Connectionists: how the brain works? (UNCLASSIFIED)

[Juyang Weng]

Dear Danko,

Thank you for your pointer which gave me an opportunity to browse your 
paper, especially the section 2.4.
If you like to get my feedback that you might like to take into account 
for your next stage work, here are two major ones:
(1) Your work is still qualitative, not quantitative.   This gap is 
wide, toward an experimentally verifiable theory.
(2) Fig. 2, A: your feed-forward computational model is incorrect, even 
though you have a comparator.

For any neuroscientists who are interested in modeling the brain, I 
suggest that he should first look into computer vision
systems that are at least quantitative.  There is a series of 
overwhelmingly challenging problems that computer vision
systems face, but they only touched on a small number of the problems.   
Unfortunately, the computer vision field basically gets lost
on its way in search for a general vision system.  The field seems to be 
hopeless because it does not seriously look into the brain.

When one can claim that his computational model autonomously learns 
general-purpose vision by directly learning for
a wide variety of invariance from cluttered scenes without 
pre-segmentation of the scenes (my proposed it as the first principle),
he is well on his way toward how the same vision system addresses many 
other brain problems. Vision was the field the
DN model was first verified on before the same model was used to address 
other brain functions, using basically the same
set of developmental principles.

-John

On 4/14/14 3:04 PM, Danko Nikolic wrote:
> Dear Jon,
>
> Please see my comments below.
>
>>
>> Much of your views are consistent to our DN model, an overarching 
>> model for developing brains.
>>
> I agree that there are some consistencies. Moreover, I agree that 
> development is important and cannot be neglected in any theory. 
> Development and learning cannot be dissociated because development is 
> just a way of learning.
>
>> > The system always adjusts--to everything(!)
>>
>> Yes, since the system does not know which is new and which is old. 
>> However, the amount of adjustment is different.
>> There is also a novelty system imbedded into the basic brain 
>> circuits, realized by neurotransmitters such as ACh and NE.
> I believe that this above is the description of your DN system, but it 
> is not a description of an anapoietic system. In an anapoietic system 
> the amount of adjustment corresponds to the degree to which the mental 
> contents (e.g., those of working memory) have changed. So, if in one 
> moment we think about vacation and then, the next moment we think 
> about chess, the change is large because the mental contents have 
> changed. The novelty of those contents does not matter so much. We may 
> be recalling familiar information about chess and still the change is 
> large. Novelty makes this process slower but largely does not 
> determine the amount of change during anapoiesis.
>
>>
>> > The only simple input-output mappings that take place are the 
>> sensory-motor loops that execute the actual behavior.
>>
>> Sorry, I do not quite agree. All sensory-motor loops that execute the 
>> actual behavior are not simple input-output mappings.
>> They affect all related brain representations, including perception, 
>> cognition and motivation, as the DN system implies.
> Again, I agree that this is what DN system does. However, I was 
> describing the properties of a practopoietic system that implements 
> anapoiesis. This system has different properties.
>>
>> > If the current goals of the system requires treating a slightly 
>> novel stimulus as new, it will be treated as "new". However, if a 
>> slight change in the stimulus features does not make a difference for 
>> the current goals and the situation, than the stimulus will be 
>> treated as "old".
>>
>> The brain does not seem to have an if-then-else circuit like your 
>> above statement seems to suggest. Regardless new or old,
>> the brain uses basically the same set of mechanisms. Only the outcome 
>> is always different.
>>
> There is no if-then. It is more accurate to say that, with anapoiesis, 
> these properties emerge.
>
>
>> > Importantly, practopoietic theory is not formulated in terms of 
>> neurons (inhibition, excitation, connections, changes of synaptic 
>> weights, etc.).
>>
>> Then, does it fall into the trap of symbolic representations? 
> No, no symbolics. It is more like cybernetics organized into a 
> creational (i.e., poietic) hierarchy.
>
>> How does the theory explain the development of various types of 
>> invariance?
> An anapoietic system only can learn in an invariant way. In fact, it 
> is very hard to learn things literally, much like non-invariant 
> learning is very hard for a human mind. The system must put a lot of 
> effort, a lot of learning, in order to reduce its invariance.
>
> Too understand why invariance is a natural property of anapoiesis, 
> please see the section 2.4 entitled "Practopoietic transcendence of 
> knowledge: Generality-­‐specificity hierarchy".
>
> Best regards,
>
> Danko
>
>

-- 
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
428 S Shaw Ln Rm 3115
Michigan State University
East Lansing, MI 48824 USA
Tel: 517-353-4388
Fax: 517-432-1061
Email: weng at cse.msu.edu
URL: http://www.cse.msu.edu/~weng/
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