In contrast, random noise has more flexibility in stimulus duration, as indefinitely long stimuli can be pre-computed, arbitrary segments of which can be shown during data collection without selleck compound adversely affecting stimuli statistics. In contrast, Sincich et al. (2009a) found that neither correlated Gaussian nor random white

noise were as effective at driving neurons as luminance flicker that resembled natural scene temporal fluctuations with 1/f properties. Their observations suggest that work using other and currently more common noise techniques could be sampling a limited portion of the neuronal response range. Methodological advances have brought about the possibility of independently stimulating single

retinal photoreceptors for extraordinarily fine-grained control over retinal input to LGN. McMahon et al. (2000) showed that retinothalamic circuitry can be probed in monkeys using a clever laser interferometry technique that bypasses the optics of the eye to form grating stimuli directly on the retina. In a similarly technically impressive effort, Sincich et al. (2009b) were able to reliably evoke activity from macaque LGN cells by stimulating single retinal cone cells using micron-scale spots of light targeted at the LGN CRF center Birinapant with a scanning laser stimulus. Although neither study explored the ECRF, both were able to quantify the contribution of each of multiple cones spanning the CRF for a set of example thalamic cells. As the technique of adaptive optics is relatively new, we might well expect to see additional, high-input precision visual mapping results in the near future, as suggested in the recent review by Roorda (2011). Recent technical advances have included progress in analytical methods as well. Fairhall et al. (2012) discuss recent advances in information theory such as Maximally Informative Dimensions (MID). MID allows

for the use of reverse correlation techniques with stimuli other than Gaussian white noise. It also allows for the estimation of feature selectivity either when natural stimuli are used. Sharpee’s review (Sharpee, 2013) discusses the various models that exist to define the receptive field, specifically for use in conjunction with natural stimuli. The review is a good resource for information on linear models and their expansions, STAs, STCs, MIDs, multidimensional feature selectivity, maximally informative subspace, and maximally informative quadratic models, as well as all of these models’ best suited applications and the assumptions that go along with each.