Journal
NEUROCOMPUTING
Volume 71, Issue 16-18, Pages 3635-3639Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2008.03.007
Keywords
Cross-entropy method; Spike-based reconstruction; Reconstruction networks
Categories
Funding
- Hungarian Ministry of Education
- EC NEST [FP6-043261]
Ask authors/readers for more resources
Most neural optimization algorithms use either gradient tuning methods or complicated recurrent dynamics that may lead to suboptimal solutions or require huge number of iterations. Here we propose a framework based on the cross-entropy method (CEM). CEM is an efficient global optimization technique, but it requires batch access to many samples. We transcribed CEM to an online form and embedded it into a reconstruction network that finds optimal representations in a robust way as demonstrated by computer simulations. We argue that this framework allows for neural implementation and suggests a novel computational role for spikes in real neuronal systems. (C) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available