Literature DB >> 20074588

AnimatLab: a 3D graphics environment for neuromechanical simulations.

David Cofer1, Gennady Cymbalyuk, James Reid, Ying Zhu, William J Heitler, Donald H Edwards.   

Abstract

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control requires a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. AnimatLab is a software tool that provides an approach to this problem through computer simulation. AnimatLab enables a computational model of an animal's body to be constructed from simple building blocks, situated in a virtual 3D world subject to the laws of physics, and controlled by the activity of a multicellular, multicompartment neural circuit. Sensor receptors on the body surface and inside the body respond to external and internal signals and then excite central neurons, while motor neurons activate Hill muscle models that span the joints and generate movement. AnimatLab provides a common neuromechanical simulation environment in which to construct and test models of any skeletal animal, vertebrate or invertebrate. The use of AnimatLab is demonstrated in a neuromechanical simulation of human arm flexion and the myotactic and contact-withdrawal reflexes. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 20074588     DOI: 10.1016/j.jneumeth.2010.01.005

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  20 in total

1.  Control of tumbling during the locust jump.

Authors:  David Cofer; Gennady Cymbalyuk; William J Heitler; Donald H Edwards
Journal:  J Exp Biol       Date:  2010-10-01       Impact factor: 3.312

Review 2.  Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement.

Authors:  Lena H Ting; Stacie A Chvatal; Seyed A Safavynia; J Lucas McKay
Journal:  Int J Numer Method Biomed Eng       Date:  2012-05-16       Impact factor: 2.747

3.  Control of Mammalian Locomotion by Somatosensory Feedback.

Authors:  Alain Frigon; Turgay Akay; Boris I Prilutsky
Journal:  Compr Physiol       Date:  2021-12-29       Impact factor: 8.915

4.  A leg to stand on: computational models of proprioception.

Authors:  Chris J Dallmann; Pierre Karashchuk; Bingni W Brunton; John C Tuthill
Journal:  Curr Opin Physiol       Date:  2021-03-19

5.  Muscle-spring dynamics in time-limited, elastic movements.

Authors:  M V Rosario; G P Sutton; S N Patek; G S Sawicki
Journal:  Proc Biol Sci       Date:  2016-09-14       Impact factor: 5.349

6.  Neuromechanical simulation.

Authors:  Donald H Edwards
Journal:  Front Behav Neurosci       Date:  2010-07-14       Impact factor: 3.558

7.  Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements.

Authors:  Shreya Saxena; Sridevi V Sarma; Munther Dahleh
Journal:  Neural Comput       Date:  2020-03-18       Impact factor: 2.026

8.  Three-dimensional simulation of the Caenorhabditis elegans body and muscle cells in liquid and gel environments for behavioural analysis.

Authors:  Andrey Palyanov; Sergey Khayrulin; Stephen D Larson
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-09-10       Impact factor: 6.237

9.  A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion.

Authors:  Nicholas S Szczecinski; Alexander J Hunt; Roger D Quinn
Journal:  Front Neurorobot       Date:  2017-08-09       Impact factor: 2.650

10.  SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Authors:  Cristian Jimenez-Romero; Jeffrey Johnson
Journal:  Neural Comput Appl       Date:  2016-06-07       Impact factor: 5.606

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