412
F. Young et al.
through the addition of a series damper. Modifications to the model inherent to the
program will necessitate an overhaul of the software itself, as Animatlab does not natively
allow for different muscle models. However, the work presented here demonstrates
the capability of the existing software to represent muscular activation so long as the
simulation constraints are considered.
References
1. Lewinson, R.T., Stefanyshyn, D.J.: A descriptive analysis of the climbing mechanics of a
mountain goat (Oreamnos americanus). Zoology 119, 541–546 (2016). https://doi.org/10.
1016/j.zool.2016.06.001
2. Nishikawa, K., Biewener, A.A., Aerts, P., et al.: Neuromechanics: an integrative approach for
understanding motor control. Integr. Comp. Biol. 47, 16–54 (2007). https://doi.org/10.1093/
icb/icm024
3. Chiel, H.J., Beer, R.D., Sterling, L.S.: Heterogeneous neural networks for adaptive behavior
in dynamic environments. In: Proceedings of the 1st International Conference on Neural
Information Processing Systems, pp. 577–585. MIT Press, Cambridge, MA, USA (1988)
4. Aoi, S., Kondo, T., Hayashi, N., et al.: Contributions of phase resetting and interlimb coordination to the adaptive control of hindlimb obstacle avoidance during locomotion in rats:
a simulation study. Biol. Cybern. 107, 201–216 (2013). https://doi.org/10.1007/s00422-0130546-6
5. Thota, A.K., Watson, S.C., Knapp, E., et al.: Neuromechanical control of locomotion in the
rat. J. Neurotrauma 22, 442–465 (2005). https://doi.org/10.1089/neu.2005.22.442
6. Ekeberg, Ö., Pearson, K.: Computer simulation of stepping in the hind legs of the cat: an
examination of mechanisms regulating the stance-to-swing transition. J. Neurophysiol. 94,
4256–4268 (2005). https://doi.org/10.1152/jn.00065.2005
7. Chiel, H.J., Beer, R.D.: The brain has a body: adaptive behavior emerges from interactions
of nervous system, body and environment. Trends Neurosci. 20, 553–557 (1997). https://doi.
org/10.1016/S0166-2236(97)01149-1
8. Chiel, H.J., Ting, L.H., Ekeberg, Ö., Hartmann, M.J.Z.: The brain in its body: motor control
and sensing in a biomechanical context. J. Neurosci. 29, 12807–12814 (2009). https://doi.
org/10.1523/JNEUROSCI.3338-09.2009
9. Cofer, D., Cymbalyuk, G., Reid, J., et al.: AnimatLab: a 3D graphics environment for neuromechanical simulations. J. Neurosci. Meth. 187, 280–288 (2010). https://doi.org/10.1016/
j.jneumeth.2010.01.005
10. Deng, K., Szczecinski, N.S., Arnold, D., et al.: Neuromechanical model of rat hindlimb
walking with two-layer CPGs. Biomimetics 4, 21 (2019). https://doi.org/10.3390/biomimeti
cs4010021
11. Hunt, A.J., Szczecinski, N.S., Andrada, E., Fischer, M., Quinn, R.D.: Using animal data and
neural dynamics to reverse engineer a neuromechanical rat model. In: Wilson, S.P., Verschure,
P.F., Mura, A., Prescott, T.J. (eds.) LIVINGMACHINES 2015. LNCS (LNAI), vol. 9222,
pp. 211–222. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22979-9_21
12. Young, F., Rode, C., Hunt, A., Quinn, R.: Analyzing moment arm profiles in a full-muscle
rat hindlimb model. Biomimetics 4, 10 (2019). https://doi.org/10.3390/biomimetics4010010
13. Szczecinski, N.S., Brown, A.E., Bender, J.A., Quinn, R.D., Ritzmann, R.E.: A neuromechanical simulation of insect walking and transition to turning of the cockroach Blaberus
discoidalis. Biol. Cybern. 108(1), 1–21 (2013). https://doi.org/10.1007/s00422-013-0573-3
14. Hunt, A., Szczecinski, N., Quinn, R.: Development and training of a neural controller for
hind leg walking in a dog robot. Front. Neurorobot. 11 (2017). https://doi.org/10.3389/fnbot.
