Does plasticity imply local learning? And other questions

Asim Roy ataxr at IMAP1.ASU.EDU
Sun Mar 2 13:12:24 EST 1997

There is a continuing discussion on the above topic over several 
mailing lists. I thought neur-sci members might also want to 
participate in this discussion. So please post. I will compile all 
responses and post it to the participating mailing lists.


I thought it might be productive to the discussion if I compiled 
and posted the responses I have received so far. I am posting them 
any comments. I hope this will promote further discussion on this 
topic. The original posting is attached below for reference.

Response # 1:

Re: your note on plasticity and learning


As you mention, neuroscience tends to equate network plasticity 
with learning. Connectionists tend to do the same. However this 
raises a problem with biological systems because this conflates the 
processes of 
development and learning. Even the smartest organism starts from an 
egg, and develops for its entire lifespan - how do we distinguish 
changes are learnt, and which are due to development. No one would 
argue that we 
*learn* to have a cortex, for instance, even though it is due to 
emryological changes in the central nervous system of the animal.

This isn't a problem with artificial nets, because they do not 
usually have a true developmental process and so there can be no 
confusion between the two; but it has been a long-standing problem 
in the ethology 
literature, where learnt changes are contrasted with "innate" 
ones. A very interesting recent contribution to this debate is 
Ariew's "Innateness and Canalization", in Philosophy of Science 63 
(Proceedings), in which he identifies non-learnt changes as being 
due to canalised 
processes. Canalization was a concept developed by the biologist 
Waddington in 
the 40's to describe how many changes seem to have fixed end-goals 
that are robust against changes in the environment.

The relationship between development and learning was also 
thoroughly explored by Vygotsky (see collected works vol 1, pages 

I'd like to see what other sorts of responses you get,

Joe Faith <josephf at>

Evolutionary and Adaptive Systems Group,
School of Cognitive and Computing Sciences,
University of Sussex, UK.

Response # 2:

I fully agree with you, that local learning is not the one
and only ultimate approach - even though it results in very
good learning for some domains.

I am currently writing a paper on the competitive learning
paradigm. I am proposing, that this competition that occurs
e.g. within neurons should be called local competition. The
network as a whole gives a global common goal to these local
competitors and thus their competition must be regarded as
cooperation from a more global point of view.

There is a nice paper by Kenton Lynne that integrates the 
ideas of reinforcement and competition. When external 
evaluations are present, they can serve as teaching values,
if nor the neurons compete locally.

  author = 	 {K.J.\ Lynne},
  title = 	 {Competitive Reinforcement Learning},
  booktitle = 	 {Proceedings of the 5th International Conference 
                    Machine Learning},
  year = 	 {1988},
  publisher =      {Morgan Kaufmann},
  pages = 	 {188--199}

Best regards,
Christoph Herrmann
Christoph Herrmann                     Visiting researcher
Hokkaido University
Meme Media Laboratory
Kita 13 Nishi 8, Kita-          Tel: +81 - 11 - 706 - 7253
Sapporo 060                     Fax: +81 - 11 - 706 - 7808
Japan                      Email: chris at

Response #3:

I've just read your list of questions on local vs. global learning
mechanisms.  I think I'm sympathatic to the implications or
presuppositions of your questions but need to read them more 
carefully later.  Meanwhile, you might find very interesting a 
two-part article on such a mechanism by Peter G. Burton in the 1990 
of _Psychobiology_ 18(2).119-161 & 162-194.

Steve Chandler					
<chandler at>

Response #4:

A few years back, I wrote a review article on issues
of local versus global learning w.r.t. synaptic plasticity.
(Unfortunately, it has been "in press" for nearly 4 years). 
Below is an abstract. I can email the paper to you in TeX or 
postscript format, or mail you a copy, if you're interested.

