scholarly journals Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

2019 ◽  
Vol 25 (3) ◽  
pp. 263-311 ◽  
Author(s):  
Qinbing Fu ◽  
Hongxin Wang ◽  
Cheng Hu ◽  
Shigang Yue
Keyword(s):  
Motion Perception ◽  
Computational Models ◽  
Evolutionary Development ◽  
Biological Research ◽  
Multiple Systems ◽  
Intelligent Machines ◽  
Visual Systems ◽  
Dynamic Vision ◽  
Movement Detectors ◽  
Motion Detectors

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

scholarly journals Computational models of adaptive behavior and prefrontal cortex

2021 ◽  
Author(s):  
Alireza Soltani ◽  
Etienne Koechlin
Keyword(s):  
Prefrontal Cortex ◽  
Adaptive Behavior ◽  
Computational Models ◽  
Cognitive Architecture ◽  
Behavioral Outcomes ◽  
Internal Models ◽  
Multiple Systems ◽  
Statistical Inferences ◽  
Neural Computations ◽  
Task Sets

AbstractThe real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

scholarly journals Invasion performance-similarity found among multiple cell systems

2020 ◽  
Author(s):  
Om Prakash
Keyword(s):  
Cell Lines ◽  
Drug Repositioning ◽  
Biological Research ◽  
Multiple Systems ◽  
Biologically Relevant ◽  
Selective Targeting ◽  
Ann Models ◽  
Multiple Cell ◽  
Artificial Neural Network Ann ◽  
Network Mapping

ABSTRACTUnderstanding of inter-system behavior develops biologically relevant intuition for drug repositioning as well as other biological research. But combining all the possible genes interactions into a system, and furthermore comparisons of multiple systems are a challenge on time ground with feasible experiments. In present study, 64 cell lines from 11 different organs were compared for their invasion performance. RNA expressions of 23 genes were used to create systems artificial neural network (ANN) models. ANN models were prepared for all 64 cell lines and observed for their invasion performance through network mapping. The resulted cell line clusters bear feasible capacity to perform experiments for biologically relevant research motivations as drug repositioning and selective targeting etc.; and can be used for analysis of invasion related aspects.

4. Motion perception

2017 ◽  
pp. 50-58
Author(s):  
Brian Rogers
Keyword(s):  
Motion Perception ◽  
Apparent Motion ◽  
Time To Contact ◽  
Motion System ◽  
Visual Systems ◽  
Temporal Process ◽  
The World ◽  
Induced Motion ◽  
After Effect ◽  
Spatio Temporal

The ability to detect motion is one of the most important properties of our visual system and the visual systems of nearly every other species. Motion perception is not just important for detecting the movement of objects—both for catching prey and for avoiding predators—but it is also important for providing information about the 3-D structure of the world, for maintaining balance, determining our direction of heading, segregating the scene and breaking camouflage, and judging time-to-contact with other objects in the world. ‘Motion perception’ describes the spatio-temporal process of motion perception and the perceptual effects that tell us something about the characteristics of the motion system: apparent motion, the motion after-effect, and induced motion.

scholarly journals Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Frank T. Bergmann ◽  
Jonathan Cooper ◽  
Matthias König ◽  
Ion Moraru ◽  
David Nickerson ◽  
...  
Keyword(s):  
Time Course ◽  
Computational Models ◽  
Simulation Experiment ◽  
Computational Simulation ◽  
Biological Research ◽  
Markup Language ◽  
Formal Representation ◽  
Post Process ◽  
Experiment Description ◽  
Level 1

AbstractThe creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.

Perception of Depth in Rotating Objects: 5. Phenomenal Motion in Stereokinesis

1968 ◽  
Vol 27 (3) ◽  
pp. 903-926 ◽  
Author(s):  
Roy B. Mefferd
Keyword(s):  
Motion Perception ◽  
Anchor Point ◽  
Theoretical Concepts ◽  
Motion Data ◽  
Visual Systems ◽  
Multiple Object ◽  
Main Current ◽  
Rotating Objects

Stereokinetic motion percepts are described and analyzed. The fluctuations of three visual systems, the near-far, one object-multiple object, and anchor point mechanisms are shown to be independent, and the consequences of the fluctuations for phenomenal motion are examined. The main current theoretical concepts of motion perception fail to account for all the observations reported. Speculations on the function of the anchor point mechanism and on how motion data may be processed are advanced.

