The Effect of Spatial Frequency on the Visual Category Representation in the Macaque Inferior Temporal Cortex

Abstract

To expand our knowledge about the object recognition, it is critical to understand the role of spatial frequency (SF) in an object representation that occurs in the inferior temporal (IT) cortex at the final stage of processing the visual information across the ventral visual pathway. Object categories are being recognized hierarchically in at least three levels of abstraction: superordinate (e.g., animal), mid-level (e.g., human face), and subordinate (e.g., face identity). Psychophysical studies have shown rapid access to mid-level category information and low SF (LSF) contents. Although the hierarchical representation of categories has been shown to exist inside the IT cortex, the impact of SF on the multi-level category processing is poorly understood. To gain a deeper understanding of the neural basis of the interaction between SF and category representations at multiple levels, we examined the neural responses within the IT cortex of macaque monkeys viewing several SF-filtered objects. Each stimulus could be either intact or bandpass filtered into either the LSF (coarse shape information) or high SF (HSF) (fine shape information) bands. We found that in both High- and Low- SF contents, the advantage of mid-level representation has not been violated. This evidence suggests that mid-level category boundary maps are strongly represented in the IT cortex and remain unaffected with respect to any changes in the frequency content of stimuli. Our observations indicate the necessity of the HSF content for the superordinate category representation inside the IT cortex. In addition, our findings reveal that the representation of global category information is more dependent on the HSF than the LSF content. Furthermore, the lack of subordinate representation in both LSF and HSF filtered stimuli compared to the intact stimuli provide strong evidence that all SF contents are necessary for fine category visual processing.

Spyker: High-performance Library for Spiking Deep Neural Networks

“…In order to demonstrate the use case and the importance of the library in neuroscience research, a similarity analysis is done in this section to compare the biological plausibility of an SNN and a deep CNN model. The neural data needed for the analysis is recorded as spiking activity and LFP signals from Inferior Temporal (IT) cortex using a single electrode (169 sessions from two macaque monkeys, the neural data for the monkeys are pooled together) [51]. The task implemented here is a Rapid Serial Visual Presentation (RSVP).…”