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The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics

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Sensorimotor Integration in the Whisker System

Abstract

We consider the problem of sensorimotor co-ordination in mammals through the lens of vibrissal touch, and via the methodology of embodied computational neuroscience—using biomimetic robots to synthesize and investigate models of mammalian brain architecture. The chapter focuses on five major brain sub-systems and their likely role in vibrissal system function—superior colliculus, basal ganglia, somatosensory cortex, cerebellum, and hippocampus. With respect to each of these we demonstrate how embodied modelling has helped elucidate their likely function in the brain of awake behaving animals. We also demonstrate how the appropriate co-ordination of these sub-systems, with a model of brain architecture, can give rise to integrated behaviour in a life-like whiskered robot.

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References

  1. Brecht M, Naumann R, Anjum F, Wolfe J, Munz M, Mende C et al (2011) The neurobiology of Etruscan shrew active touch. Philos Trans R Soc Lond B Biol Sci 366(1581):3026–3036

    Article  PubMed  PubMed Central  Google Scholar 

  2. Munz M, Brecht M, Wolfe J (2010) Active touch during shrew prey capture. Front Behav Neurosci 5:12

    Google Scholar 

  3. Roth-Alpermann C, Anjum F, Naumann R, Brecht M (2010) Cortical organization in the Etruscan shrew (Suncus etruscus). J Neurophysiol 104(5):2389–2406

    Article  PubMed  Google Scholar 

  4. Anjum F, Turni H, Mulder PG, van der Burg J, Brecht M (2006) Tactile guidance of prey capture in Etruscan shrews. Proc Natl Acad Sci U S A 103(44):16544–16549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston

    Google Scholar 

  6. Prescott TJ, Diamond ME, Wing AM (2011) Active touch sensing. Philos Trans R Soc Lond B Biol Sci 366(1581):2989–2995

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ahissar E, Kleinfeld D (2003) Closed-loop neuronal computations: focus on vibrissa somatosensation in rat. Cereb Cortex 13(1):53–62

    Article  PubMed  Google Scholar 

  8. Prescott TJ, Gonzalez FM, Humphries MD, Gurney K, Redgrave P (2006) A robot model of the basal ganglia: behaviour and intrinsic processing. Neural Networks 19(1):31–61

    Google Scholar 

  9. Prescott, TJ, Ayers J, Grasso FW, Verschure P. F. M. J (In press) Embodied Models and Neurorobotics. In: Arbib MA, Bonaiuto JJ (eds) From Neuron to Cognition via Computational Neuroscience. MIT Press, MA, Cambridge

    Google Scholar 

  10. Braitenberg V (1986) Vehicles: experiments in synthetic psychology. MIT Press, Cambridge

    Google Scholar 

  11. Mitchinson B, Pearson M, Pipe T, Prescott TJ (2011) Biomimetic robots as scientific models: a view from the whisker tip. In: Krichmar J (ed) Neuromorphic and brain-based robots. MIT Press, Boston

    Google Scholar 

  12. Rosenblueth A, Wiener N (1945) The role of models in science. Philos Sci 12(4):316–321

    Article  Google Scholar 

  13. Prescott TJ (2007) Forced moves or good tricks in design space? Landmarks in the evolution of neural mechanisms for action selection. Adapt Behav 15(1):9–31

    Article  Google Scholar 

  14. Hallam JCT, Malcolm CA (1994) Behaviour—perception, action, and intelligence—the view from situated robotics. Philos Trans R Soc Lond Ser A 349(1689):29–42

    Article  Google Scholar 

  15. Mitchinson B, Pearson MJ, Pipe AG, Prescott TJ (2012) The emergence of action sequences from spatial attention: insight from mammal-like robots. Living machines: biomimetic and biohybrid systems. Barcelona, Spain

    Google Scholar 

  16. Prescott TJ, Redgrave P, Gurney KN (1999) Layered control architectures in robots and vertebrates. Adapt Behav 7(1):99–127

    Article  Google Scholar 

  17. Redgrave P, Prescott TJ, Gurney KN (1999) The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89:1009–1023

    Article  CAS  PubMed  Google Scholar 

  18. Brooks RA (1991) New approaches to robotics. Science 253:1227–1232

    Article  CAS  PubMed  Google Scholar 

  19. Gandhi NJ, Katnani HA (2011) Motor functions of the superior colliculus. Annu Rev Neurosci 34:205–231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Posner MI (1980) Orienting of attention. Q J Exp Psychol 32:3–25

