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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:How Materials Can Learn How to Function
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260910T035223Z
UID:tag:localist.com\,2008:EventInstance_37955785979164
DTSTART:20210930T190000Z
DTEND:20210930T200000Z
DESCRIPTION:How does learning occur? Neural networks learn via optimization
 \, where a loss function is minimized by a computer to achieve the desired
  result. But physical networks such as mechanical spring networks or flow 
 networks have no central processor so they cannot minimize such a loss fun
 ction. An alternative is to encode local rules into those networks so that
  they can evolve under external driving to develop function. For example\,
  if the springs in a mechanical network have equilibrium lengths that grow
  if the springs are stretched\, and shrink when the springs are compressed
 \, the network will naturally evolve under applied stresses. I will descri
 be how both of these strategies—global minimization of a loss function a
 s well as training by local rules--can be used to teach systems how to per
 form functions inspired by biology\, such as the ability of proteins (e.g.
  hemoglobin) to change their conformations upon binding of an atom (oxygen
 ) or molecule\, or the ability of the brain’s vascular network to send e
 nhanced blood flow and oxygen to specific areas of the brain associated wi
 th a given task.
LOCATION:
SUMMARY:How Materials Can Learn How to Function
URL;VALUE=URI:https://events.seas.harvard.edu/event/how_materials_can_learn
 _how_to_function
CATEGORIES:Colloquia / Seminar / Lecture
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