Connectionists: Weird beliefs about consciousness

[Juyang Weng]

Dear Tsvi,
You wrote " Nor does there seem to be cross interest within the academic
computational communities where this model can show top-down attention
effects and controls similar to Bayesian models  (with dynamic priors,
priming, and weights similar to likelihoods helping with explainability)
but is completely connectionist."

As far as I can see, in terms of top-down modeling, it is more an issue of
"lack of computational knowledge" than a lack of "cross interest".  The
landscape of AI should greatly change soon from my brain model of
conscious learning, sooner than the wave that Cresceptron created.   I hope
those who will use the DN brain model do not intentionally plagiarize DN as
many papers did to Cresceptron.

Best regards,
-John

On Thu, Feb 17, 2022 at 5:25 AM Tsvi Achler <achler at gmail.com> wrote:

> Hi Danko,
> Indeed there are so many top down effects based on what has been learned,
> which is further evidence that salience is inseparable from recognition.
> However as a note about education, I dont buy into defining
> especially STEM fields that need to be studied. If what is needed to be
> known is known, it wouldn't be science.
> My undergraduate degree is in (ahem) Electrical Engineering Computer
> Science.
> The electrical engineering provided useful tools through control theory to
> understand and manage the dynamics of top-down feedback.  This includes the
> mathematical tools to determine stability and steady state endpoints of
> dynamical systems.
>
> In the recognition model I developed, regulatory feedback networks, each
> input is regulated by the outputs it activates which subsequently creates a
> dynamical salience which makes sure inputs are not over or under
> represented within the network and lets the system evaluate all the inputs
> together.
>
> This model inherently displays the signal-to-noise salience phenomena that
> includes difficulty with similarity and asymmetry seen in humans even if
> the inputs are not spatial.
> It shows Excitation-Inhibition balance findings in neuroscience (Xue et al
> 2014) or what I call network-wide-bursting.
> It also does not require the iid rehearsal to learn like feedforward
> models because it uses the salience-feedback dynamics to determine the best
> relevance of each input given what has been learned and what is present at
> recognition time.  This dynamic salience is inseparable from the mechanism
> of recognition.
>
> Unfortunately I dont seem to be able to convince the academic cognitive
> psychology and neuroscience communities that while they can model any of
> their phenomena with enough parameters, models with huge parameter spaces
> are less desirable.  Scalability and minimizing degrees of freedom are
> important in models: models that show the most amount of phenomena
> (across multiple disciplines) with the least amount of free parameters are
> better.
> Neither do I seem able to convince the academic connectionist community
> that the iid rehearsal requirements are killing the ability to use models
> in a natural way and can be avoided using feedback back to the inputs
> during recognition.  Nor does there seem to be cross interest within the
> academic computational communities where this model can show top-down
> attention effects and controls similar to Bayesian models  (with dynamic
> priors, priming, and weights similar to likelihoods helping with
> explainability) but is completely connectionist.
> Sincerely,
> -Tsvi
> Xue, M, Atallah, BV, Scanziani, M. (2014). Equalizing
> excitation-inhibition ratios across visual cortical neurons. Nature 511,
> 596–600
>
> https://www.youtube.com/watch?v=F-GBIZoZ1mI&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=1
>
>
>
> On Thu, Feb 17, 2022 at 12:15 AM Danko Nikolic <danko.nikolic at gmail.com>
> wrote:
>
>>
>> Dear Juyang,
>>
>> You wrote "Senior people do not want to get a PhD in all 6 disciplines in
>> the attached figure: biology, neuroscience, psychology, computer science,
>> electrical engineering, mathematics."
>>
>> I would cross electrical engineering from that list. It seems to me that
>> the contribution of electrical engineering is minor. But then I would add
>> philosophy of mind and cybernetics. These two seem a lot more important
>> to acquire a PhD-level knowledge in.
>>
>> Best,
>>
>> Danko
>>
>> Dr. Danko Nikolić
>> www.danko-nikolic.com
>> https://www.linkedin.com/in/danko-nikolic/
>> --- A progress usually starts with an insight ---
>>
>>
>> On Thu, Feb 17, 2022 at 8:22 AM Juyang Weng <juyang.weng at gmail.com>
>> wrote:
>>
>>> Dear Tsvi:
>>>
>>> You wrote: "I believe scientists not seeing eye-to-eye with each other
