Modeling the Receptive Fields of Visual Neurons
This unit starts with a detailed introduction on fitting functions to data using the SciPy-Optimize library. Learn how to fit models to your data and evaluate the quality of the model fit. This knowledge then gets applied to spiking data recorded from hundreds of neurons in response to visual stimuli from different locations. The unit covers estimating neurons’ receptive field maps using the libraries Pandas and Seaborn, then fitting a two-dimensional Gaussian function to model the receptive field. Finally, you’ll learn how to evaluate the fitted models to infer which neurons have a response profile that is well characterized by a receptive field and estimate and compare the receptive field sizes of neurons throughout the visual system.
Tools
Sessions
Curve Fitting and Model Evaluation
Create parametric models and fit them to data, evaluate goodness-of-fit, and quantify parameter uncertainty
Estimating and Modeling Receptive Field Maps
Compute spatial receptive field maps from neural responses to visual stimuli and fit Gaussian models to characterize receptive field properties