Difference between revisions of "Neuronal decision making model"
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{{stub|[[User:WinSysop|Tal Yaron]] 22:23, 17 June 2014 (IDT)}} | {{stub|[[User:WinSysop|Tal Yaron]] 22:23, 17 June 2014 (IDT)}} | ||
| − | The basic mechanism of decision making is [[evaluative neural network]] (ENN). ENN has two or more paths for reaching a goal. For every [[node]] in a path, there are several [[implications]]. Every implication has a value which is represented by the [[reward system]]. the value can very from positive to negative. | + | The basic mechanism of decision making is [[evaluative neural network]] (ENN). ENN has two or more paths for reaching a goal. For every [[node]] in a path, there are several [[implications]]. Every implication has a value which is represented by the [[reward system]]. the value can very from positive to negative. The strength of the reward will be composed by the intensity of the reward that the reward system cell produce, and the closeness of the reward to the action, where immediate reward will increase the strength of the reward to the implication, while delayed reward will result reduced connection between implication and reward. The strength will be produced according to the learning rules of [[LTP]] and [[STP]]. |
[[category: decision making]] | [[category: decision making]] | ||
[[category: deliberation]] | [[category: deliberation]] | ||
Revision as of 13:38, 17 June 2014
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The basic mechanism of decision making is evaluative neural network (ENN). ENN has two or more paths for reaching a goal. For every node in a path, there are several implications. Every implication has a value which is represented by the reward system. the value can very from positive to negative. The strength of the reward will be composed by the intensity of the reward that the reward system cell produce, and the closeness of the reward to the action, where immediate reward will increase the strength of the reward to the implication, while delayed reward will result reduced connection between implication and reward. The strength will be produced according to the learning rules of LTP and STP.