Russell Anderson


"Biased Random-Walk Learning:
A Neurobiological Correlate to Trial-and-Error"
(In press: Progress in Neural Networks)

Russell W. Anderson
Smith-Kettlewell Eye Research Institute
2232 Webster Street
San Francisco, CA  94115
Office: (415) 561-1715
FAX:    (415) 561-1610
anderson at

Neural network models offer a theoretical testbed for
the study of learning at the cellular level.
The only experimentally verified learning rule,
Hebb's rule, is extremely limited in its ability
to train networks to perform complex tasks.
An identified cellular mechanism responsible for
Hebbian-type long-term potentiation, the NMDA receptor,
is highly versatile.  Its function and efficacy are
modulated by a wide variety of compounds and conditions
and are likely to be directed by non-local phenomena.
Furthermore, it has been demonstrated that NMDA receptors
are not essential for some types of learning.
We have shown that another neural network learning
rule, the chemotaxis algorithm, is theoretically much more powerful
than Hebb's rule and is consistent with experimental data.
A biased random-walk in synaptic weight space is
a learning rule immanent in nervous activity and
may account for some types of learning -- notably the
acquisition of skilled movement.

send request to rwa at

Slow mail:
Russell Anderson
2415 College Ave. #33
Berkeley, CA  94704

Response #5:

Asim Roy typed ...
> B) "Pure" local learning does not explain a number of other 
> activities that are part of the process of learning!! 
> So, in the whole, there are a "number of activities" that need to 
> be 
> performed before any kind of "local learning" can take place. 
> aforementioned learning activities "cannot" be performed by a 
> collection of "local learning" cells! There is more to the 
> of learning than simple local learning by individual cells. Many 
> learning "decisions/tasks" must precede actual training by "local 
> learners." A group of independent "local learners" simply cannot 
> start learning and be able to reproduce the learning 
> characteristics and processes of an "autonomous system" like the 
> brain.

I cannot see how you can prove the above statement (particularly 
the last sentence). Do you have any proof. By analogy, consider 
many insect colonies (bees, ants etc). No-one could claim that one 
the insects has a global view of what should happen in the colony. 
insect has its own purpose and goes about that purpose without 
knowing the 
global purpose of the colony. Yet an ants nest does get built, and 
the colony does survive. Similarly, it is difficult to claim that 
evolution has a master plan, order just seems to develop out of 

I am not claiming that one type of learning (local or global) is 
better than another, but I would like to see some evidence for your 
somewhat outrageous claims.

> Note that the global learning mechanism may actually be 
> with a collection of local learners!!

You seem to contradict yourself here. You first say that local 
learning cannot cope with many problems of learning, yet global 
learning can. You then say that global learning can be implemented 
local learners. This is like saying that you can implement things 
in C, 
that cannot be implemented in assembly!! It may be more convenient 
to implement 
it in C (or using global learning), but that doesn't make it 
impossible for assembly.


Brendan McCane, PhD.                      Email:  
mccane at
Comp.Sci. Dept., Otago University,        Phone:  +64 3 479 8588.
Box 56, Dunedin, New Zealand.             There's only one catch - 
Catch 22.

Response #6:

In regards to arguments against global learning:
I think no one seriously questions this possibility,
but think that global learning theories are currently
non-verifiable/ non-falsifyable.
Part of the point of my paper was that there ARE ways to
investigate non-local learning, but it requires changes
in current experimental protocols.

Anyway, good luck.
I look forward to seeing your compilation.


send request to rwa at

Slow mail:
Russell Anderson
2415 College Ave. #33
Berkeley, CA  94704

Response #7:

