METABOLIC COST AS AN ORGANIZING PRINCIPLE FOR COOPERATIVE LEARNING

David Balduzzi (), Pedro A. Ortega () and Michel Besserve ()
Additional contact information
David Balduzzi: Swiss Federal Institute of Technology, ETH Zürich, Switzerland
Pedro A. Ortega: Max Planck Institute for Intelligent Systems, Tübingen, Germany
Michel Besserve: Max Planck Institute for Intelligent Systems, Tübingen, Germany

Advances in Complex Systems (ACS), 2013, vol. 16, issue 02n03, 1-18

Abstract: This article investigates how neurons can use metabolic cost to facilitate learning at a population level. Although decision-making by individual neurons has been extensively studied, questions regarding how neurons should behave to cooperate effectively remain largely unaddressed. Under assumptions that capture a few basic features of cortical neurons, we show that constraining reward maximization by metabolic cost aligns the information content of actions with their expected reward. Thus, metabolic cost provides a mechanism whereby neurons encode expected reward into their outputs. Further, aside from reducing energy expenditures, imposing a tight metabolic constraint also increases the accuracy of empirical estimates of rewards, increasing the robustness of distributed learning. Finally, we present two implementations of metabolically constrained learning that confirm our theoretical finding. These results suggest that metabolic cost may be an organizing principle underlying the neural code, and may also provide a useful guide to the design and analysis of other cooperating populations.

Keywords: Neural code; information theory; constrained reward maximization; synaptic plasticity; reinforcement learning; metabolic cost (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525913500124
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:16:y:2013:i:02n03:n:s0219525913500124

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525913500124

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().