Biology subjects

Gardner, J. L.

Publications and source records attributed to Gardner, J. L..

9 recordsLinked to original sources

bioRxiv2026

A hierarchical computational motif unifies neural dynamics across the ventral visual stream

Neural representations within individual visual cortical areas are dynamic; they evolve over tens to hundreds of milliseconds, even in response to static images. These dynamics have typically been described as area-specific phenomena with unique computational signatures. Here, we show that representational dynamics across the ventral visual stream follow a common motif: representations within each area shift over time along the same complexity axis that organizes hierarchical visual areas. The spatiotemporal signatures of this shift indicate that it is broadly distributed across the neural population, rather than concentrated in specific subpopulations. These shifts are functionally consequential, allowing for the recognition of more complex images over the course of the response. Further, we show evidence in all visual areas of a 30 ms within-area predictivity signal whose properties are consistent with local recurrence, which may be driving the representational shifts. Yet, we show that current state-of-the-art dynamic models, including one with built-in local recurrent processing, fail to recapitulate the measured neural dynamics. Together, these results reveal a common temporal motif across the ventral hierarchy, suggest local recurrence as a possible driver, and provide a concrete dynamic target for future models of the ventral visual stream.

neuroscience↗

bioRxiv2025

Shared computational principles for mouse superior colliculus and primate population orientation selectivity

While the mouse visual system is known to differ substantially from the primate, if the two systems share computational principles, then generalization of results across species may still be possible. One prominent difference is that orientation selectivity is found in mouse superficial superior colliculus (SC), but is not commonly observed in primate SC. Nevertheless, there may be conservation of computational principles if orientation selectivity in mouse superficial SC displays similar properties to primate primary visual cortex (V1), such as invariance to differences in other stimulus dimensions. However, a recent calcium (Ca2+) imaging study revealed a population map for stimulus orientation in mouse superficial SC that changed with stimulus properties such as size, shape and spatial frequency, in apparent contradistinction to computational principles for orientation selectivity in primates. To reconcile mouse and primate mechanisms for orientation selectivity, we constructed computational models of mouse superficial SC populations with fixed, stimulus-invariant receptive fields (RFs) classically used to describe neural RFs in monkey lateral geniculate nucleus (LGN) and V1. At preferred spatial frequencies, model RFs exhibited stronger responses where the aperture and gratings were differently oriented, while at non-preferred frequencies, orientation selectivity reversed, matching the imaging data. We provide an intuitive explanation by visualizing stimulus-RF interactions in the spatial frequency domain. Intrinsically oriented RFs were unnecessary to explain much of the imaging data, but modeling of single units suggests a possible subpopulation of intrinsically orientation-selective cells. In summary, our population modeling approach provides a parsimonious explanation for stimulus-dependent orientation selectivity consistent with well-established results from sensory neurophysiology. More broadly, we provide a population modeling framework for establishing shared computations across species.

neuroscience↗

bioRxiv2023

Limitations of line-scan MRI for directly measuring neural activity

Several groups have reported using 2D line-scan MRI sequences in humans and mice to directly measure neural responses to stimuli (the "DIANA response"). Other groups have been unable to replicate the DIANA response, even with higher field strength and more repetitions. Part of this discrepancy is likely due to a limited understanding of the noise profile of the line-scan MRI sequence: specifically, the consequences of deviations from the assumption of stationarity between each line acquisition. Here, we collected data using an MRI line-scan method while human subjects viewed a blank screen with the purpose of studying noise unique to the acquisition sequence. We found temporal fluctuations in the reconstructed time series from localized groups of voxels that could easily be confused with neural responses to stimuli. These fluctuations were present both in the head and in the surrounding empty volume along the span of the phase-encoding direction from the head. The timing of these fluctuations varied systematically and smoothly along the phase-encoding direction. These features can be explained by a model that accounts for the acquisition sequence and incorporates time-varying contrast fluctuations in the imaging substrate. Using the model, we quantify the amount of cortical- and scan-averaging one might need to reliably distinguish a DIANA response from noise.

