Multilayer feedforward networks are universal approximators
Neural Networks
Regularization theory and neural networks architectures
Neural Computation
Statistically efficient estimation using population coding
Neural Computation
The handbook of brain theory and neural networks
Population computation of vectorial transformations
Neural Computation
A Neural Model of Perceptual-Motor Alignment
Journal of Cognitive Neuroscience
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Parameter extraction from population codes: A critical assessment
Neural Computation
Neural Networks - 2005 Special issue: IJCNN 2005
Interpolation and Extrapolation in Human Behavior and Neural Networks
Journal of Cognitive Neuroscience
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The parametric variation in neuronal discharge according to the values of sensory or motor variables strongly influences the collective behavior of neuronal populations. A multitude of studies on the populations of broadly tuned neurons (e.g., cosine tuning) have led to such well-known computational principles as population coding, noise suppression, and line attractors. Much less is known about the properties of populations of monotonically tuned neurons. In this letter, we show that there exists an efficient weakly biased linear estimator for monotonic populations and that neural processing based on linear collective computation and least-square error learning in populations of intensity-coded neurons has specific generalization capacities.