Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Information-based objective functions for active data selection
Neural Computation
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Neural network exploration using optimal experiment design
Neural Networks
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
How to assess a model's testability and identifiability
Journal of Mathematical Psychology
The importance of complexity in model selection
Journal of Mathematical Psychology
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Nonlinear V1 responses to natural scenes revealed by neural network analysis
Neural Networks - 2004 Special issue Vision and brain
Asymptotic Theory of Information-Theoretic Experimental Design
Neural Computation
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Sequential optimal design of neurophysiology experiments
Neural Computation
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Active learning with statistical models
Journal of Artificial Intelligence Research
Gaussian sum approach with optimal experiment design for neural network
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Automating the design of informative sequences of sensory stimuli
Journal of Computational Neuroscience
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The stimulus-response relationship of many sensory neurons is nonlinear, but fully quantifying this relationship by a complex nonlinear model may require too much data to be experimentally tractable. Here we present a theoretical study of a general two-stage computational method that may help to significantly reduce the number of stimuli needed to obtain an accurate mathematical description of nonlinear neural responses. Our method of active data collection first adaptively generates stimuli that are optimal for estimating the parameters of competing nonlinear models and then uses these estimates to generate stimuli online that are optimal for discriminating these models. We applied our method to simple hierarchical circuit models, including nonlinear networks built on the spatiotemporal or spectral-temporal receptive fields, and confirmed that collecting data using our two-stage adaptive algorithm was far more effective for estimating and comparing competing nonlinear sensory processing models than standard nonadaptive methods using random stimuli.