Biology subjects

van der Meer, M.

Publications and source records attributed to van der Meer, M..

13 recordsLinked to original sources

bioRxiv2026

Taxon-specific differences in C and N cycling and metabolic activity of intertidal organisms: Part B - Long-term processes

Tidal flats cover less than 1% of the surface of our planet, but they provide important ecosystem functions and services, like carbon sequestration and nutrient (re-)mineralization, primary, sec-ondary, and food production, and habitats for a variety of species. Despite their importance, they are threatened by environmental stressors and anthropogenic impacts. Hence, a better under-standing of the interaction between macrozoobenthos, their metabolic activity, and their contri-bution to carbon and nitrogen cycling is important. Therefore, we conducted in-situ pulse-chase tracer experiments at a Pacific oyster (Magallana gigas) reef in the Eastern Scheldt (Southwest Dutch Delta, Netherlands) in summer and autumn 2020. During these experiments, we provided the submerged benthic community with 13C and 15N-enriched bacterioplankton and incubated it in seawater enriched in 1% deuterium oxide (2H2O) for one tidal cycle (~9 h), before the benthic community was kept at in situ conditions for another 14 d. Our aim was (1) to assess seasonal variations in 13C and 15N processing by macrozoobenthos, (2) to evaluate potential life-stage de-pendent differences in 2H uptake - a proxy for metabolic activity, and (3) to identify the macro-zoobenthos taxon which had the highest metabolic turnover rate (MTR). In summer, most bacte-rioplankton-derived 13C- and 15N was taken up by sponges (Halichondria panicea), limpets (Crepidula fornicata), and clams (Ruditapes philippinarum), whereas in autumn, H. panicea, C. fornicata and the sponge Hymeniacidon perlevis dominated 13C and 15N incorporation. Statisti-cally significant differences in 2H uptake between life stages (juvenile/ small vs. adult) was de-tected in Malacostraca (i.e., Rhithropanopeus harrisii), but not in Polychaeta or Bivalvia. There-fore, this difference between taxonomic classes might be linked to mold-intermolt periods. Sea-sonal differences in MTR of H. panicea and M. gigas (i.e., the species with the highest MTRs in summer and autumn) might be linked to seasonal environmental variability, such as temperature and food availability.

ecology↗

bioRxiv2024

Paradoxical replay can protect contextual task representations from destructive interference when experience is unbalanced

Experience replay is a powerful mechanism to learn efficiently from limited experience. Despite decades of compelling experimental results, the factors that determine which experiences are selected for replay remain unclear. A particular challenge for current theories is a set of studies reporting "paradoxical" replay of trajectories that are neither experienced recently or about to be chosen. To understand why, we simulated a feedforward neural network with two regimes: rich learning (structured representations tailored to task demands) and lazy learning (unstructured, task-agnostic representations). Rich, but not lazy, representations degraded following unbalanced experience, an effect that could be reversed with replay of non-chosen options. To test if this computational principle can account for the experimental data, we examined the relationship between paradoxical replay and learned task representations in the rat hippocampus. Across two different studies, we found an association between the richness of learned task representations and the paradoxicality of replay. Taken together, these results suggest that paradoxical replay may serve to protect rich representations from the destructive effects of unbalanced experience, and more generally demonstrate a novel interaction between the nature of task representations and the function of replay in artificial and biological systems. Significance StatementWe provide an explicit normative explanation and simulations of the experimentally observed puzzle of "paradoxical" replay of experiences that are neither experienced recently or about to be chosen. We show computationally that such replay can serve to protect certain task representations from the destructive effects of unbalanced experience. We experimentally confirm the main prediction of the theory, that "rich" task representations, measured using representational distance in the rodent hippocampus, show more paradoxical replay compared to "lazy" task representations. Our theory refines the notion of consolidation in complementary learning systems theory in showing that not all task representations benefit equally from interleaving, and provides an example of how the use of replay in artificial neural networks can be optimized.

neuroscience↗

bioRxiv2023

Human visual cortex and deep convolutional neural network care deeply about object background

