scholarly journals Modeling Nonlinear Dendritic Processing of Facilitation in a Dragonfly Target-Tracking Neuron

2021 ◽  
Vol 15 ◽  
Author(s):  
Bo M. B. Bekkouche ◽  
Patrick A. Shoemaker ◽  
Joseph M. Fabian ◽  
Elisa Rigosi ◽  
Steven D. Wiederman ◽  
...  
Keyword(s):  
Nmda Receptors ◽  
Target Tracking ◽  
Computational Models ◽  
Target Location ◽  
Dendritic Morphology ◽  
Morphological Data ◽  
Motion Processing ◽  
Nonlinear Properties ◽  
Data Support

Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking of prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modeling to test whether a combination of dendritic morphology and nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid computational model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during in vivo recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on the morphology of an unrelated type of motion processing neuron from a dipteran fly required more than three times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data support a potential role for NMDA receptors in target tracking and also demonstrate the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

scholarly journals Modelling nonlinear dendritic processing of facilitation in a dragonfly target-tracking neuron

2021 ◽  
Author(s):  
Bo M. B. Bekkouche ◽  
Patrick A. Shoemaker ◽  
Joseph M. Fabian ◽  
Elisa Rigosi ◽  
Steven D. Wiederman ◽  
...  
Keyword(s):  
Nmda Receptors ◽  
Target Tracking ◽  
Computational Models ◽  
Target Location ◽  
Computational Modelling ◽  
Neuronal Model ◽  
Dendritic Morphology ◽  
Morphological Data ◽  
Motion Processing

AbstractDragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modelling to test whether a combination of dendritic morphology combined with the nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid neuronal model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured duringin vivorecordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on morphology of an unrelated type of motion processing neuron from a dipteran fly required more than 3 times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data supports a potential role for NMDA receptors in target tracking and also demonstrates the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

Nanobiosensing with target amplification, and nanoswimmers can be controlled by magnetic fields and facilitate gene therapy and nanomedicine applications

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Magnetic Fields ◽  
Computational Models ◽  
Target Location ◽  
Vascular System ◽  
Target Identification ◽  
Tracking System ◽  
Rotating Magnetic Field ◽  
Three Dimensions ◽  
Learning Approaches

Nanobiosensing with target amplification is one such example. In this scenario, "activator" nanoparticles stimulate the target location, such as a tumor, resulting in spatial amplification of a tumor-triggered phenomenon-of-interest (POI). The typical targeting approach, which relies on the human vascular system to transport nanoparticles, is inefficient and is considered a brute-force search from a computing standpoint. By evaluating the observable properties of these nanoswimmers, which are controlled by magnetic fields created by electromagnetic coils, an external tracking system is utilized to explore the tissue environment. The stochastic movement of numerous loosely connected, disc-shaped components in the system results in deterministic locomotion. When each component is programmed to oscillate omnidirectionally along its radius, expanding and contracting in response to varying environmental signals, the system can collectively locomote towards the source of the environmental signal. The main goal is to enable interoperability while developing multiple simulation components for computational nanobiosensing with different and non-interoperable interfaces. The accuracy of the computational models and algorithms should be tested utilizing multi-physics in silico platforms that simulate the targeting of externally manipulable or self-regulatable nanorobots. To minimize the rates of erroneous and missed detection, "natural" deep learning approaches might be used to train mathematical models for in vivo target identification. The particle stretching approach for creating worm-like structures capable of low-Reynolds-number propulsion when actuated by a rotating magnetic field is one possibility. To replicate the function of an MRI, a sensor array made up of several magnetoresistive sensors might be utilized to precisely place nanorobots. Such systems would also need to be developed in three dimensions, with more complicated locomotive behavior of components and aggregates inside blood flows. In conclusion, computational nanobiosensing is to improve in vivo POI targeting and understanding of POI-induced gradients.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Current trends in mathematical modeling of tumor–microenvironment interactions: a survey of tools and applications

2010 ◽  
Vol 235 (4) ◽  
pp. 411-423 ◽  
Author(s):  
Katarzyna A Rejniak ◽  
Lisa J McCawley
Keyword(s):  
Mathematical Modeling ◽  
Protein Interactions ◽  
Computational Models ◽  
Chemical Species ◽  
Tumor Stroma ◽  
Cellular Functions ◽  
Complex Interactions ◽  
Expanding Population

