Biology subjects

Farivar, R.

Publications and source records attributed to Farivar, R..

2 recordsLinked to original sources

bioRxiv2025

Efficient Coding of Spatial Frequency in Natural Images: Cross-frequency Dependence

Research suggests that spatial frequency (SF) channels in the visual system operate with a degree of independence. However, the independence model has been questioned by evidence of non-additive effects in compound gratings, indicating complex interactions between SF channels. These studies, however, typically employ artificial stimuli, leaving questions about SF processing in natural images. Efficient Coding hypothesis, which posits that the visual system minimizes redundancy and retains relevant information, predicts a dependence between HSF and LSF. In this study, we examined interactions between LSF and HSF using natural and phase-scrambled images to explore SF integration during perception. Participants completed an SF identification task, using both natural and scrambled images to isolate the role of phase alignment. Our results indicate that HSF and LSF interact primarily in phase-aligned conditions, with phase scrambling driving independent processing of two SFs and reducing error rates. These findings suggest that phase alignment enhances perceptual efficiency, facilitating a trade-off between accuracy and redundancy reduction in natural scene processing.

neuroscience↗

bioRxiv2024

The Topology of Representational Geometry

Representational similarity analysis (RSA) is a powerful tool for abstracting and then comparing neural representations across brains, regions, models and modalities. However, typical RSA analyses compares pairs of representational dissimilarities to judge similarity of two neural systems, and we argue that such methods can not capture the shape of representational spaces. By leveraging tools from computational topology, which can probe the shape of high-dimensional data, we augment RSA to be able to detect more subtle yet real differences and similarities of representational geometries. This new method could be used in conjunction with regular RSA in order to make new inferences about neural function. Significance StatementBig data in high-dimensional spaces, like neuroimaging datasets, contain important shape structures. These shape structures can be analyzed to identify the underlying features and dynamics which drive the system. We showed that such analyses, applied to neural activity patterns elicited by viewing various objects, can identify real but subtle and complex features of those objects which are encoded in the brain.

neuroscience↗