    Article  CAS  PubMed  Google Scholar 

  21. Cisek P (2007) Cortical mechanisms of action selection: the affordance competition hypothesis. Phil Trans Roy Soc B 362(1485):1585–1599

    Article  Google Scholar 

  22. Pearson MJ, Mitchinson B, Welsby J, Pipe T, Prescott TJ (2010) SCRATCHbot: Active tactile sensing in a whiskered mobile robot. In: Meyer J-A, Guillot A (eds) The 11th international conference on simulation of adaptive behavior. Paris, Springer

    Google Scholar 

  23. Sullivan JC, Mitchinson B, Pearson MJ, Evans M, Lepora NF, Fox CW et al (2012) Tactile discrimination using active whisker sensors. IEEE Sens J 12(2):350–362

    Article  Google Scholar 

  24. Fox CW, Evans M, Pearson M, Prescott TJ (2012a) Tactile SLAM with a biomimetic whiskered robot. IEEE International Conference on Robotics and Automation (ICRA)

    Google Scholar 

  25. O’Keefe JA, Nadel L (1978) The hippocampus as a cognitive map. London, Oxford University Press

    Google Scholar 

  26. Anderson SR, Pearson MJ, Pipe A, Prescott T, Dean P, Porrill J (2010) Adaptive cancelation of self-generated sensory signals in a whisking robot. Robotics. IEEE Trans 26(6):1065–1076

    Google Scholar 

  27. Anderson SR, Porrill J, Prescott TJ, Dean P (2012) An internal model architecture for novelty detection: Implications for cerebellar and collicular roles in sensory processing. PLoS One 7(9):e44560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Brecht M, Preilowski B, Merzenich MM (1997) Functional architecture of the mystacial vibrissae. Behav Brain Res 84(1–2):81–97

    Article  CAS  PubMed  Google Scholar 

  29. McHaffie JG, Stein BE (1982) Eye movements evoked by electrical stimulation in the superior colliculus of rats and hamsters. Brain Res 247:243–253

    Article  CAS  PubMed  Google Scholar 

  30. Sahibzada N, Dean P, Redgrave P (1986) Movements resembling orientation or avoidance elicited by electrical stimulation of the superior colliculus in rats. J Neurosci 6(3):723–733

    CAS  PubMed  Google Scholar 

  31. Favaro PD, Gouvea TS, de Oliveira SR, Vautrelle N, Redgrave P, Comoli E (2011) The influence of vibrissal somatosensory processing in rat superior colliculus on prey capture. Neuroscience 176:318–327

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cohen JD, Hirata A, Castro-Alamancos MA (2008) Vibrissa sensation in superior colliculus: wide-field sensitivity and state-dependent cortical feedback. J Neurosci 28(44):11205–11220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hemelt ME, Keller A (2007) Superior sensation: superior colliculus participation in rat vibrissa system. BMC Neurosci 8:12

    Article  PubMed  PubMed Central  Google Scholar 

  34. Miyashita E, Hamada Y (1996) The ‘functional connection’ of neurones in relation to behavioural states in rats. Neuroreport 7(14):2407–2411

    Article  CAS  PubMed  Google Scholar 

  35. Benedetti F (1991) The postnatal emergence of a functional somatosensory representation in the superior colliculus of the mouse. Dev Brain Res 60(1):51–57

    Article  CAS  Google Scholar 

  36. Drager UC, Hubel DH (1976) Topography of visual and somatosensory projections to mouse superior colliculus. J Neurophysiol 39(1):91–101

    CAS  PubMed  Google Scholar 

  37. Mitchinson B, Prescott TJ (2013) Whisker movements reveal spatial attention: a unified computational model of active sensing control in the rat. PLoS Comput Biol 9(9):e1003236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Grant RA, Mitchinson B, Fox C, Prescott TJ (2009) Active touch sensing in the rat: anticipatory and regulatory control of whisker movements during surface exploration. J Neurophysiol 101(2):862–74

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mitchinson B, Martin CJ, Grant RA, Prescott TJ (2007) Feedback control in active sensing: rat exploratory whisking is modulated by environmental contact. Proc Biol Sci 1613(274):1035–1041

    Article  Google Scholar 

  40. Mitchinson B, Grant RA, Arkley K, Rankov V, Perkon I, Prescott TJ (2011) Active vibrissal sensing in rodents and marsupials. Philos Trans R Soc Lond B Biol Sci 366(1581):3037–3048

