Purchase this article with an account.
Charles H Anderson, Harris Nover, Gregory C DeAngelis; Modeling the velocity tuning of macaque MT neurons. Journal of Vision 2003;3(9):404. doi: 10.1167/3.9.404.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
The direction and speed tuning of MT neurons has been well characterized physiologically. Direction tuning curves are generally Gaussian, whereas speed tuning curves are typically skewed toward higher speeds on a linear axis.
We show that the speed tuning curves of MT neurons are well fit (median R2 = 0.97) by a Gaussian in log speed, q(|v|) = ln((|v|+v0)|v0), with center position q(v_c) and standard deviation sigma_s. Speed is denoted by |v|, v0 is a constant, and v_c denotes the preferred speed. The fits produced tightly clustered values for sigma_s (mean =1.22) and v0 (mean = 0.30 deg/sec) across a population of 501 MT neurons. Fixing both of these parameters causes only a modest reduction in the quality of the fits (median R2 = 0.92), leaving only the preferred speed, v_c, and amplitude of the tuning curve to vary across neurons. Thus, the speed tuning curves of the MT neurons are well-described by a single function that is simply shifted along the log speed axis by q(v_c).
We next generalized the expression for the velocity tuning of MT neurons in two dimensions by adopting a log-polar model (similar to that used to model the retinotopic map in V1) in which the 2D mapping from v to q is locally isotropic. The tuning curves are modeled as a Gaussian in q, with variance sigma_s along the preferred direction and sigma_d in the orthogonal direction. This model provided good simultaneous fits to the direction and speed tuning curves of MT neurons (median R2 = 0.94), with the values of sigma_s and sigma_d tightly clustered around their means of 1.3 and 0.80. Theoretical analysis predicts sigma_d should be less than sigma_s.
Thus, our modeling shows the shape of the velocity tuning of the majority of MT neurons can be fit by a single function parameterized solely by the preferred direction and speed. For speeds > v0=0.3deg/sec, which is quite small, the representation is scale invariant and supports a Weber-Fechner law for speed discrimination.
This PDF is available to Subscribers Only