🚀 go-pugleaf

Louis,

Perhaps you can help me to understand something?

I looked up the references you gave, and it does appear that there
is enough substance to the evidence for "differentiial" Hebbian
mechanisms (reinforcing only inputs which precede the response
in time) that I can't dismiss the notion out of hand.

However, that leaves me confused as to how to address a problem
that I came across several years ago, and thought I had resolved by
accepting that the basic Hebbian mechanisms were not sensitive to
the ordering of stimulus and response, that correlation rather than
causation was indeed reinforced.

If a hebbian-adaptive cell learns to recognize some cluster of events
in a continuing ensemble of inputs, it will necessarily reinforce
any correllated predecessors to the events which it is currently
recognizing.

As the earlier-occurring signals acquire weight, the cell temds to fire
earlier, shifting the center of its response toward the leading edge
of a sequence of correllated events.

If it does not also reinforce inputs arriving after its current firing
time, the originally-learned inputs lose weight as it moves forward.

Unfortunately, this leads to an intractible instablility, in that
the response will drift to the leading edge of a correllated sequence,
where it will hover until transient correlations entice the cell into
firing a bit earlier, reacting to events not reliably correllated with
the original series over a longer term.

Since the original adaptation has been "forgotten" (due to asymetric
reinforcement), the cell does not acquire a stable response... it keeps
moving on to different things.

This turned out to be (at least so far as I could determine) an inherent
weakness in the assumption of precursor-only reinforcement, which could
not be overcome by "tweaking" any other aspects of the model.

I could fix it only by giving up on asymetric reinforcement, allowing
reinforcement to extend to inputs occurring both before and after the
post-synaptic response.

As it turned out, I found this to be a useful step in a number of ways,
and discovered that I could re-introduce the learning of causality at
higher levels in a much more robust fashion.  So I was, until now,
quite happy with the assumption of symetric reinforcement.

But if that is truly not the way real cells work, I ought to
understand how that can be.

So.  Can you tell me how to avoid the instability associated with
anticipatory drift, which appears to me to be an unavoidable result
of the model you are espousing?

Bill Modlin
modlin@ttlc.net
modlin@ibm.net