Deep convolutional neural networks (DCNNs) are able to predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with electroencephalography (EEG) recordings obtained from 62 human subjects during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation - the ability to distinguish objects from their backgrounds. Therefore, we set out to investigate if figure-ground segregation could explain DCNNs predictive power. Using a stimuli set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category on both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally-controlled backgrounds creates a sufficiently challenging and naturalistic task, while allowing us to retain experimental control. Our results showed that early EEG activity (<100ms) and early DCNN layers represent object background rather than object category. We also found that the predictive power of DCNNs on EEG activity is related to processing of object backgrounds, rather than categories. We provided evidence from both trained and untrained (i.e. random weights) DCNNs, showing figure-ground segregation to be a crucial step prior to the learning of object features. These findings suggest that both human visual cortex and DCNNs rely on the segregation of object backgrounds and target objects in order to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background. Author summaryOur study aimed to investigate the factors contributing to the predictive power of deep convolutional neural networks (DCNNs) on EEG activity in object recognition tasks. We compared the activity of four DCNN architectures with human neural recordings during an object categorization task. We used a stimuli set consisting of identical target objects embedded in different phase-scrambled backgrounds. The distinction between object backgrounds and object categories allows us to investigate the influence of either factor for human subjects and DCNNs. Surprisingly, we found that both human visual processing and early DCNNs layers dedicate a large proportion of activity to processing object backgrounds instead of object category. Furthermore, this shared ability to make object backgrounds (and not just object category) invariant is largely the reason why DCNNs are predictive of brain dynamics in our experiment. We posit this shared ability to be an important solution for object categorization. Finally, we conclude that DCNNs, like humans, care deeply about object backgrounds.

neuroscience↗

bioRxiv2022

A critical test of deep convolutional neural networks ability to capture recurrent processing in the brain using visual masking

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent processing remain unclear. In this paper, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Though ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain electroencephalography (EEG) activity within a visual masking paradigm. Sixty-two humans and fifty artificial agents (10 ResNet models of depths - 4, 6, 10, 18 and 34) completed an object categorization task. We show that deeper networks (ResNet-10, 18 and 34) explained more variance in brain activity compared to shallower networks (ResNet-4 and 6). Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at [~]98ms (from stimulus onset). These early differences indicated that EEG activity reflected pure feedforward signals only briefly (up to [~]98ms). After [~]98ms, deeper networks showed a significant increase in explained variance which peaks at [~]200ms, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans. Significance statementThe challenge of modeling recurrent processes is not trivial and the operationalization of recurrent processing is highly contested. In this paper, we tested the ability of deep residual networks (ResNets) to explain recurrent processes in the human brain. Though ResNets are feedforward networks, they have been shown to equate operations in recurrent neural networks. In this study, we show that deeper networks explained more variance in brain activity than shallower networks. However, all networks still performed far from the noise ceiling. Thus, we conclude that recurrent processing in ResNets captures a form of recurrent processing in humans though other types of recurrent processing (inhibition, multiplicative) that are not present in current regular deep neural networks (alexnet, cornet, resnet) are necessary for building better visual models.

neuroscience↗

bioRxiv2021

Contextual gating of motivationally-relevant stimuli in the mouse nucleus accumbens

Neural activity in the nucleus accumbens (NAc) is thought to track fundamentally value-centric quantities such as current or future expected reward, reward prediction errors, the value of work, opportunity cost, and approach vigor. However, the NAc also contributes to flexible behavior in ways that are difficult to explain based on value signals alone, raising the question of if and how non-value signals are encoded in NAc. We recorded NAc neural ensembles while head-fixed mice performed a biconditional discrimination task where context-setting cues modulated the stimulus-outcome association of subsequently presented reward-predictive cues. We extracted single-unit and population-level correlates of task features, and found value-independent coding for the context cues. This context signal occupied a subspace orthogonal to outcome-predictive representations, and was predictive of subsequent behaviorally-relevant coding. Together, these findings support a circuit-level gating model for how the NAc contributes to behavioral flexibility and provide a novel population-level perspective from which to view NAc computations.

neuroscience↗

bioRxiv2020

On the necessity of recurrent processing during object recognition: it depends on the need for scene segmentation

While feed-forward activity may suffice for recognizing objects in isolation, additional visual operations that aid object recognition might be needed for real-world scenes. One such additional operation is figure-ground segmentation; extracting the relevant features and locations of the target object while ignoring irrelevant features. In this study of 60 participants, we show objects on backgrounds of increasing complexity to investigate whether recurrent computations are increasingly important for segmenting objects from more complex backgrounds. Three lines of evidence show that recurrent processing is critical for recognition of objects embedded in complex scenes. First, behavioral results indicated a greater reduction in performance after masking objects presented on more complex backgrounds; with the degree of impairment increasing with increasing background complexity. Second, electroencephalography (EEG) measurements showed clear differences in the evoked response potentials (ERPs) between conditions around 200ms - a time point beyond feed-forward activity and object decoding based on the EEG signal indicated later decoding onsets for objects embedded in more complex backgrounds. Third, Deep Convolutional Neural Network performance confirmed this interpretation; feed-forward and less deep networks showed a higher degree of impairment in recognition for objects in complex backgrounds compared to recurrent and deeper networks. Together, these results support the notion that recurrent computations drive figure-ground segmentation of objects in complex scenes.

neuroscience↗

bioRxiv2020

Reconstructing the diet, trophic level, and migration pattern of Mysticete whales based on baleen isotopic composition.

