Discrimination thresholds for channel-coded systems
Biological Cybernetics
Probabilistic interpretation of population codes
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
The effect of correlated variability on the accuracy of a population code
Neural Computation
Representational accuracy of stochastic neural populations
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
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We investigated the connection between electrophysiological properties of neural populations and their ability to discriminate between the presence of one and two stimuli in a two-alternative forced choice task. The model is based on maximum likelihood estimation in a stimulus space that allows for the presence of multiple stimuli. Repetitive presentation of virtual stimuli yields receiver–operator–characteristics (ROC) curves and psychometric functions from noisy neural responses. For the case of one-dimensional stimuli like the movement direction of a random dot cloud we tested two coding strategies for discriminative ability. It turns out that narrow tuning curves and a variability of tuning widths within the neural population yields a high percentage of correct responses in the simulated psychophysical discrimination task. These results are similar to findings about the localization of single stimuli by neural populations: The examined encoding strategies lead to both an improvement of single stimulus estimation and discrimination between one and two stimuli.