neuroscience↗

bioRxiv2023

Effects of ocean acidification on red king crab larval survival and development

Ocean acidification, a decrease in oceanic pH resulting from the uptake of anthropogenic CO2, can be a significant stressor for marine organisms. In this study, we reared red king crab larvae from hatching to the first crab stage in four different pH treatments: current surface ambient, diel fluctuation to mimic larval migration between the surface and mixed layer under current ambient conditions, pH 7.8, and pH 7.5. Larvae were monitored throughout development and the average length of each stage was determined. At each of the zoeal stages, the glaucothoe stage, and the first crab stage, we measured survival, morphometry, dry mass, and carbon, nitrogen, calcium, and magnesium content. Red king crab larvae were highly resilient to ocean acidification. There were no differences among treatments in survival or in average stage length. Although there were clear ontogenetic trends in size, weight, and elemental composition, most of these did not vary with pH treatment. Zoeal morphology did not vary among treatments, although glaucothoe and C1 crabs were slightly smaller in pH 7.8 than in the ambient treatment. Ambient larvae also had a slightly higher mass than pH 7.8 larvae but not pH 7.5. Ambient larvae had higher magnesium contents than pH 7.8 and pH 7.5, but calcium levels were the same. Ambient larvae also had slightly lower carbon and nitrogen content than pH 7.8 and pH 7.5 larvae but only in the 4th zoeal stage. Overall this study suggests that red king crab larvae are well adapted to a wide range of pH conditions and are unlikely to be significantly affected by ocean acidification levels projected for the next two centuries.

ecology↗

bioRxiv2022

Behavioral benefits of spatial attention explained by multiplicative gain, not receptive field shifts, in a neural network model

Attention allows us to focus sensory processing on behaviorally relevant aspects of the visual world. One potential mechanism of attention is a change in the gain of sensory responses. However, changing gain at early stages could have multiple downstream consequences for visual processing. Which, if any, of these effects can account for the benefits of attention for detection and discrimination? Using a model of primate visual cortex we document how a Gaussian-shaped gain modulation results in changes to spatial tuning properties. Forcing the model to use only these changes failed to produce any benefit in task performance. Instead, we found that gain alone was both necessary and sufficient to explain category detection and discrimination during attention. Our results show how gain can give rise to changes in receptive fields which are not necessary for enhancing task performance.

neuroscience↗

bioRxiv2021

Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity. Author summaryConvolutional neural networks (CNNs) are the most quantitatively accurate models of multiple visual areas. In contrast to humans, however, their image classification behaviour can be modified drastically by human-imperceptible image perturbations. To provide insight as to why CNNs are so brittle, we investigated the image features extracted by models that are robust and not robust to these image perturbations. We found that CNNs had a preference for high spatial frequency image features, unlike primary visual cortex (V1) cells. Models that were more robust to image perturbations had a preference for image features more aligned with those extracted by V1 and also improved predictions of neural responses in V1. This suggests that the dependence on high-frequency image features for image classification may be related to the image perturbations affecting models but not humans. Our work is consistent with other findings that CNNs may be relying on image features not aligned with those used by humans for image classification and suggests possible optimization targets to improve the robustness of and the V1 correspondence of CNNs.

