From: meekerdb meeke...@verizon.net


>> Although reductionism                          has recently received a lot 
>> of bad press from                          supermarket tabloids and new age 
>> gurus the                          fact remains that if you want to study    
>>                       something complex you've got to break it into          
>>                 simpler parts and then see how the parts fit                 
>>          together. And in the final analysis things                          
>> happen for a reason or they don't happen for a                          
>> reason; and if they did then it's                          deterministic and 
>> if they didn't then it's                          random.   

Perhaps your final analysis is a bit too shallow                        and 
self limiting. Why you cling so                        tenaciously to this need 
for definitive                        causality chains (or else it must be 
complete                        randomness) is amusing, but is not misguided.   
                     You cannot show definitive causality for most of           
             what goes on in most of the universe. You can                      
  hypothesize a causal relationship perhaps, but                        you 
cannot prove one for all manner of                        phenomenon arising 
out of chaotic systems. The                        brain is a noisy chaotic 
system and you are                        attempting to impose your Newtonian 
order on it. 

Your approach does not map well onto the                        problem domain. 
And what you say has no                        predictive value; it does not 
help unravel how                        the brain works... or how the mind 
arises within                         it.

>> It does help.  There's no evidence that the brain can't be    understood as 
>> a parallel computer plus some randomness.  The problem    with John's 
>> formulation is he insists there is either *a* reason or    not *a* reason.  
>> Hardly anything can be thought of as having *a*    reason.  In the case of 
>> human behavior, each instance almost    certainly has many different causes, 
>> some in memory, some in the    immediate environment, and some which are 
>> random and don't have an    effective cause.  I think of the person, 
>> brain/body/etc, plus    immediate environment narrow down the probable 
>> actions to a few,    e.g. 1 to 20, and then some quantum randomness realizes 
>> one of    those.  So it's not deterministic like Laplace's clockwork world,  
>>   but it's not anything-is-possible either.
Sure reductionist approach can gain you a partial understanding; you can slice 
the brain up; analyze processes and try to classify and drill down to smaller 
and down into increasingly tightly focused problem domains within the larger 
problem domain of how the brain works. But this approach fails to capture the 
holistic dynamic processes and subtle interplays between rapidly forming and 
also rapidly subsiding synchronized firing networks that pull together 
coalitions of neurons from many different brain regions. The brain is not only 
massively parallel -- it is a superbly tight packed one hundred trillion 
connection machine with 86 billion operating nodes in the network -- it is also 
incredibly noisy and seemingly chaotic.

The simple deterministic causality approach cannot model a vastly parallel and 
very noisy chaotic system such as the brain. The brain is not operating on 
deterministic principles -- or at least not completely so. Without modeling the 
chaos -- and chaos is modeled all the time and predictive statements can be 
made about chaotic systems (say the chaotic airflow over an air foil for 
example). But these models and the equations that comprise them account for 
chaos and often rely on probabilistic and consensus based algorithms. 
I am not arguing that the brain is beyond study or cannot be understood, 
analyzed or modeled. What I am arguing is that it is not a simple deterministic 
system in which state X will always lead to outcome Y; nor can it always be 
determined based on knowing an outcome Y in the brain what the causational 
state was that ultimately lead to that outcome. Even if there may be causation 
the processes by which the brain operates are so distributed and 
inter-dependent and the system is so incredibly noisy (and it really is a very 
high noise to signal ratio)  that any attempt to work backwards from some 
outcome down the causal chain of neural activity that resulted in it rapidly 
breaks down and grows geometrically more difficult with each remove from the 
final result and back into the densely nested forest of potential network 

John keeps insisting that X is Y or X is not Y. True, but so what? It does not 
provide any great insight into how the brain works as a dynamic entity. 
Basically based on reading his posts on the subject what I am stating is that 
he would not be hired to help work out the problem based on his views of how 
the brain can be understood. In fact he would not make it past the initial 
screening interview -- IMO. I am not calling him stupid -- though he does 
question my intelligence -- but for some reason (which I know not of) he clings 
to this simplistic view of what is in fact a highly dynamic, noisy, chaotic and 
vastly parallelized system.



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