Meta learning and `homeostatic self-regulation’ are closely related. Both are needed for the long-term stability of the cognitive system, regulating internal thresholds, learning-rates, attention fields and so on. They do not affect directly the primary cognitive information processing, e.g. they do not change directly the firing state of individual neurons, nor do they affect the primary learning, i.e. changes of synaptic strengths. The regulation of the sensibility of the synaptic plasticities with respect to the pre- and to the post-synaptic firing state is, on the other hand, a prime task for both meta learning and homeostatic self-regulation. Homeostatic self-regulation is local, always active and present, irrespectively of any global signal. Meta learning is, on the other hand, triggered by global signals, the diffusive control signals, generated by the cognitive system itself through distinct sub-components.
Published in Chapter:
Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
C. Gros (J.W. Goethe University Frankfurt, Germany)
Copyright: © 2009
|Pages: 14
DOI: 10.4018/978-1-60566-354-8.ch007
Abstract
All self-active living beings need to solve the motivational problem—the question of what to do at any moment of their life. For humans and non-human animals at least two distinct layers of motivational drives are known, the primary needs for survival and the emotional drives leading to a wide range of sophisticated strategies, such as explorative learning and socializing. Part of the emotional layer of drives has universal facets, being beneficial in an extended range of environmental settings. Emotions are triggered in the brain by the release of neuromodulators, which are, at the same time, are the agents for meta-learning. This intrinsic relation between emotions, meta-learning and universal action strategies suggests a central importance for emotional control for the design of artificial intelligences and synthetic cognitive systems. An implementation of this concept is proposed in terms of a dense and homogeneous associative network (dHan).