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Chun-I Yeh, Dajun Xing, Robert M. Shapley; Differences in spatial signal processing between neurons in the input and output layers of the macaque primary visual cortex, V1. Journal of Vision 2009;9(8):751. doi: 10.1167/9.8.751.
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© ARVO (1962-2015); The Authors (2016-present)
The primary visual cortex (V1) is the gateway for visual information to reach other cortical areas. Here we report differences in V1 spatial processing by using different mapping techniques to measure neuronal receptive fields in different V1 layers of sufentanil-anesthetized monkeys. Layer-2/3 neurons, unlike their layer-4C counterparts, showed significantly different spatial properties when mapped with sparse noise (SN, Jones and Palmer, 1987) and dense noise [Hartley subspace (Ringach et al., 1997) and m-sequence white noise (Reid et al., 1997)] by reverse correlation. Many layer-2/3 neurons had spatial maps with multiple elongated on/off subregions when mapped with dense noise, but had unimodal and less elongated spatial maps when mapped with sparse noise. The similarity between the sparse-noise map and the dense-noise map, quantified as the spatial correlation between the two maps, was significantly lower for layer-2/3 neurons than for layer-4C neurons. For layer-2/3 neurons, the preferred orientation from dense-noise maps tended to be closer to the preferred orientation measured with drifting gratings than was the orientation preference of sparse-noise maps. Moreover, the majority of layer-2/3 neurons (93%) responded more strongly to light decrements (DEC) than to light increments (INC) when mapped with sparse noise [log (INC/DEC) = −0.37+0.32], but this was not the case for layer-4C neurons [log (INC/DEC) = −0.08+0.38]. The dark-dominated responses in V1 superficial layer might provide the substrate for stronger light-decrement responses presented in several psychophysical studies (e.g. Zemon et al., 1988; Chubb et al., 2004; Olman et al., 2008). Overall, these results suggest that, compared to neurons in layer 4C, layer 2/3 cells are affected by additional nonlinear stages of signal processing. Therefore, frequently used models of V1 neurons, such as the L-N-P [dynamic Linear filter - static Nonlinearity - Poisson spike encoder] model, may not be sufficient for most neurons in V1.
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