Sep 12, 2015

Model-free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

BioRxiv : the Preprint Server for Biology
Markus HelmerDemian Battaglia


Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response dat...Continue Reading

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Mentioned in this Paper

Entire Brodmann Areas 17 (Striate Cortex),18 (Parastriate Cortex) and 19 (Peristriate Cortex) of Occipital Lobe
Afferent Neuron
Macaca mulatta
Visual Cortex
Sensory Receptor Cells
Orientation (spatial)

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