In its simplest description, a tumor is comprised of an expanding population of transformed cells supported by a surrounding microenvironment termed the tumor stroma. The tumor microcroenvironment has a very complex composition, including multiple types of stromal cells, a dense network of various extracellular matrix (ECM) fibers interpenetrated by the interstitial fluid and gradients of several chemical species that either are dissolved in the fluid or are bound to the ECM structure. In order to study experimentally such complex interactions between multiple players, cancer is dissected and considered at different scales of complexity, such as protein interactions, biochemical pathways, cellular functions or whole organism studies. However, the integration of information acquired from these studies into a common description is as difficult as the disease itself. Computational models of cancer can provide cancer researchers with invaluable tools that are capable of integrating the complexity into organizing principles as well as suggesting testable hypotheses. We will focus in this Minireview on mathematical models in which the whole cell is a main modeling unit. We will present a current stage of such cell-focused mathematical modeling incorporating different stromal components and their interactions with growing tumors, and discuss what modeling approaches can be undertaken to complement the in vivo and in vitro experimentation.

Inter-reflux and bolus clearance times in non-pathologic pediatric patients: data support computational models

2014 ◽  
Vol 28 (2) ◽  
pp. 138-144 ◽  
Author(s):  
A. Barriga-Rivera ◽  
M. J. Moya ◽  
M. Elena ◽  
M. Lopez-Alonso
Keyword(s):  
Computational Models ◽  
Pediatric Patients ◽  
Data Support

Hybrid Rigid-Deformable Model for Prediction of Neighboring Intervertebral Disk Loads During Flexion Movement After Lumbar Interbody Fusion at L3–4 Level

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Tien Tuan Dao
Keyword(s):  
Lumbar Spine ◽  
Computational Models ◽  
Interbody Fusion ◽  
Clinical Decision ◽  
Intervertebral Disk ◽  
Design Guideline ◽  
Spinal Loads ◽  
Stress Changes ◽  
Stress Behaviors

Knowledge of spinal loads in neighboring disks after interbody fusion plays an important role in the clinical decision of this treatment as well as in the elucidation of its effect. However, controversial findings are still noted in the literature. Moreover, there are no existing models for efficient prediction of intervertebral disk stresses within annulus fibrosus (AF) and nucleus pulposus (NP) regions. In this present study, a new hybrid rigid-deformable modeling workflow was established to quantify the mechanical stress behaviors within AF and NP regions of the L1–2, L2–3, and L4–5 disks after interbody fusion at L3–4 level. The changes in spinal loads were compared with results of the intact model without interbody fusion. The fusion outcomes revealed maximal stress changes (10%) in AF region of L1–2 disk and in NP region of L2–3 disk. The minimal stress change (1%) is noted at the NP region of the L1–2 disk. The validation of simulation outcomes of fused and intact lumbar spine models against those of other computational models and in vivo measurements showed good agreements. Thus, this present study may be used as a novel design guideline for a specific implant and surgical scenario of the lumbar spine disorders.

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

2013 ◽  
Vol 110 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Arij Daou ◽  
Matthew T. Ross ◽  
Frank Johnson ◽  
Richard L. Hyson ◽  
Richard Bertram
Keyword(s):  
Computational Models ◽  
Premotor Cortex ◽  
Brain Slices ◽  
Ionic Currents ◽  
Proper Name ◽  
Using Data ◽  
Electrophysiological Characterization

The nucleus HVC (proper name) within the avian analog of mammal premotor cortex produces stereotyped instructions through the motor pathway leading to precise, learned vocalization by songbirds. Electrophysiological characterization of component HVC neurons is an important requirement in building a model to understand HVC function. The HVC contains three neural populations: neurons that project to the RA (robust nucleus of arcopallium), neurons that project to Area X (of the avian basal ganglia), and interneurons. These three populations are interconnected with specific patterns of excitatory and inhibitory connectivity, and they fire with characteristic patterns both in vivo and in vitro. We performed whole cell current-clamp recordings on HVC neurons within brain slices to examine their intrinsic firing properties and determine which ionic currents are responsible for their characteristic firing patterns. We also developed conductance-based models for the different neurons and calibrated the models using data from our brain slice work. These models were then used to generate predictions about the makeup of the ionic currents that are responsible for the different responses to stimuli. These predictions were then tested and verified in the slice using pharmacological manipulations. The model and the slice work highlight roles of a hyperpolarization-activated inward current ( Ih), a low-threshold T-type Ca2+ current ( ICa-T), an A-type K+ current ( IA), a Ca2+-activated K+ current ( ISK), and a Na+-dependent K+ current ( IKNa) in driving the characteristic neural patterns observed in the three HVC neuronal populations. The result is an improved characterization of the HVC neurons responsible for song production in the songbird.