neuroscience↗

bioRxiv2021

Unsupervised Models of Mouse Visual Cortex

Studies of the mouse visual system have revealed a variety of visual brain areas that are thought to support a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouses visual cortex, and how it supports a range of behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex and identifying key structural and functional principles underlying that models success. Structurally, we find that a comparatively shallow network structure with a low-resolution input is optimal for modeling mouse visual cortex. Our main finding is functional - that models trained with task-agnostic, self-supervised objective functions based on the concept of contrastive embeddings are much better matches to mouse cortex, than models trained on supervised objectives or alternative self-supervised methods. This result is very much unlike in primates where prior work showed that the two were roughly equivalent, naturally leading us to ask the question of why these self-supervised objectives are better matches than supervised ones in mouse. To this end, we show that the self-supervised, contrastive objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouses limited resources to create a light-weight, general-purpose visual system - in contrast to the deep, high-resolution, and more categorization-dominated visual system of primates. Author summaryStudies of mouse visual behavior have revealed a multitude of visual abilities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. A principled system-wide model of mouse visual cortex would be useful both in providing an organizing theory for this wide spectrum of behaviors, and enabling practical technology for many model-driven studies of mouse neuroscience more broadly. However, the standard approach to creating quantitatively accurate models of primate visual cortex has been less successful with the mouse system. Here we identify critical computational features needed to capture mouse-specific neural stimulus-response patterns, and illustrate how these features can be interpreted as giving the highly resource-limited mouse brain a comparative advantage in self-learning a task-general visual representation.

neuroscience↗

bioRxiv2021

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Human observers use cues to guide visual attention to the most behaviorally relevant parts of the visual world. Cues are often separated into two forms: those that rely on spatial location and those that use features, such as motion or color. These forms of cueing are known to rely on different populations of neurons. Despite these differences in neural implementation, attention may rely on shared computational principles, enhancing and selecting sensory representations in a similar manner for all types of cues. Here we examine whether evidence for shared computational mechanisms can be obtained from how attentional cues enhance performance in estimation tasks. In our tasks, observers were cued either by spatial location or feature to two of four dot patches. They then estimated the color or motion direction of one of the cued patches, or averaged them. In all cases we found that cueing improved performance. We decomposed the effects of the cues on behavior into model parameters that separated sensitivity enhancement from sensory selection and found that both were important to explain improved performance. We found that a model which shared parameters across forms of cueing was favored by our analysis, suggesting that observers have equal sensitivity and likelihood of making selection errors whether cued by location or feature. Our perceptual data support theories in which a shared computational mechanism is re-used by all forms of attention. Significance StatementCues about important features or locations in visual space are similar from the perspective of visual cortex, both allow relevant sensory representations to be enhanced while irrelevant ones can be ignored. Here we studied these attentional cues in an estimation task designed to separate different computational mechanisms of attention. Despite cueing observers in three different ways, to spatial locations, colors, or motion directions, we found that all cues led to similar perceptual improvements. Our results provide behavioral evidence supporting the idea that all forms of attention can be reconciled as a single repeated computational motif, re-implemented by the brain in different neural architectures for many different visual features.

neuroscience↗

bioRxiv2018

A quantitative framework for motion visibility in human cortex

Despite the central use of motion visibility to reveal the neural basis of perception, perceptual decision making, and sensory inference there exists no comprehensive quantitative framework establishing how motion visibility parameters modulate human cortical response. Random-dot motion stimuli can be made less visible by reducing image contrast or motion coherence, or by shortening the stimulus duration. Because each of these manipulations modulates the strength of sensory neural responses they have all been extensively used to reveal cognitive and other non-sensory phenomenon such as the influence of priors, attention, and choice-history biases. However, each of these manipulations is thought to influence response in different ways across different cortical regions and a comprehensive study is required to interpret this literature. Here, human participants observed random-dot stimuli varying across a large range of contrast, coherence, and stimulus durations as we measured blood-oxygen-level dependent responses. We developed a framework for modeling these responses which quantifies their functional form and sensitivity across areas. Our framework demonstrates the sensitivity of all visual areas to each parameter, with early visual areas V1-V4 showing more parametric sensitivity to changes in contrast and V3A and MT to coherence. Our results suggest that while motion contrast, coherence, and duration share cortical representation, they are encoded with distinct functional forms and sensitivity. Thus, our quantitative framework serves as a reference for interpretation of the vast perceptual literature manipulating these parameters and shows that different manipulations of visibility will have different effects across human visual cortex and need to be interpreted accordingly.

neuroscience↗