2017.00018
F. Young et al.
through the addition of a series damper. Modifications to the model inherent to the
program will necessitate an overhaul of the software itself, as Animatlab does not natively
allow for different muscle models. However, the work presented here demonstrates
the capability of the existing software to represent muscular activation so long as the
simulation constraints are considered.
References
1. Lewinson, R.T., Stefanyshyn, D.J.: A descriptive analysis of the climbing mechanics of a
mountain goat (Oreamnos americanus). Zoology 119, 541–546 (2016). https://doi.org/10.
1016/j.zool.2016.06.001
2. Nishikawa, K., Biewener, A.A., Aerts, P., et al.: Neuromechanics: an integrative approach for
understanding motor control. Integr. Comp. Biol. 47, 16–54 (2007). https://doi.org/10.1093/
icb/icm024
3. Chiel, H.J., Beer, R.D., Sterling, L.S.: Heterogeneous neural networks for adaptive behavior
in dynamic environments. In: Proceedings of the 1st International Conference on Neural
Information Processing Systems, pp. 577–585. MIT Press, Cambridge, MA, USA (1988)
4. Aoi, S., Kondo, T., Hayashi, N., et al.: Contributions of phase resetting and interlimb coordination to the adaptive control of hindlimb obstacle avoidance during locomotion in rats:
a simulation study. Biol. Cybern. 107, 201–216 (2013). https://doi.org/10.1007/s00422-0130546-6
5. Thota, A.K., Watson, S.C., Knapp, E., et al.: Neuromechanical control of locomotion in the
rat. J. Neurotrauma 22, 442–465 (2005). https://doi.org/10.1089/neu.2005.22.442
6. Ekeberg, Ö., Pearson, K.: Computer simulation of stepping in the hind legs of the cat: an
examination of mechanisms regulating the stance-to-swing transition. J. Neurophysiol. 94,
4256–4268 (2005). https://doi.org/10.1152/jn.00065.2005
7. Chiel, H.J., Beer, R.D.: The brain has a body: adaptive behavior emerges from interactions
of nervous system, body and environment. Trends Neurosci. 20, 553–557 (1997). https://doi.
org/10.1016/S0166-2236(97)01149-1
8. Chiel, H.J., Ting, L.H., Ekeberg, Ö., Hartmann, M.J.Z.: The brain in its body: motor control
and sensing in a biomechanical context. J. Neurosci. 29, 12807–12814 (2009). https://doi.
org/10.1523/JNEUROSCI.3338-09.2009
9. Cofer, D., Cymbalyuk, G., Reid, J., et al.: AnimatLab: a 3D graphics environment for neuromechanical simulations. J. Neurosci. Meth. 187, 280–288 (2010). https://doi.org/10.1016/
j.jneumeth.2010.01.005
10. Deng, K., Szczecinski, N.S., Arnold, D., et al.: Neuromechanical model of rat hindlimb
walking with two-layer CPGs. Biomimetics 4, 21 (2019). https://doi.org/10.3390/biomimeti
cs4010021
11. Hunt, A.J., Szczecinski, N.S., Andrada, E., Fischer, M., Quinn, R.D.: Using animal data and
neural dynamics to reverse engineer a neuromechanical rat model. In: Wilson, S.P., Verschure,
P.F., Mura, A., Prescott, T.J. (eds.) LIVINGMACHINES 2015. LNCS (LNAI), vol. 9222,
pp. 211–222. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22979-9_21
12. Young, F., Rode, C., Hunt, A., Quinn, R.: Analyzing moment arm profiles in a full-muscle
rat hindlimb model. Biomimetics 4, 10 (2019). https://doi.org/10.3390/biomimetics4010010
13. Szczecinski, N.S., Brown, A.E., Bender, J.A., Quinn, R.D., Ritzmann, R.E.: A neuromechanical simulation of insect walking and transition to turning of the cockroach Blaberus
discoidalis. Biol. Cybern. 108(1), 1–21 (2013). https://doi.org/10.1007/s00422-013-0573-3
14. Hunt, A., Szczecinski, N., Quinn, R.: Development and training of a neural controller for
hind leg walking in a dog robot. Front. Neurorobot. 11 (2017). https://doi.org/10.3389/fnbot.
2017.00018