Biology subjects

Wiesner, S.

Publications and source records attributed to Wiesner, S..

5 recordsLinked to original sources

bioRxiv2025

Critical spatial separation at the scale of V1 receptive fields determines motion segmentation

Integrating elements that belong to a single object while segregating overlapping objects is a fundamental challenge for the visual system, exemplified by the phenomenon of motion transparency. While the middle temporal (MT) cortex is central to motion processing, the role of V1 in motion transparency remains controversial. It is still unclear at what spatial scale segmentation for motion transparency occurs. To address these questions, we conducted human psychophysics experiments using locally paired-dot stimuli moving in two directions separated by 90{degrees}. Subjects performed a 3AFC task to report whether the visual stimulus had no motion, a single direction, or two distinct directions. We systematically manipulated the path length of the dots, and therefore, the spatial separation between the paired dots, and the retinal eccentricity of the visual stimulus. We found that as the spatial separation between the paired dots increased, subjects perception shifted from a single direction to two distinct directions. Critically, we found that the spatial separation required for this perceptual transition increased with the eccentricity and closely matched the known receptive field sizes of V1 neurons at those eccentricities. Direction segmentation occurred only when the spatial separation between motion components exceeded the V1 receptive field size. Conversely, direction integration occurred when the spatial separation was smaller than the V1 receptive field size. Our results demonstrate that the receptive field size of V1 neurons sets the critical spatial scale for direction segmentation and suggest that V1 plays a key role in motion transparency and, more generally, in motion segmentation.

neuroscience↗

bioRxiv2025

Mechanisms of Neural Representation and Segregation of Multiple Spatially Separated Visual Stimuli

Segregating objects from one another and the background is essential for scene understanding, object recognition, and visually guided action. In natural scenes, it is common to encounter spatially separated stimuli, such as distinct figure-ground regions, adjacent objects, and partial occlusions. Neurons in mid- and high-level visual cortex have large receptive fields (RFs) that often encompass multiple, spatially separated stimuli. It is unclear how neurons represent and segregate multiple stimuli within their RFs, and the role of spatial cues for such representation. To investigate these questions, we recorded neuronal responses in the middle temporal (MT) cortex of monkeys to spatially separated stimuli that moved simultaneously in two directions. We found that, across motion directions, response tuning to multiple stimuli was systematically biased toward the stimulus located at the more-preferred RF subregion of the neuron. The sign and magnitude of this spatial-location bias were correlated with the spatial preference of the neuron for single stimuli presented in isolation. We demonstrated that neuronal responses to multiple stimuli can be captured by an extended normalization model, which is a sum of the responses elicited by individual stimuli weighted by the spatial preference of the neuron. We also proposed a circuit implementation for the model. Our results indicate that visual neurons leverage spatial selectivity within their RFs to represent multiple spatially separated stimuli. The spatial-location bias in neuronal responses enables individual components of multiple stimuli to be represented by a population of neurons with different spatial preferences, providing a neural substrate for segregating multiple stimuli. Significance StatementElucidating how neurons represent multiple visual stimuli is crucial for understanding the principles and mechanisms of neural coding. We found that the neuronal response in MT to spatially separated moving stimuli can be captured by the well-known normalization model, with an important new extension: the responses elicited by the individual stimulus components are combined and weighted by the neurons spatial preference for single stimuli within its receptive field. Consequently, the response of a neuron to multiple stimuli can be substantially biased toward the stimulus at the neurons preferred spatial location. Our results revealed a previously unknown coding strategy for representing and segregating multiple spatially separated stimuli. Our proposed circuit implementation provides insight into the neural mechanisms underlying spatial preference-weighted normalization.

neuroscience↗

bioRxiv2023

Neural coding of multiple motion speeds in visual cortical area MT

Motion speed provides a salient cue for visual segmentation, yet how the visual system represents and differentiates multiple speeds remains poorly understood. Here, we investigated the neural coding of multiple speeds. First, we characterized the perceptual capacity of human and macaque subjects to segment overlapping random-dot stimuli moving at different speeds. We then recorded from neurons in the middle temporal (MT) cortex of macaque monkeys to determine how multiple speeds are represented. We made a novel finding that the responses of MT neurons to two speeds showed a robust bias toward the faster speed component when both speeds were slow ([≤] 20{degrees}/s). This faster-speed bias emerged early in the neuronal response. It occurred regardless of whether the two speed components moved in the same or different directions, and even when attention was directed away from the receptive field. As stimulus speed increased, the faster-speed bias diminished. Our finding can be explained by a modified divisive normalization model, in which the weights for the speed components are proportional to the responses of a population of neurons, referred to as the weighting pool, elicited by the individual speeds. We suggest that the weighting pool include neurons with a broad range of speed preferences. We found that a classifier can differentiate the responses of MT neurons to two speeds versus a corresponding log-mean speed. We further showed that it was possible to decode two speeds from MT population response, supporting the theoretical framework of coding multiplicity of visual features in neuronal populations. The decoded speeds can account for the perceptual performance of segmenting two speeds with a large (x4) but not a small (x2) separation, raising questions for future investigations. Our findings help define the neural coding rule of multiple speeds. The faster-speed bias in MT at slow stimulus speeds could benefit important behavioral tasks such as figure-ground segregation, as figural objects tend to move faster than the background in the natural environment.

neuroscience↗

bioRxiv2023

The global distribution of paired eddy covariance towers

The eddy covariance technique has revolutionized our understanding of ecosystem-atmosphere interactions. Eddy covariance studies often use a "paired" tower design in which observations from nearby towers are used to understand how different vegetation, soils, hydrology, or experimental treatment shape ecosystem function and surface-atmosphere exchange. Paired towers have never been formally defined and their global distribution has not been quantified. We compiled eddy covariance tower information to find towers that could be considered paired. Of 1233 global eddy covariance towers, 692 (56%) were identified as paired by our criteria. Paired towers had cooler mean annual temperature (mean = 9.9 {degrees}C) than the entire eddy covariance network (10.5 {degrees}C) but warmer than the terrestrial surface (8.9 {degrees}C) from WorldClim 2.1, on average. The paired and entire tower networks had greater average soil nitrogen (0.57-0.58 g/kg) and more silt (36.0-36.4%) than terrestrial ecosystems (0.38 g/kg and 30.5%), suggesting that eddy covariance towers sample richer soils than the terrestrial surface as a whole. Paired towers existed in a climatic space that was more different from the global climate distribution sampled by the entire eddy covariance network, as revealed by an analysis of the Kullback-Leibler divergence, but the edaphic space sampled by the entire network and paired towers was similar. The lack of paired towers with available data across much of Africa, northern, central, southern, and western Asia, and Latin America with few towers in savannas, shrublands, and evergreen broadleaf forests point to key regions, ecosystems, and ecosystem transitions in need of additional research. Few if any paired towers study the flux of ozone and other atmospherically active trace gases at the present. By studying what paired towers measure - and what they do not - we can make infrastructural investments to further enhance the value of FLUXNET as it moves toward its fourth decade.

ecology↗

bioRxiv2019

Spatial arrangement drastically changes the interaction between visual stimuli that compete in multiple feature domains in extrastriate area MT

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.

neuroscience↗