ORIENTATION TUNING CURVES - EMPIRICAL DESCRIPTION AND ESTIMATION OF PARAMETERS

This paper compares the ability of some simple model functions to desc ribe orientation tuning curves obtained in extracellular single-unit r ecordings from area 17 of the cat visual cortex. It also investigates the relationships between three methods currently used to estimate pre ferred orientation from tuning curve data: (a) least-squares curve fit ting, (b) the vector sum method and (c) the Fourier transform method ( Worgotter and Eysel 1987). The results show that the best fitting mode l function for single-unit orientation tuning curves is a von Mises ci rcular function with a variable degree of skewness. However, other fun ctions, such as a wrapped Gaussian, fit the data nearly as well. A cos ine function provides a poor description of tuning curves in almost al l instances. It is demonstrated that the vector sum and Fourier method s of determining preferred orientation are equivalent and identical to calculating a least-square fit of a cosine function to the data. Leas t-squares fitting of a better model function, such as a von Mises func tion or a wrapped Gaussian, is therefore likely to be a better method for estimating preferred orientation. Monte-Carlo simulations confirme d this, although for broad orientation tuning curves sampled at 45 deg rees intervals, as is typical in optical recording experiments, all th e methods gave similarly accurate estimates of preferred orientation. The sampling interval, the estimated error in the response measurement s and the probable shape of the underlying response function all need to be taken into account in deciding on the best method of estimating preferred orientation from physiological measurements of orientation t uning data.