	I am sorry that it has taken so long for me to reply to 
your inquiry about plasticity and local/global learning.  As I 
mentioned in my first note to you, I am sympathetic to the view 
that learning 
involves some sort of overarching, global mechanism even though the 
actual information storage may consist of distributed patterns of 
information.  Because I am sympathetic to such a view, it makes it 
very difficult for me to try to imagine and anticipate the problems 
for such views.  That's why I am glad to see that you are 
trying to find people to point out possible problems; we need the 
	The Peter Burton articles that I have sent you describes 
exactly the kind of mechanism implied by your first question: Does 
plasticity imply local learning?  Burton describes a neurological 
mechanism by which local learning could emerge from a global 
Essentially he posits that whenever the new perceptual input being 
attended to at 
any given moment differs sufficiently from the record of previously 
recorded experiences to which that new input is being compared, the 
difference triggers a global "proceed-to-store" signal.  This 
signal creates a neural "snapshot" (my term, not Burton's) of the 
activations at that moment, a global episodic memory (subject to 
stimulus sampling 
effects, etc.).  Burton goes on to describe how discrete episodic 
memories could become associated with one another so as to give 
rise to 
schematic representations of percepts (personally I don't think 
that positing 
this abstraction step is necessary, but Burton does it).
	As neuroscientists sometimes note, while it is widely 
assumed that LTP/LTD are local learning mechanisms, the direct 
evidence for such a hypothesis is pretty slim at best.  Of course 
of of the most 
serious problems with that view is that the changes don't last very 
long and thus are not really good candidates for long term (i.e., 
long) memory. Now, to my mind, one of the most important 
overlooked in LTP studies (inherently so in all in vitro 
preparations and so 
far as I know--which is not very far because this is not my 
field--in the in 
vivo preparations that I have read about) is that LTP/D is either 
an artifact of the experiment or some sort of short term change 
requires a global signal to become consolidated into a long term 
Burton describes one such possible mechanism.
	Another motivation for some sort of global mechanism comes 
from the so-called 'binding problem' addressed especially by the 
Damasio's, but others too.  Somehow somewhere all the distributed 
pieces of information about what an orange is, for example, have to 
be tied 
together.  A number of studies of different sorts have demonstarted 
that such information is distributed throughout cortical areas.
	Burton distinguishes between "perceptual learning" 
requiring no external teacher (either locally or globally) and 
"conceptual learning", which may require the assistance of a 
'teacher'.  In his 
model though, both types of learning are activated by global 
"proceed-to-learn" signals triggered in turn by the global 
summation of local 
disparities between remembered episodes and current input.
	I'll just mention in closing that I am particularly 
interested in the empirical adequacy of neuropsychological accounts 
such as Burton's because I am very interested in "instance-based" 
"exemplar-based" models of learning.  In particular, Royal 
Skousen's _Analogical Modeling of Language_ (Kluwer, 1989) 
describes an explicit, 
mathematical model for predicting new behavior on analogy to 
instances stored in 
long term memory.  Burton's model suggests a possible neurological 
basis for 
such behavior.


Response #8:

	 Fred Wolf                      E-Mail: 
fred at
    Institut fuer Theor. Physik 
      Robert-Mayer-Str. 8               Tel:     069/798-23674
    D-60 054 Frankfurt/Main 11          Fax: (49) 69/798-28354

Dear Asim Roy,

could you please point me to a few neuroBIOLOGICAL references 
that justify your claim that
> A predominant belief in neuroscience is that synaptic plasticity
> and LTP/LTD imply local learning (in your sens).

I think many people appreciate that real learning implies the
concerted interplay of a lot of different brain systems and should 
not even be attempted to be explained by "isolated local learners".
See e.g. the series of review-papers on memory in a recent volume 
of PNAS 93 (1996) (

Good luck with your general theory of global/local learning.

best wishes 
Fred Wolf

Response #9:

Comp-Neuro Mailing List wrote:
> Date: Sun, 16 Feb 1997 22:57:11 -0500 (EST)
> From: Asim Roy <ataxr at IMAP1.ASU.EDU>
> Subject: Does plasticity imply local learning? And other 
> Any comments on these ideas and possibilities are welcome.
> Asim Roy
> Arizona State University

I am into neurocomputing for several years. I read your arguments 
with interest. They certainly deserve further attention. Perhaps 
some combination of global-local learning agents would be the right 

- Vassilis G. Kaburlasos
Aristotle University of Thessaloniki, Greece


Original Memo:

A predominant belief in neuroscience is that synaptic plasticity 
and LTP/LTD imply local learning. It is a possibility, but it is 
not the only possibility. Here are some thoughts on some of the 
other possibilities (e.g. global learning mechanisms or a 
combination of global/local mechanisms) and some discussion on the 
problems associated with "pure" local learning. 

The local learning idea is a very core idea that drives research in 
a number of different fields. I welcome comments on the questions 
and issues raised here. 

This note is being sent to many listserves. I will collect all of 
the responses from different sources and redistribute them to all 
of the participating listserves. The last such discussion was very 
productive. It has led to the realization by some key researchers 
in the connectionist area that "memoryless" learning perhaps is not 
a very "valid" idea. That recognition by itself will lead to more 
robust and reliable learning algorithms in the future. Perhaps a 
more active debate on the local learning issue will help us resolve 
this issue too.

A) Does plasticity imply local learning? 

