Format

Send to

Choose Destination
Neural Netw. 2013 May;41:225-39. doi: 10.1016/j.neunet.2012.11.008. Epub 2012 Dec 8.

Modulation for emergent networks: serotonin and dopamine.

Author information

1
Michigan State University, East Lansing, MI, USA. weng@cse.msu.edu

Abstract

In autonomous learning, value-sensitive experiences can improve the efficiency of learning. A learning network needs be motivated so that the limited computational resources and the limited lifetime are devoted to events that are of high value for the agent to compete in its environment. The neuromodulatory system of the brain is mainly responsible for developing such a motivation system. Although reinforcement learning has been extensively studied, many existing models are symbolic whose internal nodes or modules have preset meanings. Neural networks have been used to automatically generate internal emergent representations. However, modeling an emergent motivational system for neural networks is still a great challenge. By emergent, we mean that the internal representations emerge autonomously through interactions with the external environments. This work proposes a generic emergent modulatory system for emergent networks, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., pleasure or sweet). We experimented with this motivational system for two settings. The first is a visual recognition setting to investigate how such a system can learn through interactions with a teacher, who does not directly give answers, but only punishments and rewards. The second is a setting for wandering in the presence of a friend and a foe.

PMID:
23294763
DOI:
10.1016/j.neunet.2012.11.008
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center