>>> and other members of the community is in no small part due to these terms."
>>>
>>> I agree.  This is a HUGE problem, as the attached figure "Blind Men and
>>> an Elephant"  indicates.   What should this multidisciplinary community
>>> do?   Senior people do not want to get a PhD in all 6 disciplines in the
>>> attached figure: biology, neuroscience, psychology, computer science,
>>> electrical engineering, mathematics.
>>>
>>> Best regards,
>>> -John
>>>
>>> On Tue, Feb 15, 2022 at 10:00 PM Tsvi Achler <achler at gmail.com> wrote:
>>>
>>>> After studying the brain from a multidisciplinary perspective I am well
>>>> aware of the difficulties speaking and understanding each other across
>>>> disciplines.  There are many terms that are defined differently in
>>>> different fields... and unfortunately things are not as simple as looking
>>>> them up in a dictionary.
>>>>
>>>> For example the term recurrent connections have different meanings in
>>>> the computational neuroscience, neural networks, and cognitive psychology
>>>> communities.
>>>> In neural networks recurrent means an output used back as an input
>>>> within a paradigm of delayed inputs.  It is a method of representing time
>>>> or sequences.  Often recurrent connections in neural networks are confused
>>>> with feedback back to the same inputs which are actually never used in
>>>> neural networks because it forms an infinite loop and is not possible to
>>>> rewind in order to generate an error signal.
>>>> In computational neuroscience recurrent connections are used to
>>>> describe lateral connections.
>>>> In cognitive psychology the term re-entrant connections are used to
>>>> describe feedback back to the same inputs.
>>>>
>>>> I believe in order to truly appreciate "brain-like" ideas, members of
>>>> this group need to familiarize themselves with these brain-focused fields.
>>>>  For example in cognitive psychology there is a rich literature on salience
>>>> (which again is a bit different from salience in the neural network
>>>> community).  Salience is a dynamic process which determines how well a
>>>> certain input or input feature is processed. Salience changes in the brain
>>>> depending on what other inputs or features are concurrently present or what
>>>> the person is instructed to focus on.  There is very little appreciation,
>>>> integration or implementation of these findings in feedforward networks,
>>>> yet salience plays a factor in every recognition decision and modality
>>>> including smell and touch.
>>>>
>>>> Consciousness is a particularly problematic minefield which also adds
>>>> in philosophy, metaphysics and subjectivity into the mix.
>>>>
>>>> Juyang, I think we both agree about the basics: the need for more
>>>> realistic real world recognition and to move beyond the rehearsal
>>>> limitations of neural networks.  I believe scientists not seeing eye-to-eye
>>>> with each other and other members of the community is in no small part due
>>>> to these terms.
>>>>
>>>> Sincerely,
>>>> -Tsvi
>>>>
>>>>
>>>>
>>>> On Tue, Feb 15, 2022 at 9:54 AM Juyang Weng <juyang.weng at gmail.com>
>>>> wrote:
>>>>
>>>>> Dear Tsvi,
>>>>> You wrote "A huge part of the problem in any discussion about
>>>>> consciousness is there isn't even a clear definition of consciousness".
>>>>> Look at the 5 level definition of consciousness:
>>>>> https://www.merriam-webster.com/dictionary/consciousness
>>>>>
>>>>> You wrote: "So consciousness is not necessary or sufficient for
>>>>> complex thoughts or behavior."
>>>>> I was thinking that way too, until recently.
>>>>> I found consciousness IS REQUIRED for even learning basic
>>>>> intelligence.
>>>>> To put it in a short way so that people on this list can benefit:
>>>>> The motors (as context/actions) in the brain require consciousness in
>>>>> order to learn correctly in the physical world.   Please read the first
>>>>> model about conscious learning:
>>>>> J. Weng, "3D-to-2D-to-3D Conscious Learning", in Proc. IEEE 40th
>>>>> International Conference on Consumer Electronics, pp. 1-6, Las Vegas NV,
>>>>> USA, Jan.7-9, 2022. PDF file
>>>>> <http://www.cse.msu.edu/%7eweng/research/ConsciousLearning-ICCE-2022-rvsd-cite.pdf>
>>>>> .
>>>>>
>>>>> Best regards,
>>>>> -John
>>>>> ----
>>>>> From: Tsvi Achler <achler at gmail.com>
>>>>> To: Iam Palatnik <iam.palat at gmail.com>
>>>>> Cc: Connectionists <connectionists at cs.cmu.edu>
>>>>> Subject: Re: Connectionists: Weird beliefs about consciousness
>>>>>
>>>>> --
>>>>> Juyang (John) Weng
>>>>>
>>>>
>>>
>>> --
>>> Juyang (John) Weng
>>>
>>

-- 
Juyang (John) Weng
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