scholarly journals A realistic locomotory model of Drosophila larva for behavioral simulations.

2021 ◽  
Author(s):  
Panagiotis Parthenios Sakagiannis ◽  
Anna-Maria Jürgensen ◽  
Martin Paul Nawrot
Keyword(s):  
Neural Circuit ◽  
Population Level ◽  
Individual Variability ◽  
Behavioral Experiments ◽  
Preference Testing ◽  
Free Exploration ◽  
Autonomous Behavior ◽  
Single Animal ◽  
Drosophila Larva ◽  
Concentration Changes

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and top-down modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Inter-individual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

Biliary transport of glutathione and methylmercury

1983 ◽  
Vol 244 (4) ◽  
pp. G435-G441 ◽  
Author(s):  
N. Ballatori ◽  
T. W. Clarkson
Keyword(s):  
Bile Flow ◽  
Individual Variability ◽  
Secretion Rate ◽  
Female Rats ◽  
Biliary Secretion ◽  
Male And Female ◽  
Male And Female Rats ◽  
Sodium Dehydrocholate ◽  
Bile Fluid ◽  
Concentration Changes

The hepatobiliary transport of glutathione (GSH) and methylmercury (MM) was investigated in male and female rats anesthetized with pentobarbital sodium. When bile flow was altered with either sodium dehydrocholate (DHC), hypertonic sucrose infusion, or by hypothermia, the absolute rates of GSH and MM secretion into bile were not affected, resulting in parallel concentration changes in the bile fluid for both GSH and MM. Indocyanine green and sulfobromophthalein (BSP), but not BSP-glutathione complex, inhibited the biliary secretion of free GSH. This inhibition was accompanied by a parallel inhibition of MM secretion into bile and occurred without any changes in liver GSH or MM levels. On the other hand, the intravenous administration of cysteine, GSH, and penicillamine was associated with an increase in the secretion rate of reduced sulfhydryl groups into bile and an increase in the biliary secretion rate of MM. The increased biliary secretion rate of MM after phenobarbital pretreatment was also associated with an increased rate of secretion of GSH into bile. In addition, sex differences and individual variability in the biliary secretion of MM were correlated with differing abilities to secrete GSH into bile. The results suggest the presence of a biliary transport system for GSH that determines the biliary secretion of MM.

scholarly journals Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

2016 ◽  
Vol 114 (2) ◽  
pp. 394-399 ◽  
Author(s):  
John D. Murray ◽  
Alberto Bernacchia ◽  
Nicholas A. Roy ◽  
Christos Constantinidis ◽  
Ranulfo Romo ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Population Level ◽  
Population Coding ◽  
Neural Dynamics ◽  
Stable Population ◽  
Neuron Activity ◽  
Delayed Response

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

Patterns of metamorphic age and length in marine fishes, from individuals to taxa

2000 ◽  
Vol 57 (4) ◽  
pp. 856-869 ◽  
Author(s):  
Hugues P Benoît ◽  
Pierre Pepin ◽  
Joseph A Brown
Keyword(s):  
Growth Rate ◽  
Population Level ◽  
Individual Variability ◽  
Marine Fishes ◽  
Exponential Relationship ◽  
Factors Affecting ◽  
The Mean ◽  
Time Required ◽  
Increasing Temperature ◽  
Dominant Influence

We present a summary of variability in age and length at metamorphosis for marine fishes. Data from the literature were partitioned into taxonomic, population, and individual levels of resolution to examine the factors affecting the timing of metamorphosis. Temperature appears to be a dominant influence on timing, likely due to its effect on growth rate. Interspecifically, length at metamorphosis correlated poorly with that at hatching but was significantly related to temperature. This pattern was inconsistent for population-level comparisons. Metamorphic age decreased exponentially with increasing temperature in interspecific and population-level comparisons but did not covary with length for either level of resolution. This suggests that age at metamorphosis largely reflects the time required to grow to a given metamorphic length. Within populations, the correlation between metamorphic age and length increases with growth rate, a reflection of variance in age and length. A strong exponential relationship between mean metamorphic age and length and their associated variability (SD) exists, with a slope greater than unity in both cases (i.e., variability increases relative to the mean). With these relationships, we can infer the manner in which individual variability in metamorphic traits is generated throughout ontogeny. These results are considered in light of recruitment variability in marine fishes.

