Toosi Ramin, Karami Behnam, Koushki Roxana, Shakerian Farideh, Noroozi Jalaledin, Rezayat Ehsan, Vahabie Abdol-Hossein, Akhaee Mohammad Ali, Dehaqani Mohammad-Reza A
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Islamic Republic of Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
Elife. 2025 Sep 12;13:RP93589. doi: 10.7554/eLife.93589.
Understanding the neural representation of spatial frequency (SF) in the primate cortex is vital for unraveling visual processing in object recognition. While many studies focus on SF in the primary visual cortex, the characteristics and interaction of SF with category representation remain unclear. To explore SF representation in the inferior temporal (IT) cortex of macaques, we conducted extracellular recordings with complex stimuli systematically filtered by SF. Our findings reveal explicit SF coding at both single-neuron and population levels. Temporal dynamics analysis of SF representation reveals that low SF (LSF) is decoded faster than high SF (HSF), and the SF preference dynamically shifts from LSF to HSF over time. Additionally, the SF representation for each neuron forms a profile that predicts category selectivity at the population level. IT neurons cluster into four groups based on SF preference, each with distinct category coding behaviors. Notably, HSF-preferring neurons show the highest category decoding for faces. Despite the existing connection between SF and category coding, we have identified uncorrelated representations of SF and category. Unlike category coding, SF is more sparsely represented and depends more on individual neurons. These findings dissociate SF and category representations, underscoring SF's pivotal role in object recognition.
了解灵长类动物皮层中空间频率（SF）的神经表征对于揭示物体识别中的视觉处理至关重要。虽然许多研究聚焦于初级视觉皮层中的空间频率，但空间频率与类别表征的特征及相互作用仍不清楚。为了探究猕猴颞下（IT）皮层中的空间频率表征，我们使用经空间频率系统滤波的复杂刺激进行了细胞外记录。我们的研究结果揭示了在单神经元和群体水平上明确的空间频率编码。对空间频率表征的时间动态分析表明，低空间频率（LSF）的解码速度比高空间频率（HSF）快，并且随着时间的推移，空间频率偏好会从低空间频率动态转变为高空间频率。此外，每个神经元的空间频率表征形成了一种轮廓，可在群体水平上预测类别选择性。基于空间频率偏好，IT神经元聚为四组，每组具有不同的类别编码行为。值得注意的是，偏好高空间频率的神经元对面部的类别解码能力最强。尽管空间频率与类别编码之间存在现有联系，但我们已经确定了空间频率和类别的不相关表征。与类别编码不同，空间频率的表征更为稀疏，且更多地依赖于单个神经元。这些发现区分了空间频率和类别表征，强调了空间频率在物体识别中的关键作用。
