Biology subjects

Palmer, S. E.

Publications and source records attributed to Palmer, S. E..

15 recordsLinked to original sources

bioRxiv2026

Interaction forces reflect the perception of texture during active exploration

AO_SCPLOWBSTRACTC_SCPLOWWe are constantly exploring the world around us through touch. Active touch depends on coordination of movement and force, yet the forces used during natural exploration and their relationship with perception is largely unexplored. Here we measured exploration forces together with fingertip motion while 17 participants explored 14 textures and rated their perceived hardness, slipperiness, or roughness. Exploration strategies differed systematically across tasks: hardness judgments involved relatively stationary pressing with larger and more variable normal forces, whereas slipperiness and roughness judgments relied more on sweeping movements. Within tasks, interaction forces covaried with perceptual ratings: harder textures elicited larger maximum tangential forces, consistent with diagonal pressing, while more slippery textures were explored with faster fingertip motion and lower forces. Estimated dynamic friction was negatively correlated with perceived slipperiness but not roughness. At the same time perceived roughness was strongly related to vibrations in the force, indicating distinct physical bases for these perceptual dimensions. These results show that humans actively tailor contact mechanics to perceptual goals during active exploration, supporting a sensorimotor account of texture perception. SO_SCPLOWIGNIFICANCEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSTATEMENTC_SCPLOWTouch is usually studied as if the skin passively receives information, but in everyday life we actively move and press against objects to recognize them and learn what they feel like. This study measured both fingertip motion and contact forces while people freely explored textures and judged hardness, slipperiness, and roughness - the three of the most salient dimensions of tactile experience. The results show that people adjust how they move and press depending on what they want to perceive, and that different physical signals - friction and vibration - support different texture judgments. This work helps explain touch as an active sensorimotor process, with implications for neuroscience, haptics, robotics, and neuroprosthetics.

neuroscience↗

bioRxiv2025

Coarse-graining reveals collective predictive information in a sensory population

Biological systems perform complex computations using hundreds of individual actors, but they do so efficiently and in a way that can be read out and interpreted by other biological networks. Coarsegraining may allow for key collective features to be effectively and efficiently communicated. In the brain, early sensory systems perform prediction, which can compensate for lags in neural processing. This computation is collective, meaning it relies upon interactions between many neurons, and operates in complex, dynamic natural environments. Taking these two facets of biological complexity together, we search for maximally-predictive collective variables in large groups of retinal ganglion cells responding to dynamic natural visual scenes. To find collective variables that best capture predictive computations in the neurons, we apply a tractable, approximate implementation of the information bottleneck method to neural data. We infer a lower-dimensional representation that is maximally informative about the future neural activity. We observe scaling relationships between this mutual information estimate, neural subset size, and information decay timescale. Further, the structure of collective modes changes for predicting at short versus longer timescales.

neuroscience↗

bioRxiv2025

Preserving predictive information under biologically plausible compression

Retinal ganglion cells (RGCs) show high convergence onto their downstream projections, which poses a problem for information transfer: how can information be preserved through a synaptic layer that has significantly more inputs than outputs? Lossy compression suggests many efficient, yet computation-agnostic, methods for reading out input stimuli or activity patterns. Focusing on prediction as a ubiquitous computation in the brain, we compare compressions that explicitly retain predictive information to common neural compression frameworks that do not. We find evidence that downstream areas may compress their retinal inputs in a way that allows them to perform optimal predictive computations across many natural scenes. Other sensory systems also exhibit compression in their processing hierarchies, such as at the glomeruli stage in the olfactory system, and we hope that our framework will be useful in cases where it is not yet known how information about a specific computation is maintained under compression. SIGNIFICANCE STATEMENTProducing successful behavior, such as escaping predators, requires the visual system to overcome significant sensory processing delays by predicting the future state of the world. Neurons in the eye capture some of the most predictive features of visual information, but this information must be accessible to downstream areas that receive synaptically compressed inputs from the retina. We tested how carefully retinal activity must be compressed to preserve predictive information in natural scenes. Biologically plausible compressions that are agnostic to prediction can preserve substantial future information, but only compression optimized for this task extracts generalizable motifs that allow it to predict in any natural scene. This suggests that downstream circuits may optimally compress their inputs specifically for the task of prediction.

