Elements of information theory
Elements of information theory
Probabilistic interpretation of population codes
Neural Computation
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Slow feature analysis: unsupervised learning of invariances
Neural Computation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Computation: finite and infinite machines
Computation: finite and infinite machines
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Object recognition by artificial cortical maps
Neural Networks
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Modeling the visual system can be done at multiple levels of description ranging from computer simulations of detailed biophysical models to firing rate and so-called 'black-box' models. Re-introducing David Marr's analysis levels for the visual system, we motivate the use of more abstract models in order to answer the question of what the visual system is computing. The approaches we selected to review in this article concentrate on modeling the changes of sensory representations. The considered time-scales, range from the developmental time-scale of receptive field formation to fast transient neuronal dynamics during a single stimulus presentation. Common to all approaches is their focus on providing functional interpretations, instead of 'only' explanations in terms of mechanisms. Although the concrete approaches can be distinguished along different lines, a common theme is emerging which may qualify as a paradigm for providing functional interpretations for changes of receptive field properties, i.e. the dynamic adjustment of sensory representations to varying external or internal conditions.