Baleen from mysticete whales is a well-preserved proteinaceous material that can be used to identify migrations and feeding habits for species whose migration pathways are unknown. Analysis of {delta}13C and {delta}15N from bulk baleen has been used to infer migration patterns for individuals. However, this approach has fallen short of identifying migrations between regions as it is difficult to determine variations in isotopic shifts without temporal sampling of prey items. Here we apply analysis of {delta}15N values of amino acids to five baleen plates belonging to three species, revealing novel insights on trophic position, metabolic state, and migration between regions. Humpback and minke whales had higher reconstructed trophic levels than fin whales (3.4-3.5 versus 2.7-2.9, respectively) as expected due to different feeding specialization. Isotopic niche areas between baleen minima and maxima were well separated, indicating regional resource use for individuals during migration that aligned with isotopic gradients in Atlantic Ocean particulate organic matter. {delta}15N values from phenylalanine confirmed regional separation between the niche areas for two fin whales as migrations occurred and elevated glycine and threonine {delta}15N values revealed physiological changes due to fasting. Simultaneous resolution of trophic level and physiological changes allow for identification of regional migrations in mysticetes.

ecology↗

bioRxiv2020

Impacts of host phylogeny, feeding styles, and parasite attachment site on isotopic discrimination in helminths infecting coral reef fish hosts.

Stable isotopes of carbon and nitrogen characterize trophic relationships in predator-prey relationships, with clear differences between consumer and diet (discrimination factor, {Delta}13C, {Delta}15N). However, parasite-host isotopic relationships remain unclear, with {Delta}13C and {Delta}15N remaining incompletely characterized, especially for helminths. In this study, we used stable isotopes to determine discrimination factors for 13 parasite-host pairings of helminths in coral reef fish. {Delta}15N differences grouped according to phylogeny and attachment site on the hosts: {Delta}15N was positive for trematodes and nematodes from the digestive tract and varied for cestodes and nematodes from the general cavity. {Delta}13C showed more complex patterns with no effect of phylogeny or attachment site. A negative relationship was observed between {Delta}15N and host {delta}15N value among different host-parasite pairings as well as within 7 out of the 13 parings, indicating that host metabolic processing affects host-parasite discrimination values. In contrast, no relationships were observed for {Delta}13C. Our results indicate that host phylogeny, attachment site and host stable isotope value drive {Delta}15N of helminths in coral reef fish while {Delta}13C is more idiosyncratic. These results call for use of taxon- or species-specific and scaled framework for bulk stable isotopes in the trophic ecology of parasites.

ecology↗

bioRxiv2020

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns - or "statistical ethograms" - are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.

animal behavior and cognition↗

bioRxiv2019

Practical considerations for improved reliability and precision during compound specific analysis of δ15N in amino acids using a single combined oxidation-reduction reactor.

RATIONALEThere has been increased interest in the analysis for {delta}15N in amino acids to gain simultaneous insight into both trophic relationships and source producers within ecosystems. New developments in gas chromatography combustion isotope ratio mass spectrometry equipment has led to variable outcomes in performance due to limited information about best practices for new systems.\n\nMETHODSPrecision for {delta}15N in amino acids using the single combined oxidation-reduction reactor is improved across a sequence of analyses if the reactor is oxidized for a substantial period (2 h), immediately followed with a conditioning run of alkanes prior to analysis for N, and the liquid N2 CO2 trap is left immersed throughout. A five point calibration curve using amino acids with a range of {delta}15N values from -2.4{per thousand} to +61.5{per thousand} was used in combination with a 13 amino acid mixture to correct for offsets during derivatization.\n\nRESULTSCombining the improved setup with normalization techniques using both internal and external standards allows for a reliable throughput of ~25 samples per week. It allowed for a reproducible level of error of <{+/-}0.5{per thousand} within standards repeated 10 times across each sequence and a sample error of ({+/-}0.18{per thousand}), which is lower than analytical error typically associated with {delta}15N-amino acid analysis ({+/-}1{per thousand}).\n\nCONCLUSIONSA few practical considerations regarding oxidation and conditioning of the combustion reactor allow for increased sequence capacity with the single combined oxidation-reduction reactor. These considerations combined with normalization techniques result in a higher throughput and reduced analytical error during analysis of {delta}15N in amino acids.