The Golden Section and the Aesthetics of Form and Composition: A Cognitive Model

1997 ◽  
Vol 15 (2) ◽  
pp. 209-232 ◽  
Author(s):  
I. C. McManus ◽  
P. Weatherby
Keyword(s):  
Individual Differences ◽  
Fuzzy Set ◽  
Golden Section ◽  
Cognitive Model ◽  
Population Level ◽  
Individual Variability ◽  
Large Individual ◽  
Powerful Method ◽  
Preference Functions ◽  
Geometric Objects

Previous work on the aesthetics of simple figures such as rectangles and triangles, as well as on the aesthetics of color, suggests that although there are clear population level preferences, there are also large individual differences which are temporally stable, and which any adequate theoretical analysis must take into account. Data presented here show similar phenomena in a related problem in composition—where to place an object within the frame of a picture to produce the optimal aesthetic effect. A novel and powerful “method of randomized paired comparisons” first showed that there are overall population level preferences, with objects being placed preferentially at the two golden sections horizontally, and between the two golden sections vertically. As in the studies of simple figures and colors, there are large individual differences. A cognitive model of “sensory aesthetics” is proposed in which continua of any type (space, geometric objects, colors, or whatever), are described categorically, usually in terms of words such as “square,” “rectangle,” “line,” etc., each of which is a fuzzy set. Preference functions are then derived from the union and intersection of the fuzzy set functions, which differ between individuals as their categories differ or as they prefer objects which are prototypical, or are at the boundaries between prototypes. There is therefore wide inter-individual variability.

scholarly journals Influence of HLA Class II Polymorphism on Predicted Cellular Immunity Against SARS-CoV-2 at the Population and Individual Level

2021 ◽  
Vol 12 ◽  
Author(s):  
Hannah C. Copley ◽  
Loren Gragert ◽  
Andrew R. Leach ◽  
Vasilis Kosmoliaptsis
Keyword(s):  
Cellular Immunity ◽  
Experimental Studies ◽  
Population Level ◽  
Class Ii ◽  
Individual Variability ◽  
Hla Class Ii ◽  
Hla Polymorphism ◽  
Individual Level ◽  
Hla Genotype

Development of adaptive immunity after COVID-19 and after vaccination against SARS-CoV-2 is predicated on recognition of viral peptides, presented on HLA class II molecules, by CD4+ T-cells. We capitalised on extensive high-resolution HLA data on twenty five human race/ethnic populations to investigate the role of HLA polymorphism on SARS-CoV-2 immunogenicity at the population and individual level. Within populations, we identify wide inter-individual variability in predicted peptide presentation from structural, non-structural and accessory SARS-CoV-2 proteins, according to individual HLA genotype. However, we find similar potential for anti-SARS-CoV-2 cellular immunity at the population level suggesting that HLA polymorphism is unlikely to account for observed disparities in clinical outcomes after COVID-19 among different race/ethnic groups. Our findings provide important insight on the potential role of HLA polymorphism on development of protective immunity after SARS-CoV-2 infection and after vaccination and a firm basis for further experimental studies in this field.

scholarly journals Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

PLoS Biology ◽  
2021 ◽  
Vol 19 (5) ◽  
pp. e3001009
Author(s):  
Takuji Usui ◽  
Malcolm R. Macleod ◽  
Sarah K. McCann ◽  
Alistair M. Senior ◽  
Shinichi Nakagawa
Keyword(s):  
Interindividual Variability ◽  
Reference Group ◽  
Infarct Volume ◽  
Meta Analysis ◽  
Methodological Approach ◽  
Population Level ◽  
Individual Variability ◽  
Disease State ◽  
Preclinical Research ◽  
High Interindividual Variability

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as “heterogenization” of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research.

scholarly journals Bidirectional modulation of pain-related behaviors in the zona incerta

2021 ◽  
Author(s):  
Sudhuman Singh ◽  
Spring Valdivia ◽  
Omar Soler-Cedeño ◽  
Anisha P. Adke ◽  
Barbara Benowitz ◽  
...  
Keyword(s):  
Nerve Injury ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Zona Incerta ◽  
Pain Processing ◽  
Ventral Region ◽  
Behavioral Experiments ◽  
Efferent Pathway ◽  
Kinase C ◽  
Bidirectional Modulation

AbstractCentral amygdala neurons expressing protein kinase C-delta (CeA-PKCδ) are sensitized following nerve injury and promote pain-related responses in mice. The neural circuits underlying modulation of pain-related behaviors by CeA-PKCδ neurons, however, remain unknown. In this study, we identified a functional monosynaptic inhibitory neural circuit that originates in CeA-PKCδ neurons and terminates in the ventral region of the zona incerta (ZI), a subthalamic structure previously linked to pain processing. Behavioral experiments further show that chemogenetic inhibition of GABAergic ZI neurons is sufficient to induce bilateral hypersensitivity in uninjured mice as well as contralateral hypersensitivity after nerve injury. In contrast, chemogenetic activation of GABAergic ZI neurons reverses nerve injury-induced hypersensitivity, demonstrating that silencing of the ZI is required for injury-induced behavioral hypersensitivity. Our results identify a previously unrecognized inhibitory efferent pathway from CeA-PKCδ neurons to the ZI and demonstrate that ZI-GABAergic neurons can bidirectionally modulate pain-related behaviors in mice.

