sackier is a pioneer in non-invasive techniques for speeding the healing of traumatic brain injuries
If Thinking Can Heal, Why Do We Need Antidepressants? J.P. Moreland, who struggles with anxiety disorders, likens medications to engine oil for the brain
Mind-controlled robot brain needs no brain implant
The placebo effect is real, not a trick. But the fact that the mind acts on the body troubles materialists. Such facts, they say, require revision
One Reply to “Why the “computer” model of the human brain fails”
It appears that the human brain has approximately 10**12 neurons, where averaged out each neuron has approximately 10**5 synapses. Using the “brain is a computer” paradigm each synaptical connection is apparently now thought to actually be equivalent to a microprocessor in complexity. So, the number of equivalent microprocessors in the brain is approximately 10**17, approaching a billion billion.
As of 2017, the largest transistor count in a commercially available single-chip microprocessor was 19.2 billion, (1.9×10**10). As of 2018 the physically smallest microprocessor (smaller than a grain of sand) had 100,000 (10**5) transistors.
Therefore the number of equivalent logic circuit transistors in the brain is somewhere between 10**22 and 1.9×10**27. Between ten thousand billion billion and 1.9 billion billion billion.
Good luck to the researchers who are trying to build artificial simulations of the brain completely emulating the complexity of its logic. This would mean down to the basic digital logic unit level. Obviously just building it to the synapse level (assuming as they used to that each synapse is a single logic gate) would be totally inadequate. Simulating the entire complexity of the brain is totally and completely impractical for any foreseeable technology.
Of course, there are other even stronger reasons why such enterprises are futile, having to do with the fundamental nonequivalency of the brain to computer processors. For instance, as pointed out by the expert interviewee, as soon as you assume that each neuron is a microprocessor, you assume that somehow it was programmed. And programs are fundamentally algorithms. But there is no programmer. Accordingly there are no algorithms (as we understand them) in the brain. Whatever processing there is, is unknown. But the dilemma is then that even non-algorithmic programs also require a programmer. Very basically, this boils down to that we just don’t have any understanding of how the brain really works.
Then there is also the problem of the origin of such masses of very extremely complex specified information, with the only candidate of materialism being the blind purposeless semi-random Darwinian processes.