    Article  PubMed  PubMed Central  Google Scholar 

  41. Towal RB, Hartmann MJ (2006) Right-left asymmetries in the whisking behavior of rats anticipate head movements. J Neurosci 26(34):8838–8846

    Article  CAS  PubMed  Google Scholar 

  42. Arkley K, Grant RA, Mitchinson B, Prescott TJ (2014) Strategy change in vibrissal active sensing during rat locomotion. Curr Biol 24(13):1507–1512

    Article  CAS  PubMed  Google Scholar 

  43. Hemelt ME, Keller A (2008) Superior colliculus control of vibrissa movements. J Neurophysiol 100(3):1245-1254

    Article  PubMed  PubMed Central  Google Scholar 

  44. Miyashita E, Mori S (1995) The superior colliculus relays signals descending from the vibrissal motor cortex to the facial nerve nucleus in the rat. Neurosci Lett 195(1):69–71

    Article  CAS  PubMed  Google Scholar 

  45. Gold JI, Shadlen MN (2001) Neural computations that underlie decisions about sensory stimuli. Trends Cogn Sci 5(1):10–16

    Article  PubMed  Google Scholar 

  46. Chambers J, Humphries M, Gurney KN, Prescott TJ (2011) Mechanisms of choice in the primate brain: a quick look at positive feedback. In: Seth A, Bryson JJ, Prescott TJ (eds) Modelling natural action selection. Cambridge University Press, Cambridge, pp 390–418

    Chapter  Google Scholar 

  47. Hikosaka O, Takikawa Y, Kawagoe R (2000) Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol Rev 80(3):953–978

    Google Scholar 

  48. Knill DC, Pouget A (2004) The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci 27(12):712–719

    Article  CAS  PubMed  Google Scholar 

  49. Wald A (1947) Sequential analysis. Wiley, New York

    Google Scholar 

  50. Bogacz R, Brown E, Moehlis J, Holmes P, Cohen JD (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol Rev 113(4):700–765

    Article  PubMed  Google Scholar 

  51. Gold JI, Shadlen MN (2007) The neural basis of decision making. Annu Rev Neurosci 30:535–574

    Article  CAS  PubMed  Google Scholar 

  52. Platt ML, Glimcher PW (1999) Neural correlates of decision variables in parietal cortex. Nature 400(6741):233–238

    Article  CAS  PubMed  Google Scholar 

  53. Lepora N, Pearson MJ, Mitchinson B, Evans M, Fox CW, Pipe T et al (eds) (2010) Naive Bayes novelty detection for a moving robot with whiskers. IEEE International Conference on Robotics and Biomimetics (ROBIO) Tainjin, December 14–18

    Google Scholar 

  54. Lepora N, Fox CW, Evans MH, Diamond ME, Gurney K, Prescott TJ (2012) Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination. J R Soc Interface 9(72):1517–1528

    Article  PubMed  PubMed Central  Google Scholar 

  55. Lepora NF, Evans M, Fox CW, Diamond ME, Gurney K, Prescott TJ (eds) (2010) Naive Bayes texture classification applied to whisker data from a moving robot. Neural Networks (IJCNN), The 2010 International Joint Conference on, 2010 18–23

    Google Scholar 

  56. Lepora NF, Sullivan JC, Mitchinson B, Pearson M, Gurney K, Prescott TJ (eds) (2012) Brain-inspired Bayesian perception for biomimetic robot touch. Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2012 14–18

    Google Scholar 

  57. Bogacz R, Gurney K (2007) The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Comput 19(2):442–477

    Article  PubMed  Google Scholar 

  58. Gurney K, Prescott TJ, Wickens JR, Redgrave P (2004) Computational models of the basal ganglia: from robots to membranes. Trends Neurosci 27(8):453–459

    Article  CAS  PubMed  Google Scholar 

  59. Lepora N, Gurney K (2012) The basal ganglia optimize decision making over general perceptual hypotheses. Neural Comput 24(11):2924–2945

    Article  PubMed  Google Scholar 

  60. Diamond ME, von Heimendahl M, Arabzadeh E (2008) Whisker-mediated texture discrimination. PLoS Biol 6(8):e220

    Article  PubMed  PubMed Central  Google Scholar 

  61. Catania KC, Catania EH (2015) Comparative studies of somatosensory systems and active sensing. In: Krieger P, Groh A (eds) Sensorimotor integration in the whisker system. Springer, New York This Volume

