<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sejnowski, TJ</style></author><author><style face="normal" font="default" size="100%">Poizner, H</style></author><author><style face="normal" font="default" size="100%">Lynch, G</style></author><author><style face="normal" font="default" size="100%">Gepshtein, S</style></author><author><style face="normal" font="default" size="100%">Greenspan, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Prospective Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Proc IEEE Inst Electr Electron Eng</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&amp;arnumber=6803897&amp;queryText%3Dprospective+optimization</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">102</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div&gt;Human performance approaches that of an ideal&lt;/div&gt;

&lt;div&gt;observer and optimal actor in some perceptual and motor&lt;/div&gt;

&lt;div&gt;tasks. These optimal abilities depend on the capacity of the&lt;/div&gt;

&lt;div&gt;cerebral cortex to store an immense amount of information&lt;/div&gt;

&lt;div&gt;and to flexibly make rapid decisions. However, behavior only&lt;/div&gt;

&lt;div&gt;approaches these limits after a long period of learning while&lt;/div&gt;

&lt;div&gt;the cerebral cortex interacts with the basal ganglia, an ancient&lt;/div&gt;

&lt;div&gt;part of the vertebrate brain that is responsible for learning&lt;/div&gt;

&lt;div&gt;sequences of actions directed toward achieving goals. Progress&lt;/div&gt;

&lt;div&gt;has been made in understanding the algorithms used by the&lt;/div&gt;

&lt;div&gt;brain during reinforcement learning, which is an online&lt;/div&gt;

&lt;div&gt;approximation of dynamic programming. Humans also make&lt;/div&gt;

&lt;div&gt;plans that depend on past experience by simulating different&lt;/div&gt;

&lt;div&gt;scenarios, which is called prospective optimization. The same&lt;/div&gt;

&lt;div&gt;brain structures in the cortex and basal ganglia that are active&lt;/div&gt;

&lt;div&gt;online during optimal behavior are also active offline during&lt;/div&gt;

&lt;div&gt;prospective optimization. The emergence of general principles&lt;/div&gt;

&lt;div&gt;and algorithms for goal-directed behavior has consequences&lt;/div&gt;

&lt;div&gt;for the development of autonomous devices in engineering&lt;/div&gt;

&lt;div&gt;applications.&lt;/div&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record></records></xml>