🚀 go-pugleaf

Thanks for your reply. 


Louis Savain wrote:
> 

> 
> >1-firing a neuron strengthens its links
> 
>   I have to disagree with this......
I understood this to be true, but it is not critical to the scheme. 

> 
> >4-once fired, the link is temperorerily boosted(its weighting is
> >increased), this decays quite fast. I term this increase 'boost', for
> >want of a better term.
> 
>   I'm not sure what you mean by the fast decay part.  Again here, I
> would disagree that the simple firing of a neuron is enough to
> strengthen its synapses.  The neurophysiological evidence says
> otherwise.  We interpret this as part of the natural method used by
> the nervous system to weed out non-associated synapses.
Let me clarify. 
A link has a strength, or weight (as per usual). In this scheme, the
links strength, can be thought of as the summation of 2 parts:
-the weight: This changes slowly, in response to use, as per Hebbian
learning. The weight degrades slowly.
-the 'boost': This changes quickly w.r.t. the weight. Once fired, a
synapses 'boost' increases, this degrades fast w.r.t. the weight. This
could be thought of in terms of increasing the neurotransmitter levels
in respose to excitation.

.........

> What is more important to
> us is spike train initiation (ON) and cessation (OFF).  
Ok. 

> >ear --> |---|---------------|---|
> >eye --> |-A-|    brain      |-B-| --> outputs
> >        |---|---------------|---|
> >
> >A = input basic nervous system.
> >B = output basic nervous system.
> >
> >The job of A is to simplify the environement, to amplify 'good' input(
> >or reduce 'bad' input).
> >The job of B is to simplify the brain outputs, and to provide multiple
> >paths to the actual outputs.


>   Signal propagation speed is important but not crucial.  What is
> crucial is the temporal relationship between signals.
The reason I include signal propogation speed is because, I believe, it
important for convergense in this scheme. I include it such that signal
propogation in my net will progress in a wave. I do not want a disparate
neurone ( in terms of its position in 3d space.. or nD space) to fire
instataniously, just because it is topographically 1 away.

>   This is the straight Hebbian adaptive mechanism.  Unless a temporal
> factor is added, not much will be learned by this.
> 
.....
>   Sure.  But you need a strong causal ordering mechanism for
> correlations to be useful or even meaningful.  Actions (output effects
> or behavior) will depend on conditions.  Conditions are the sufficient
> causes of effects.  Unless the network is designed to deal with causes
> and effects from the start, not much will be learned.
> 
yes it is. The temporal factor is supplied by my sinchronised inputs, my
boosted paths, and my basic nervous system. A great deal of work will
have to be done to establish the correct basic nervous system, and the
brain topology to promote the desired behaviour.


> 
> >Note, now we can corralate inputs and produce some outputs based on that
> >corralation. This is still not very usefull as the outputs are random.
> >If I add a further 'sense'. This equates to 'right' and 'wrong'. It
> >fires more when the current situation is 'good'.
> 
>   Hmm.  'Sense' is vague.  This is getting into unchartered territory.
> IMO, "good or bad" is decided at the single neuron level.  In a causal
> system, there will be conflicts that will cause connections at
> multiple competing neurons to simultaneously weaken.  The severing of
> one or more connections usually ends the conflicts.  The winners are
> the strongest connections.
> 


i.e.
ear --> |---------------|
eye --> |    brain      | --> outputs
good--> |---------------|

 12- from 11: I should now produce outputs that tend to corralate, and
maximise my inputs. Outputs that cause my good sense to fire more will
increase the good sense, and therefore become prevalent. 'bad' behaviour
is not removed, it is superceeded by 'good' behaviour.
 
>   This is getting into even more uncharted territory.
One of the purposes of this posting is to see if I am stating the
blatently obvious.... or to see if there is something here worth
investigating.

> 
> >But, what is 'good'?, and how does it apply?
> >Now, if I remove the good sense, and replace it with a basic set of
> >drives. This equates to a very basic, low level, behaviour. This equates
> >to our basic nervious system.
> >e.g.
> >
> >ear --> |---|---------------|---|
> >eye --> |-A-|    brain      |-B-| --> outputs
> >        |---|---------------|---|
> >
> >A = input basic nervous system.
> >B = output basic nervous system.
> >
> >The job of A is to simplify the environement, to amplify 'good' input(
> >or reduce 'bad' input).
> >The job of B is to simplify the brain outputs, and to provide multiple
> >paths to the actual outputs.
> >
> >The initial basic drive could be 'closeness to parent = good'. And, in
> >my simple creature, this could be represented by my 2 sense organs as
> >'red' and 'noisy'.
> >The basic nervous system, upon encountering 'red' or 'noisy' will
> >amplify that data, or reduce input that is not 'red' or 'noisy'.
> >
> > 13- from 12: 'good' imputs, i.e. the strong inputs, are likely to
> >produce the initial structures, and will stimulate the brain more.
> > 14- from 13,6,8: Thus 'good', corralated behaviour is likely to result.
> >Outputs that enhance 'good' behavoiur will be prevalent, the net will
> >have learned to solve the situation encoded in A and B.
> 
>   This is what David Longley would call "intensional" stuff and I
> would agree.  The best thing to do in situations where speculation is
> beginning to get out of control is to run a set of experiments.
> 
That is what I intend to do... I just wanted to see if I was
re-inventing the wheel...

