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DTSTAMP:20260404T165147Z
UID:1591099200@ist.ac.at
DTSTART:20200602T140000
DTEND:20200602T150000
DESCRIPTION:Speaker: Gasper Tkacik\nAbstract: Ideas about optimization are 
 at the core of how we approach biological complexity. Quantitative predict
 ions about biological systems have been successfully derived from first pr
 inciples in the context of efficient coding\, metabolic and transport netw
 orks\, evolution\, reinforcement learning\, and decision making\, by postu
 lating that a system has evolved to optimize some utility function under b
 iophysical constraints. Yet as normative theories become increasingly high
 -dimensional and optimal solutions stop being unique\, it gets progressive
 ly hard to judge whether theoretical predictions are consistent with\, or 
 "close to"\, data. I will illustrate these issues using efficient coding a
 pplied to simple neuronal models as well as to a complex and realistic bio
 chemical reaction network. As a solution\, we developed a statistical fram
 ework which smoothly interpolates between ab initio optimality predictions
  and Bayesian parameter inference from data\, while also permitting statis
 tically rigorous tests of optimality hypotheses.Webinar ID: 948 1982 4190P
 assword: 283739
LOCATION:https://istaustria.zoom.us/j/94819824190?pwd=TEsvSzdYUmYxRzlQVlNiV
 VlOYms0Zz09\, ISTA
ORGANIZER:rita.six@ist.ac.at
SUMMARY:Gasper Tkacik: Neuroscience Seminar Series - Gasper Tkacik
URL:https://talks-calendar.ista.ac.at/events/2765
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