scholarly journals Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research

2018 ◽  
Vol 140 (2) ◽  
Author(s):  
Ahmet Erdemir ◽  
Peter J. Hunter ◽  
Gerhard A. Holzapfel ◽  
Leslie M. Loew ◽  
John Middleton ◽  
...  
Keyword(s):  
Computational Models ◽  
Medical Training ◽  
Clinical Care ◽  
Knowledge Exchange ◽  
Technical Committee ◽  
Simulation Software ◽  
Scholarly Publishing ◽  
Multiple Systems ◽  
Computational Biomechanics ◽  
Model Sharing

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

scholarly journals Concerning suppression and facilitation of motion perception in humans

2018 ◽  
Author(s):  
Michael-Paul Schallmo ◽  
Scott Murray
Keyword(s):  
Motion Perception ◽  
Response Bias ◽  
Computational Models ◽  
Neural Responses ◽  
Motion Discrimination ◽  
Scientific Dialogue

Here we consider some topics from a commentary by Tzvetanov (2018) on our article entitled Suppression and facilitation of human neural responses (Schallmo et al., 2018). The author raises two main points of criticism, which we will summarize thus: 1) psychophysical response bias is not adequately modeled, and 2) other computational models may be better suited to describe the psychophysical motion discrimination data. Both of these points are reasonable in principle, but we disagree with the author in a few areas. We provide some discussion of both issues in order to further elevate the scientific dialogue.

scholarly journals Two-Dimensional Motion Perception in Flies

1993 ◽  
Vol 5 (6) ◽  
pp. 856-868 ◽  
Author(s):  
A. Borst ◽  
M. Egelhaaf ◽  
H. S. Seung
Keyword(s):  
Simple Model ◽  
Motion Perception ◽  
Visual Stimulus ◽  
Vertical Motion ◽  
Two Dimensional ◽  
Spatial Integration ◽  
Optomotor Response ◽  
Pattern Motion ◽  
Motion Detectors

We study two-dimensional motion perception in flies using a semicircular visual stimulus. Measurements of both the H1-neuron and the optomotor response are consistent with a simple model supposing spatial integration of the outputs of correlation-type motion detectors. In both experiment and model, there is substantial H1 and horizontal (yaw) optomotor response to purely vertical motion of the stimulus. We conclude that the fly's optomotor response to a two-dimensional pattern, depending on its structure, may deviate considerably from the direction of pattern motion.

scholarly journals Relating Blindsight and AI: A Review

2021 ◽  
pp. 1-15
Author(s):  
Joshua Bensemann ◽  
Qiming Bao ◽  
Gaël Gendron ◽  
Tim Hartill ◽  
Michael Witbrock
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Models ◽  
Visual Experience ◽  
Biological Research ◽  
Future Research ◽  
Network Architectures ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Artificial Neural

Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review of research on the phenomenon of blindsight in an attempt to generate ideas for artificial intelligence models. Blindsight can be considered as a diminished form of visual experience. If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks. This paper has been structured into three parts. Section 2 is a review of blindsight research, looking specifically at the errors occurring during this condition compared to normal vision. Section 3 identifies overall patterns from Sec. 2 to generate insights for computational models of vision. Section 4 demonstrates the utility of examining biological research to inform artificial intelligence research by examining computational models of visual attention relevant to one of the insights generated in Sec. 3. The research covered in Sec. 4 shows that incorporating one of our insights into computational vision does benefit those models. Future research will be required to determine whether our other insights are as valuable.

Evolutionary internalized regularities

2001 ◽  
Vol 24 (4) ◽  
pp. 626-628 ◽  
Author(s):  
Robert Schwartz
Keyword(s):  
Circadian Rhythms ◽  
Motion Perception ◽  
Apparent Motion ◽  
Ecological Validity ◽  
Experimental Studies ◽  
Evolutionary Development ◽  
Kinematic Constraint ◽  
Explanatory Role ◽  
Kinematic Geometry ◽  
Primary Focus

Roger Shepard's proposals and supporting experiments concerning evolutionary internalized regularities have been very influential in the study of vision and in other areas of psychology and cognitive science. This paper examines issues concerning the need, nature, explanatory role, and justification for postulating such internalized constraints. In particular, I seek further clarification from Shepard on how best to understand his claim that principles of kinematic geometry underlie phenomena of motion perception.My primary focus is on the ecological validity of Shepard's kinematic constraint in the context of ordinary motion perception. First, I explore the analogy Shepard draws between internalized circadian rhythms and the supposed internalization of kinematic geometry. Next, questions are raised about how to interpret and justify applying results from his own and others' experimental studies of apparent motion to more everyday cases of motion perception in richer environments. Finally, some difficulties with Shepard's account of the evolutionary development of his kinematic constraint are considered.

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