ecology↗

bioRxiv2019

A comprehensive characterization of rhythmic spiking activity in the rat ventral striatum

The ventral striatum (vStr) is anatomically interconnected with brain structures that exhibit prominent rhythmic activity, suggesting that oscillations in ventral striatal activity are potentially informative about systems-level interactions between these structures. However, rhythmic activity in ventral striatal neurons during behavior has only been characterized piecemeal, with individual studies focusing on a single cell type or frequency band. We performed a comprehensive analysis of (1) rhythmic activity in vStr neurons without reference to the local field potential, and (2) average as well as time-resolved spike-field relationships. Spike train rhythmicity tended to be limited to low frequencies such as delta and theta, whereas spike-field relationships were seen across a broad spectrum of frequencies, with about 90% of neurons showing spike-field locking to at least one rhythm. Using a novel time-resolved generalized linear model approach, we further show that the contribution of local field potential (LFP) phase to spike timing is dynamic over time, and enhanced by the inclusion of the LFP from the hippocampus - a new measure of inter-area coupling. These results provide a foundation for a more accurate interpretation of the ventral striatal LFP, suggest the possibility of an oscillatory taxonomy of ventral striatal neurons, and provide a starting point for understanding how rhythmic activity links cell-, circuit-, and systems-level phenomena in the ventral striatum.\n\nSignificance StatementOscillations in neural activity are ubiquitous in the brain, readily accessible in the clinic and the lab, and shared by humans and animals to facilitate translational work. The ventral striatum (vStr) is a promising target structure for such a rhythmic activity perspective, not in the least because its local field potential (LFP) shows prominent task-related oscillations across a range of frequencies. However, recent work has shown that major components of the vStr LFP are in fact generated elsewhere in the brain, raising the question of how the LFP relates to local spiking activity. Unlike previous studies that focused on a specific cell type or frequency band of interest, we characterize rhythmic activity across a full range range of frequencies and cell types, and include novel analyses appropriate for a non-local LFP. Our results provide a foundation for more accurate interpretation of the vStr LFP and a starting point for an oscillatory taxonomy of vStr neurons.

neuroscience↗

bioRxiv2018

Reward revaluation biases hippocampal sequence content away from the preferred outcome

Internally generated sequences of place cell activity can depict spatial trajectories to reward locations, suggesting a role in model-based behavioral control. A largely separate literature emphasizes reward revaluation as the litmus test for such control, yet the content of hippocampal sequences under revaluation conditions is unknown. We report that the content of sharp wave-ripple hippocampal sequences is biased away from the preferred outcome following reward revaluation, challenging the idea that such sequences reflect recent experience or trajectories toward a goal.\n\nAcknowledgmentsWe thank Nancy Gibson, Martin Ryan and Jean Flanagan for animal care and MinChing Kuo, Julia Espinosa and Eric Carmichael for technical assistance. We thank Elyot Grant for developing the SWR detection method used for the main analyses in this paper. This work was supported by the University of Waterloo and Dartmouth College (start-up funds to MvdM), and the Netherlands Organization for Scientific Research (NWO, grant 863.10.013 to MvdM).\n\nAuthor contributionsAAC performed experiments and pre-processed the data. AAC, YT and MvdM wrote analysis code. AAC and MvdM performed data analysis. MvdM wrote the paper with comments from AAC and YT.\n\nConflict of InterestThe authors declare no competing financial interests.

neuroscience↗

bioRxiv2018

Persistent coding of outcome-predictive cue features in the rat nucleus accumbens

The nucleus accumbens (NAc) is important for learning from feedback, and for biasing and invigorating behavior in response to cues that predict motivationally relevant outcomes. NAc encodes outcome-related cue features such as the magnitude and identity of reward. However, little is known about how features of cues themselves are encoded. We designed a decision making task where rats learned multiple sets of outcome-predictive cues, and recorded single-unit activity in the NAc during performance. We found that coding of cue identity and location occurred alongside coding of expected outcome. Furthermore, this coding persisted both during a delay period, after the rat made a decision and was waiting for an outcome, and after the outcome was revealed. Encoding of cue features in the NAc may enable contextual modulation of ongoing behavior, and provide an eligibility trace of outcome-predictive stimuli for updating stimulus-outcome associations to inform future behavior.

neuroscience↗