scholarly journals A generative modeling approach for interpreting population-level variability in brain structure

2020 ◽  
Author(s):  
Ran Liu ◽  
Cem Subakan ◽  
Aishwarya H. Balwani ◽  
Jennifer Whitesell ◽  
Julie Harris ◽  
...  
Keyword(s):  
Brain Structure ◽  
Latent Variables ◽  
Population Level ◽  
Individual Variability ◽  
Neural Structure ◽  
Modeling Approach ◽  
Structured Perturbations ◽  
Generative Modeling ◽  
Latent Space ◽  
Low Dimensional

AbstractUnderstanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. To do this, we start by training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25 micron resolution. To then tap into the learned factors and validate the model’s expressiveness, we developed a novel bi-directional technique to interpret the latent space–by making structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space to provide insights into the representations of brain structure formed in deep neural networks.

scholarly journals Reactivation of reward-related patterns from single past episodes supports memory-based decision making

2015 ◽  
Author(s):  
G. Elliott Wimmer ◽  
Christian Buechel
Keyword(s):  
Decision Making ◽  
Financial Risk ◽  
Memory Performance ◽  
Individual Variability ◽  
Test Phase ◽  
Separate Experiment ◽  
Behavioral Experiments ◽  
High Reward ◽  
Neuroscience Research ◽  
Past Experiences

Rewarding experiences exert a strong influence on later decision making. While decades of neuroscience research have shown how reinforcement gradually shapes preferences, decisions are often influenced by single past experiences. Surprisingly, relatively little is known about the influence of single learning episodes. While recent work has proposed a role for episodes in decision making, it is largely unknown whether and how episodic experiences contribute to value-based decision making and how the values of single episodes are represented in the brain. In multiple behavioral experiments and an fMRI experiment, we tested whether and how rewarding episodes could support later decision making. Participants experienced episodes of high reward or low reward in conjunction with incidental, trial-unique neutral pictures. In a surprise test phase, we found that participants could indeed remember the associated level of reward, as evidenced by accurate source memory for value and preferences to re-engage with rewarded objects. Further, in a separate experiment, we found that high reward objects shown as primes before a gambling task increased financial risk-taking. Neurally, re-exposure to objects in the test phase led to significant reactivation of reward-related patterns. Importantly, individual variability in the strength of reactivation predicted value memory performance. Further, local searchlight analyses identified significant reactivation in the ventromedial PFC. Our results provide a novel demonstration that affect-related neural patterns are reactivated during later experience. Reactivation of value information represents a mechanism by which memory can guide decision making.

scholarly journals Dynamical patterns underlying response properties of cortical circuits

2018 ◽  
Vol 15 (140) ◽  
pp. 20170960 ◽  
Author(s):  
Adam Keane ◽  
James A. Henderson ◽  
Pulin Gong
Keyword(s):  
Neural Circuit ◽  
Experimental Studies ◽  
Population Level ◽  
Wave Pattern ◽  
External Stimulus ◽  
Response Patterns ◽  
Stimulus Strength ◽  
Population Response ◽  
Population Activity ◽  
Response Properties

Recent experimental studies show cortical circuit responses to external stimuli display varied dynamical properties. These include stimulus strength-dependent population response patterns, a shift from synchronous to asynchronous states and a decline in neural variability. To elucidate the mechanisms underlying these response properties and explore how they are mechanistically related, we develop a neural circuit model that incorporates two essential features widely observed in the cerebral cortex. The first feature is a balance between excitatory and inhibitory inputs to individual neurons; the second feature is distance-dependent connectivity. We show that applying a weak external stimulus to the model evokes a wave pattern propagating along lateral connections, but a strong external stimulus triggers a localized pattern; these stimulus strength-dependent population response patterns are quantitatively comparable with those measured in experimental studies. We identify network mechanisms underlying this population response, and demonstrate that the dynamics of population-level response patterns can explain a range of prominent features in neural responses, including changes to the dynamics of neurons' membrane potentials and synaptic inputs that characterize the shift of cortical states, and the stimulus-evoked decline in neuron response variability. Our study provides a unified population activity pattern-based view of diverse cortical response properties, thus shedding new insights into cortical processing.

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