The physical changes that are observed in synapses/cells in 
experimental neuroscience when some kind of external stimuli is 
applied to the cells may not result at all from any  specific 
"learning" at the cells. The cells might simply be responding to a 
"signal to change" - that is, to change by a specific amount in a 
specific direction. In animal brains, it is possible that the 
"actual" learning  occurs in some other part(s) of the brain, say 
perhaps by a global learning mechanism. This global mechanism can 
then send "change signals" to the various cells it is using to 
learn a specific task. So it is possible that in these neuroscience 
experiments, the external stimuli generates signals for change 
similar to those of a global learning agent in the brain and that 
the changes are not due to "learning" at the cells themselves. 

Please note that scientific facts and phenomenon like LTP/LTD or 
synaptic plasticity can probably be explained equally well by many 
theories of learning (e.g. local learning vs. global learning, 
etc.). However, the correctness of an explanation would have to be 
judged from its consistency with other behavioral and biological 
facts, not just "one single" biological phenomemon or fact.

B) "Pure" local learning does not explain a number of other 
"activities" that are part of the process of learning!! 

When learning is to take place by means of "local learning" in a 
network of cells, the network has to be designed prior to its 
training. Setting up the net before "local" learning can proceed 
implies that an external mechanism is involved in this part of the 
learning process. This "design" part of learning precedes actual 
training or learning by a collection of "local learners" whose only 
knowledge about anything is limited to the local learning law to 
use! In addition, these "local learners" may have to be told what 
type of local learning law to use, given that a variety of 
different types can be used under different circumstances. Imagine 
who is to "instruct and set up" such local learners which type of 
learning law to use? In addition to these, the "passing" of 
appropriate  information to the appropriate set of cells also has 
to be "coordinated" by some external or global learning mechanism. 
coordination cannot just happen by itself, like magic. It has to be 
directed from some place by some agent or mechanism.

In order to learn properly and quickly, humans generally collect 
and store relevant information in their brains and then "think" 
about it (e.g. what problem features are relevant, complexity of 
the problem, etc.). So prior to any "local learning," there must be 
processes in the brain that "examine" this "body of  
information/facts" about a problem in order to design the 
appropriate network that would fit the problem complexity, select 
the problem features that are meaningful, etc. It would be very 
difficult to answer the questions "What size net?" and "What 
features to use?" without looking at the problem (body of 
information)in great detail. A bunch of "pure" local learners, 
armed with their local learning laws, would have no clue to these 
issues of net design, generalization and feature selection.

So, in the whole, there are a "number of activities" that need to 
be performed before any kind of "local learning" can take place. 
These aforementioned learning activities "cannot" be performed by a 
collection of "local learning" cells! There is more to the process 
of learning than simple local learning by individual cells. Many 
learning "decisions/tasks" must precede actual training by "local 
learners." A group of independent "local learners" simply cannot 
start learning and be able to reproduce the learning 
characteristics and processes of an "autonomous system" like the 

Local learning or local computation, however, is still a feasible 
idea, but only within a general global learning context. A global 
learning mechanism would be the one that "guides" and "exploits" 
these local learners or computational elements. However, it is also 
possible that the global mechanism actually does all of the 
computations (learning) and "simply sends signals" to the network 
of cells for appropriate 
synaptic adjustment. Both of these possibilities seem logical: (a) 
a "pure" global mechanism that learns by itself and then sends 
signals to the cells to adjust, or (b) a global/local combination 
where the global mechanism performs certain tasks and then uses the 
local mechanism for training/learning. 

Thus note that the global learning mechanism may actually be 
implemented with a collection of local learners or computational 
elements!! However, certain "learning decisions" are made in the 
global sense and not by "pure" local learners.

The basic argument being made here is that there are many tasks in 
a "learning process" and that a set of "local learners" armed with 
their local learning laws is incapable of performing all of those 
tasks. So local learning can only exist in the context of global 
learning and thus is only "a part" of the total learning process. 

It will be much easier to develop a consistent learning theory 
using the global/local idea.  The global/local idea perhaps will 
also give us a better handle on the processes that we call 
"developmental" and "evolutionary." And it will, perhaps, allow us 
to better explain many of the puzzles and inconsistencies in our 
current body of discoveries about the brain. And, not the least, it 
will help us construct far better algorithms by removing the 
"unwarranted restrictions" imposed on us by the current ideas. Any 
comments on these ideas and possibilities are welcome.

Asim Roy
Arizona State University

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