What is the goal of sensory coding?
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
Application of the cross-entropy method to clustering and vector quantization
Journal of Global Optimization
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Learning the states: a brain inspired neural model
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Sparse and silent coding in neural circuits
Neurocomputing
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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.