Download In order for evolution by natural selection to explain the adaptation

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Evol/Ecol/Behav Biology IUB
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Special Presentation: "Understanding the evolution of evolvability using the theory of memory,
learning and generalization." Dr. Richard A. Watson, Institute for Life Sciences/Electronics and
Computer Science, University of Southampton.
Public
Friday, August 05, 2016 2:00 PM
Friday, August 05, 2016 3:00 PM
Jordan Hall 123
Mike Wade
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In order for evolution by natural selection to explain the adaptation that
we observe in natural populations we must account for the availability of
suitable variations that natural selection can act on. Rupert Riedl, an
early pioneer of evolutionary developmental biology, suggested that this
is facilitated by a specific developmental organisation that is itself a
product of past selection. However, the construction of a theoretical
framework to formalise such ‘evolution of evolvability’ has been
continually frustrated by the indisputable fact that natural selection
cannot favour structures for benefits they have not yet produced. Here
we resolve this problem. Recent work shows that short-term selective
pressures on gene interactions are functionally equivalent to a simple
type of associative learning, well-understood in neural network research.
This is important for the evolution of evolvability because this type of
learning system can clearly change in a way that improves its
performance on future test cases, i.e., before it has been exposed to
those cases, without the need for the future to cause the past.
Recognising a formal link with the conditions that enable such predictive
generalisation in learning systems unlocks well-established theory to
apply to understanding the evolution of evolvability. Here we use this to
elucidate, and demonstrate for the first time, conditions where short-term
selective pressures (producing the organisation that Riedl suggested)
alter evolutionary trajectories in a manner that systematically improves
long-term evolutionary outcomes. More generally, we discuss and
demonstrate how learning theory can be converted to the evolutionary
domain to demystify theoretical problems in evo-devo, evo-eco and the
major evolutionary transitions.
Contact Email: [email protected]