neuroscience↗

bioRxiv2024

Perceptual Resolution of Ambiguity: Can Tuned, Divisive Normalization Account for both Interocular Similarity Grouping and Difference Enhancement

Our visual system usually provides a unique and functional representation of the external world. At times, however, the visual system has more than one compelling interpretation of the same retinal stimulus; in this case, neural populations compete for perceptual dominance to resolve ambiguity. Spatial and temporal context can guide perceptual experience. Recent evidence shows that ambiguous retinal stimuli are sometimes resolved by enhancing either similarity or differences among multiple percepts. Divisive normalization is a canonical neural computation that enables context-dependent sensory processing by attenuating a neurons response by other neurons. Experiments here show that divisive normalization can account for perceptual representations of either similarity enhancement (so-called grouping) or difference enhancement, offering a unified framework for opposite perceptual outcomes.

neuroscience↗

bioRxiv2023

Synchrotron-source micro-x-ray computed tomography for examining butterfly eyes

1. Comparative anatomy is an important tool for investigating evolutionary relationships amongst species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. 2. We describe a method using synchrotron source micro-x-ray computed tomography (syn-{micro}XCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of [~] 15 min/mm3 at 600 nm3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species independent manner thus removing any barrier to imaging any species of interest. 3. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. 4. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

ecology↗

bioRxiv2023

A dynamic scale-mixture model of motion in natural scenes

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

neuroscience↗

bioRxiv2023

Fast or slow? Clock readout sets internal periods

Circadian clocks are essential for the function of a wide array of organisms, from cyanobacteria to humans. Despite decades of productive study of these systems, mysteries remain, including why many biological clocks have non-24-hour internal periods. We take a new approach to circadian clocks by focusing on downstream readout of the clocks state to answer physiologically relevant questions, such as when will the sun rise? Using this framework, we show that systematic errors arising from sunrise and sunset prediction can be compensated by having non-24 hour internal periods. We show that this prediction holds in models of cyanobacterial circadian clocks. Finally, we predict latitude-dependant qualitative changes in circadian clock structure and the performance of different clock phenotypes in common laboratory experimental setups.

biophysics↗

bioRxiv2023

The double-drift illusion biases the marmoset oculomotor system

The double-drift illusion has two unique characteristics: the error between the perceived and physical position of the stimulus grows over time, and saccades to the moving target land much closer to the physical than the perceived location. These results suggest that the perceptual and saccade targeting systems integrate visual information over different timescales. Functional imaging studies in humans have revealed several potential cortical areas of interest, including the prefrontal cortex. However, we currently lack an animal model to study the neural mechanisms of location perception that underlie the double-drift illusion. To fill this gap, we trained two marmoset monkeys to fixate and then saccade to the double drift stimulus. In line with human observers for radial double-drift trajectories, we find that saccade endpoints do show a significant bias that is, as it is in humans, smaller than that seen in perception. This bias is modulated by changes in the external and internal speeds of the stimulus. These results demonstrate that the saccade targeting system of the marmoset monkey is influenced by the double-drift illusion.

animal behavior and cognition↗

bioRxiv2022

Heavy-tailed neuronal connectivity arises from Hebbian self-organization

In networks of neurons, the connections are heavy-tailed, with a small number of neurons connected much more strongly than the vast majority of pairs.1-6 Yet it remains unclear whether, and how, such heavy-tailed connectivity emerges from simple underlying mechanisms. Here we propose a minimal model of synaptic self-organization: connections are pruned at random, and the synaptic strength rearranges under a mixture of Hebbian and random dynamics. Under these generic rules, networks evolve to produce scale-free distributions of connectivity strength, with a power-law exponent [Formula] that depends only on the probability p of Hebbian (rather than random) growth. By extending our model to include correlations in neuronal activity, we find that clustering--another ubiquitous feature of neuronal networks6-9--also emerges naturally. We confirm these predictions in the connectomes of several animals, suggesting that heavy-tailed and clustered connectivity may arise from general principles of self-organization, rather than the biophysical particulars of individual neural systems.

neuroscience↗

bioRxiv2022

Genetic and peripheral visual system changes underlie evolving butterfly mate preference

