Department of Computer Science, University College London, London, United Kingdom.
PLoS One. 2012;7(6):e36223. doi: 10.1371/journal.pone.0036223. Epub 2012 Jun 12.
From a computational theory of V1, we formulate an optimization problem to investigate neural properties in the primary visual cortex (V1) from human reaction times (RTs) in visual search. The theory is the V1 saliency hypothesis that the bottom-up saliency of any visual location is represented by the highest V1 response to it relative to the background responses. The neural properties probed are those associated with the less known V1 neurons tuned simultaneously or conjunctively in two feature dimensions. The visual search is to find a target bar unique in color (C), orientation (O), motion direction (M), or redundantly in combinations of these features (e.g., CO, MO, or CM) among uniform background bars. A feature singleton target is salient because its evoked V1 response largely escapes the iso-feature suppression on responses to the background bars. The responses of the conjunctively tuned cells are manifested in the shortening of the RT for a redundant feature target (e.g., a CO target) from that predicted by a race between the RTs for the two corresponding single feature targets (e.g., C and O targets). Our investigation enables the following testable predictions. Contextual suppression on the response of a CO-tuned or MO-tuned conjunctive cell is weaker when the contextual inputs differ from the direct inputs in both feature dimensions, rather than just one. Additionally, CO-tuned cells and MO-tuned cells are often more active than the single feature tuned cells in response to the redundant feature targets, and this occurs more frequently for the MO-tuned cells such that the MO-tuned cells are no less likely than either the M-tuned or O-tuned neurons to be the most responsive neuron to dictate saliency for an MO target.
从 V1 的计算理论出发,我们提出了一个优化问题,旨在通过人类在视觉搜索中的反应时间 (RT) 来研究初级视觉皮层 (V1) 的神经特性。该理论是 V1 显著度假说,即任何视觉位置的自下而上的显著度由相对于背景响应的最高 V1 响应来表示。探测到的神经特性与那些与不太知名的 V1 神经元相关,这些神经元在两个特征维度上同时或联合调谐。视觉搜索是在具有相同特征的背景条中找到在颜色 (C)、方向 (O)、运动方向 (M) 或这些特征的组合 (例如 CO、MO 或 CM) 中唯一的目标条。特征单一目标是显著的,因为它引起的 V1 响应在很大程度上逃脱了对背景条响应的同特征抑制。联合调谐细胞的反应表现在冗余特征目标 (例如 CO 目标) 的 RT 比两个相应单特征目标 (例如 C 和 O 目标) 的 RT 竞争预测的 RT 更短。我们的研究使得以下可测试的预测成为可能。当上下文输入在两个特征维度上与直接输入都不同,而不仅仅是一个特征维度上不同时,对 CO 或 MO 联合细胞的反应的上下文抑制会更弱。此外,在响应冗余特征目标时,CO 调谐细胞和 MO 调谐细胞通常比单特征调谐细胞更活跃,而且这种情况对于 MO 调谐细胞更为常见,以至于 MO 调谐细胞与 M 调谐或 O 调谐神经元一样,不太可能成为对 MO 目标决定显著度的最敏感神经元。