Journal of NeuroscienceNeurosciencePeer-reviewed journal article
Spatial arrangement drastically changes the neural representation of multiple visual stimuli that compete in more than one feature domain
Abstract
Natural scenes often contain multiple objects and surfaces. However, how neurons in the visual cortex represent multiple visual stimuli is not well understood. Previous studies have shown that, when multiple stimuli compete in one feature domain, the evoked neuronal response is biased toward the stimulus that has a stronger signal strength. Here we investigate how neurons in the middle temporal (MT) cortex of macaques represent multiple stimuli that compete in more than one feature domain. Visual stimuli were two random-dot patches moving in different directions. One stimulus had low luminance contrast and moved with high coherence, whereas the other had high contrast and moved with low coherence. We found that how MT neurons represent multiple stimuli depended on the spatial arrangement of the stimuli. When two stimuli were overlapping, MT responses were dominated by the stimulus component that had high contrast. When two stimuli were spatially separated within the receptive fields, the contrast dominance was abolished. We found the same results when using contrast to compete with motion speed. Our neural data and computer simulations using a V1-MT model suggest that the contrast dominance found with overlapping stimuli is due to normalization occurring at an input stage fed to MT, and MT neurons cannot overturn this bias based on their own feature selectivity. The interaction between spatially separated stimuli can largely be explained by normalization within MT. Our results revealed new rules on stimulus competition and highlighted the impact of hierarchical processing on representing multiple stimuli in the visual cortex.\n\nSIGNIFICANCE STATEMENTPrevious studies have shown that the neural representation of multiple visual stimuli can be accounted for by a divisive normalization model. By using multiple stimuli that compete in more than one feature domain, we found that luminance contrast has a dominant effect in determining competition between multiple stimuli when they were overlapping but not spatially separated. Our results revealed that neuronal responses to multiple stimuli in a given cortical area cannot be simply predicted by the population neural responses elicited in that area by the individual stimulus components. To understand the neural representation of multiple stimuli, rather than considering response normalization only within the area of interest, one must consider the computations including normalization occurring along the hierarchical visual pathway.
- organism: mouse
- assay: spatial transcriptomics
- research focus: cell-cell communication
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 9
- FWCI
- 0.27
- Field percentile
- 49th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Mechanisms of Neural Representation and Segregation of Multiple Spatially Separated Visual Stimuli
Steven Wiesner, Bikalpa Ghimire, Xin Huang·bioRxiv·Sep 11, 2025
- The role of binocular disparity and attention in the neural representation of multiple moving stimuli in the visual cortex
Anjani Sreeprada Chakrala, Jianbo Xiao, Xin Huang·bioRxiv·Jun 25, 2023
- Multiple objects evoke fluctuating responses in several regions of the visual pathway
Meredith N. Schmehl, Valeria C. Caruso, … Jennifer M. Groh·eLifePeer-reviewed·Mar 4, 2024
- The functional organization of high-level visual cortex determines the representation of complex visual stimuli
Libi Kliger, Galit Yovel·Journal of NeurosciencePeer-reviewed·Aug 28, 2020
- Coordinated multiplexing of information about separate objects in visual cortex
Na Young Jun, Douglas A. Ruff, … Jennifer M. Groh·eLifePeer-reviewed·Oct 24, 2022
- Neural multiplexing vs place coding: Multiplexing steps in when most needed
Julia M. Leeman, Shawn M. Willett, … Jennifer M. Groh·bioRxiv·Aug 10, 2026