Many studies have linked genetic variation to behavior, but less is known about how that variation alters the neural circuits that drive behavior. We investigated the genetic and neurobiological basis of courtship preference variation in Heliconius butterflies, which use vision to identify appropriate mates based on wing color patterns. We found that Heliconius cydno preference variation was strongly associated with genetic variation and differential expression of senseless-2, a gene predominantly expressed in the eye. Further measurements of photoreceptor sensitivities revealed differences in inter-photoreceptor inhibition of ultraviolet-sensitive cells corresponding to courtship preference variation. Our results reveal a genetic basis for preference/cue co-evolution, suggest a link between sens-2 and visual system variation, and support the idea that changing peripheral neural computations can significantly alter essential behaviors. SummaryGenetic and expression variation of senseless-2 and inter-photoreceptor inhibition predict visual mate preference in a clade of diverse butterflies.

evolutionary biology↗

bioRxiv2022

Sex-limited diversification of the eye in Heliconius butterflies

Butterflies have evolved an immense diversity in eye organization to support a range of vision-based behaviors including courtship, oviposition, and foraging. This diversity has been surveyed extensively across the butterfly phylogeny, and here we take a complementary approach to characterize the eye within a group of closely related Heliconius butterflies. Using a combination of immunostaining for different opsins and eyeshine for determining the distribution of light-filtering screening pigments, we identified several sexually dimorphic features of eye organization where male eyes varied and female eyes did not. Ultraviolet (UV) sensitive photoreceptors varied in which of two UV opsins were expressed, including co-expression of both within single photoreceptors, and these differences were consistent with a role in courtship and conspecific identification. Additional differences across species and sex included the distribution of three ommatidial types defined by the expression pattern of UV and blue opsins, the distribution of a red screening pigment, and which ommatidial types expressed the red screening pigment. We hypothesize that female eyes are optimized for a dimorphic behavior such as oviposition, while male eyes adapt to other selective pressures such as the local light environment.

neuroscience↗

bioRxiv2020

Optimal prediction with resource constraints using the information bottleneck

Responding to stimuli requires that organisms encode information about the external world. Not all parts of the signal are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position coordinates prove more predictive. We identify the properties of global, transferrable strategies for time-varying stimuli. For non-Markovian dynamics we explore the role of long-term memory of the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories, revealing a very different prediction strategy from motion prediction.

biophysics↗

bioRxiv2019

Ancestral neural computations constrain the evolution of novel computations in simulated color vision networks

Efficient coding has been a successful organizational principle in neuroscience, but a more general theoretical framework needs to include the capacity for biological constraints to impede the realization of optimal circuit design. Here, we explore how evolution shapes the computational organization of a circuit using color vision as a model system. Taking a theoretical, machine learning approach allowed us to simulate the evolution of tetrachromatic color vision from a trichromatic ancestor both within and across distinct phylogenetic lineages. Analyzing network performance showed that trichromatic starting weights impose a significant constraint on learning rate, although the incremental increase in input layer complexity leads to better overall performance. Analyzing hidden layer computations showed that ancestry severely constrained evolution into a restricted and predictable portion of the theoretically available computational state space. Overall, our simulations of color vision evolution suggest that phylogenetic history is an important aspect of the functional organization of neural circuits.

neuroscience↗

bioRxiv2019

Nonlinear mixed selectivity supports reliable neural computation

Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinear mixed selectivity (NMS) has been observed widely across the brain, from primary sensory to decision-making to motor areas. Representations of stimulus features are nearly always mixed together, rather than represented separately or with only additive (linear) mixing, as in pure selectivity. NMS has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that NMS has another important benefit: it requires as little as half the metabolic energy required by pure selectivity to achieve the same level of transmission reliability. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that NMS exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that NMS may be a general coding scheme exploited by the brain for reliable and efficient neural computation.

neuroscience↗

bioRxiv2017

HIV persistence during antiviral therapy occurs due to cellular proliferation

Antiretroviral therapy (ART) suppresses viral replication in people living with HIV. Yet, infected cells persist for decades on ART and viremia returns if ART is stopped. Persistence has been attributed to viral replication in an ART sanctuary and long-lived and1or proliferating latently infected cells. Using ecological methods and existing data, we infer that >99% of infected cells are members of clonal populations after one year of ART. We reconcile our results with observations from the first months of ART, demonstrating mathematically how a \"fossil record\" of historic HIV replication permits observed viral evolution even while most new infected cells arise from proliferation. Together, our results imply cellular proliferation generates a majority of infected cells during ART. Therefore, reducing proliferation could decrease the size of the HIV reservoir and help achieve a functional cure.

immunology↗