Path integration in large-scale space and with novel geometries: Comparing Vector Addition and Encoding-Error Models

AbstractPath integration is thought to rely on vestibular and proprioceptive cues yet most studies in humans involve primarily visual input, providing limited insight into their contributions. We developed a paradigm involving walking in an omnidirectional treadmill in which participants were guided on two legs of a triangle and then found their back way to origin. In Experiment 1, we tested a range of different triangle types while keeping distance relatively constant to determine the influence of spatial geometry. Participants overshot the angle they needed to turn and undershot the distance they needed to walk, with no consistent effect of triangle type. In Experiment 2, we manipulated distance while keeping angle relatively constant to determine how path integration operated over both shorter and longer distances. Participants underestimated the distance they needed to walk to the origin, with error increasing as a function of the walked distance. To attempt to account for our findings, we developed computational models involving vector addition, the second of which included terms for the influence of past trials on the current one. We compared against a previously developed model of human path integration, the Encoding Error model. We found that the vector addition models captured the tendency of participants to under-encode guided legs of the triangles and an influence of past trials on current trials. Together, our findings expand our understanding of body-based contributions to human path integration, further suggesting the value of vector addition models in understanding these important components of human navigation.Author SummaryHow do we remember where we have been? One important mechanism for doing so is called path integration, which refers to the ability to track one’s position in space with only self-motion cues. By tracking the direction and distance we have walked, we can create a mental arrow from the current location to the origin, termed the homing vector. Previous studies have shown that the homing vector is subject to systematic distortions depending on previously experienced paths, yet what influences these patterns of errors, particularly in humans, remains uncertain. In this study, we compare two models of path integration based on participants walking two legs of a triangle without vision and then completing the third leg based on their estimate of the homing vector. We found no effect of triangle shape on systematic errors, while path length scaled the systematic errors logarithmically, similar to Weber-Fechner law. While we show that both models captured participant’s behavior, a model based on vector addition best captured the patterns of error in the homing vector. Our study therefore has important implications for how humans track their location, suggesting that vector-based models provide a reasonable and simple explanation for how we do so.

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Path integration in large-scale space and with novel geometries: Comparing vector addition and encoding-error models

PLoS Computational Biology ◽

10.1371/journal.pcbi.1007489 ◽

2020 ◽

Vol 16 (5) ◽

pp. e1007489 ◽

Cited By ~ 1

Author(s):

Sevan K. Harootonian ◽

Robert C. Wilson ◽

Lukáš Hejtmánek ◽

Eli M. Ziskin ◽

Arne D. Ekstrom

Keyword(s):

Path Integration ◽

Large Scale ◽

Scale Space ◽

Error Models ◽

Vector Addition

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Visual influence on path integration in darkness indicates a multimodal representation of large-scale space

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1011843108 ◽

2011 ◽

Vol 108 (3) ◽

pp. 1152-1157 ◽

Cited By ~ 61

Author(s):

L. Tcheang ◽

H. H. Bulthoff ◽

N. Burgess

Keyword(s):

Path Integration ◽

Large Scale ◽

Scale Space ◽

Visual Influence ◽

Multimodal Representation

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Neural mechanisms for reward-modulated vector learning and navigation: from social insects to embodied agents

10.1101/045559 ◽

2016 ◽

Author(s):

Dennis Goldschmidt ◽

Poramate Manoonpong ◽

Sakyasingha Dasgupta

Keyword(s):

Social Insects ◽

Path Integration ◽

Computational Models ◽

Activity Patterns ◽

Learning Rule ◽

Neural Substrates ◽

Neural Mechanisms ◽

Circular Arrays ◽

Current Location ◽

Integration Mechanism

AbstractDespite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories anchored globally to the nest or visual landmarks. Although existing computational models reproduced similar behaviors, they largely neglected evidence for possible neural substrates underlying the generated behavior. Therefore, we present here a model of neural mechanisms in a modular closed-loop control - enabling vector navigation in embodied agents. The model consists of a path integration mechanism, reward-modulated global and local vector learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent’s current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In sim-ulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. This provides an explanation for, how view-based navigational strategies are guided by path integration. Consequently, we provide a novel approach for vector learning and navigation in a simulated embodied agent linking behavioral observations to their possible underlying neural substrates.Author SummaryDesert ants survive under harsh conditions by foraging for food in temperatures over 60° C. In this extreme environment, they cannot, like other ants, use pheromones to track their long-distance journeys back to their nests. Instead they apply a computation called path integration, which involves integrating skylight compass and odometric stimuli to estimate its current position. Path integration is not only used to return safely to their nests, but also helps in learning so-called vector memories. Such memories are sufficient to produce goal-directed and landmark-guided navigation in social insects. How can small insect brains generate such complex behaviors? Computational models are often useful for studying behavior and their underlying control mechanisms. Here we present a novel computational framework for the acquisition and expression of vector memories based on path integration. It consists of multiple neural networks and a reward-based learning rule, where vectors are represented by the activity patterns of circular arrays. Our model not only reproduces goal-directed navigation and route formation in a simulated agent, but also offers predictions about neural implementations. Taken together, we believe that it demonstrates the first complete model of vector-guided navigation linking observed behaviors of navigating social insects to their possible underlying neural mechanisms.

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Capture Strategy Based on a Distributed Control System for a Large-scale Space End-Effector

ROBOT ◽

10.3724/sp.j.1218.2011.00434 ◽

2011 ◽

Vol 33 (4) ◽

pp. 434-439 ◽

Cited By ~ 5

Author(s):

Dangyang JIE ◽

Fenglei NI ◽

Yisong TAN ◽

Hong LIU ◽

Hegao CAI

Keyword(s):

Control System ◽

Distributed Control ◽

Large Scale ◽

Scale Space ◽

Distributed Control System ◽

End Effector

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Reducing Risk of Large Scale Space Systems Using a Modular Architecture

10.21236/ada510666 ◽

2004 ◽

Cited By ~ 5

Author(s):

Owen Brown

Keyword(s):

Large Scale ◽

Scale Space ◽

Space Systems ◽

Modular Architecture

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DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

Protein and Peptide Letters ◽

10.2174/0929866527666201202103411 ◽

2020 ◽

Vol 27 ◽

Author(s):

Zaheer Ullah Khan ◽

Dechang Pi

Keyword(s):

Large Scale ◽

Computational Models ◽

Research Work ◽

Training Data ◽

Training Dataset ◽

Validation Dataset ◽

Cytokine Signaling ◽

Minority Class ◽

Independent Dataset ◽

Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

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Use of network approaches to solve tasks of ballistic and navigation support for large-scale space systems mission control

10.1063/5.0036174 ◽

2021 ◽

Author(s):

M. M. Matushin ◽

A. Yu. Kutomanov

Keyword(s):

Large Scale ◽

Scale Space ◽

Space Systems ◽

Navigation Support ◽

Mission Control ◽

Network Approaches

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

Cancers ◽

10.3390/cancers13092111 ◽

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.

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