Generation of Slow Network Oscillations in the Developing Rat Hippocampus After Blockade of Glutamate Uptake

2007 ◽  
Vol 98 (4) ◽  
pp. 2324-2336 ◽  
Author(s):  
Adriano Augusto Cattani ◽  
Valérie Delphine Bonfardin ◽  
Alfonso Represa ◽  
Yehezkel Ben-Ari ◽  
Laurent Aniksztejn
Keyword(s):  
Nmda Receptors ◽  
Glutamate Uptake ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Proper Function ◽  
Network Oscillations ◽  
Bath Application ◽  
Hippocampal Network

Cell-surface glutamate transporters are essential for the proper function of early cortical networks because their dysfunction induces seizures in the newborn rat in vivo. We have now analyzed the consequences of their inhibition by dl-TBOA on the activity of the developing CA1 rat hippocampal network in vitro. dl-TBOA generated a pattern of recurrent depolarization with an onset and decay of several seconds' duration in interneurons and pyramidal cells. These slow network oscillations (SNOs) were mostly mediated by γ-aminobutyric acid (GABA) in pyramidal cells and by GABA and N-methyl-d-aspartate (NMDA) receptors in interneurons. However, in both cell types SNOs were blocked by NMDA receptor antagonists, suggesting that their generation requires a glutamatergic drive. Moreover, in interneurons, SNOs were still generated after the blockade of NMDA-mediated synaptic currents with MK-801, suggesting that SNOs are expressed by the activation of extrasynaptic NMDA receptors. Long-lasting bath application of glutamate or NMDA failed to induce SNOs, indicating that they are generated by periodic but not sustained activation of NMDA receptors. In addition, SNOs were observed in interneurons recorded in slices with or without the strata pyramidale and oriens, suggesting that the glutamatergic drive may originate from the radiatum and pyramidale strata. We propose that in the absence of an efficient transport of glutamate, the transmitter diffuses in the extracellular space to activate extrasynaptic NMDA receptors preferentially present on interneurons that in turn activate other interneurons and pyramidal cells. This periodic neuronal coactivation may contribute to the generation of seizures when glutamate transport dysfunction is present.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

Role for NMDA receptors in visceral nociceptive transmission in the anterior cingulate cortex of viscerally hypersensitive rats

2008 ◽  
Vol 294 (4) ◽  
pp. G918-G927 ◽  
Author(s):  
Xiaoyin Wu ◽  
Jun Gao ◽  
Jin Yan ◽  
Jing Fan ◽  
Chung Owyang ◽  
...  
Keyword(s):  
Nmda Receptors ◽  
Receptor Antagonist ◽  
Anterior Cingulate Cortex ◽  
Cingulate Cortex ◽  
Neuronal Firing ◽  
Anterior Cingulate ◽  
High Dose ◽  
Glutamatergic Transmission ◽  
Reverse Microdialysis

We have identified colorectal distension (CRD)-responsive neurons in the anterior cingulate cortex (ACC) and demonstrated that persistence of a heightened visceral afferent nociceptive input to the ACC induces ACC sensitization. In the present study, we confirmed that rostral ACC neurons of sensitized rats [induced by chicken egg albumin (EA)] exhibit enhanced spike responses to CRD. Simultaneous in vivo recording and reverse microdialysis of single ACC neurons showed that a low dose of glutamate (50 μM) did not change basal ACC neuronal firing in normal rats but increased ACC neuronal firing in EA rats from 18 ± 2 to 32 ± 3.8 impulses/10 s. A high dose of glutamate (500 μM) produced 1.95-fold and a 4.27-fold increases of ACC neuronal firing in sham-treated rats and in EA rats, respectively, suggesting enhanced glutamatergic transmission in the ACC neurons of EA rats. Reverse microdialysis of the 3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA)/kainite receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX; 10 μM) reduced basal and abolished CRD-induced ACC neuronal firing in normal rats. In contrast, microdialysis of N-methyl-d-aspartate (NMDA) receptor antagonist AP5 had no effect on ACC neuronal firing in normal rats. However, AP5 produced 86% inhibition of ACC neuronal firing evoked by 50 mmHg CRD in the EA rats. In conclusion, ACC nociceptive transmissions are mediated by glutamate AMPA receptors in the control rats. ACC responses to CRD are enhanced in viscerally hypersensitive rats. The enhancement of excitatory glutamatergic transmission in the ACC appears to mediate this response. Furthermore, NMDA receptors mediate ACC synaptic responses after the induction of visceral hypersensitivity.

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