 
> >16- from 4: Using a thought, gears up the mind for thinking that way.
> >So, for example, the brain can gear itself up to spotting patterns, like
> >seeing a computer game when your eyes are closed.
> 
>   I have no idea what this means.
This is quite important. 
If my clarification of 'boosted' links in this response makes sense
then:
-Input will cause a certian cascade firing pattern.
-Thus, that will 'boost' those pathways.
-Thus, on recieving the next input , the neural firing is more likely to
progress along the 'boosted' pathways than it would otherwise. 
-Thus I can corralate between temorally distinct inputs.
-This is what I ment by an Idea Mesh, and the 'current thought'(agreed
its a fuzzy term, but I hope you understand what I mean by it) 

 
> >20- from 19: It is possible that this process is important, so that an
> >important idea can dominate, so a solution can be found.
> 
>   OK.  We believe that attention focusing is indeed important but only
> in large systems and focusing on the right stuff can be learned
> through conditioning.
The idea mesh in this scheme is equivalent to one form of attention
focusing. I do not dissalow loops ( as per Hebb), but I think the idea
of boosted links is much more robust, and usefull, on the small scale.

> 
> >21- from 19,20: 'stressed' neurones = overloaded boosted links =
> >physical(chemical) exhaustion. The increased long term link between
> >neurones means gets stressed less, under the same load.
> 
>   I don't know what this means.
This is speculation. In this I was proposing that a weak link will take
longer to degrade its boost. This may have important concequences for
initial learning and establishing of synapses.

> 
> >22- from 8,9,10,11: If there is a structure that is regularly
> >stimulated, like 'red'. Such structures could be regarded as inputs to
> >the rest of the brain. Thus corralation between these can prevail. Thus
> >complex, derrived, behavoiur can be produced.
> 
>   I don't know what this means.  The same association mechanism should
> take care of all correlations...........
> 
Yes, it is the same mechanism... I was merely stating an obvious
attribute of this deep network.

> 
>   OK.  What you have described is mostly the traditional Hebbian form
> of learning. ....
Yes, exactly. I am just applying it on the large scale.


> Regards,
> 
> Louis Savain
> 
> President, Marengo Media, Inc.

What I have been describing is a method for teaching a very deep neural
net using Hebbian learning. My introduction of the 'boost' is something
I believe happens (this has been confirmed to me after discussions with
various research neuro-scientists), and I believe it has important
concequences for learning, some of which I have outlined here. 
In this scheme, the basic nervous system filters the inputs and
feedback, and this teaches the net. They effectively modify the inputs
to provide the training sets. They need not be static, and can therefore
change with time to affectively alter the training data when the desired
initial behaviour has been established. This, again, has biological
precident.
From what I have gathered from the morsels of information you have been
releasing about your scheme, your description of the neurone and synapse
should apply well to this scheme. I would be inturested to hear your
oppinion on this.

If you follow my reasoning so far, then the obvious problems to be
solved are:
-Producing the basic nervous system for a given scenario. (this I see as
the hardest problem.)
-Designing the brain topology, to promote the desired structures,
behaviours and to take advantage of the inputs.

As I have previously stated here; My primary purpose in posting this is
to try and establish whether this approach is new, or worth
investigating. I have read most of the Neuro-science, Neural-net, and AI
journals(more specifically for the past 3 years), and not found this
approach anywhere. But this does not mean that it is not well
established, and had been previously discounted. 
I do make ambitious claims for this approach, and yes, I am excited by
the possibilites it presents (both in terms of Neural-nets, and in terms
of explaining biological mechanisms).
I need to properly research the scheme, both in terms of further
research and in terms of experimentation and demonstration. BUT, I
wanted to make sure I wasnt re- inventing the wheel before commiting
myself on such an undertaking.
 Many thanks for your constructive criticisms,

Howard Snart