    Google Scholar 

  62. Lee L-J, Erzurumlu RS (2005) Altered parcellation of neocortical somatosensory maps in N-methyl-D-aspartate receptor-deficient mice. J Comp Neurol 485:57–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Woolsey TA, Welker C, Schwartz RH (1975) Comparative anatomical studies of the SmL face cortex with special reference to the occurrence of “barrels” in layer IV. J Comp Neurol 164(1):79–94

    Article  CAS  PubMed  Google Scholar 

  64. Keller A (1995) Synaptic organization of the barrel cortex. In: Jones EG, Diamond IT (eds) Cerebral cortex volume II: the barrel cortex of rodents. Plenum, New York

    Google Scholar 

  65. Jeffress LA (1948) A place theory of sound localization. J Comp Physiol Psychol 41(1):35–39

    Article  CAS  PubMed  Google Scholar 

  66. Joris PX, Smith PH, Yin TC (1998) Coincidence detection in the auditory system: 50 years after Jeffress. Neuron 21(6):1235–1238

    Article  CAS  PubMed  Google Scholar 

  67. Fitzpatrick DC, Kuwada S, Batra R (2000) Neural sensitivity to interaural time differences: beyond the Jeffress model. J Neurosci 20(4):1605–1615

    CAS  PubMed  Google Scholar 

  68. Wilson SP, Bednar JA, Prescott TJ, Mitchinson B (2011) Neural computation via neural geometry: a place code for inter-whisker timing in the barrel cortex? PLoS Comput Biol 7(10):e1002188

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shimegi S, Akasaki T, Ichikawa T, Sato H (2000) Physiological and anatomical organization of multiwhisker response interactions in the barrel cortex of rats. J Neurosci 20(16):6241–6248

    CAS  PubMed  Google Scholar 

  70. Ohki K, Chung S, Ch’ng YH, Kara P, Reid RC (2005) Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433(7026):597–603

    Article  CAS  PubMed  Google Scholar 

  71. Feldman DE, Brecht M (2005) Map plasticity in somatosensory cortex. Science 310(5749):810–815

    Article  CAS  PubMed  Google Scholar 

  72. Fox K, Wong RO (2005) A comparison of experience-dependent plasticity in the visual and somatosensory systems. Neuron 48(3):465–477

    Article  CAS  PubMed  Google Scholar 

  73. Wilson SP (2011) Figuring time by space: representing sensory motion in Cortical Maps [PhD]. Sheffield

    Google Scholar 

  74. Wilson SP, Law JS, Mitchinson B, Prescott TJ, Bednar JA (2010) Modeling the emergence of whisker direction maps in rat barrel cortex. PLoS One 5(1):e8778

    Article  PubMed  PubMed Central  Google Scholar 

  75. Miikkulainen R, Bednar JA, Choe Y, Sirosh J (2005) Computational maps in the visual cortex. Springer, Berlin

    Google Scholar 

  76. Leiser SC, Moxon KA (2007) Responses of trigeminal ganglion neurons during natural whisking behaviors in the awake rat. Neuron 53(1):117–133

    Article  CAS  PubMed  Google Scholar 

  77. Bell CC, Han V, Sawtell NB (2008) Cerebellum-like structures and their implications for cerebellar function. Annu Rev Neurosci 31:1–24

    Article  CAS  PubMed  Google Scholar 

  78. von der Emde G, Bell CC (1996) Nucleus preeminentialis of mormyrid fish, a center for recurrent electrosensory feedback. I. Electrosensory and corollary discharge responses. J Neurophysiol 76(3):1581–1596

    Google Scholar 

  79. Montgomery JC, Bodznick D (1994) An adaptive filter that cancels self-induced noise in the electrosensory and lateral line mechanosensory systems of fish. Neurosci Lett 174(2):145–148

    Article  CAS  PubMed  Google Scholar 

  80. Sawtell NB, Williams A (2008) Transformations of electrosensory encoding associated with an adaptive filter. J Neurosci 28(7):1598–1612

    Article  CAS  PubMed  Google Scholar 

  81. Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  82. Fujita M (1982) Adaptive filter model of the cerebellum. Biol Cybern 45(3):195–206

    Article  CAS  PubMed  Google Scholar 

  83. Blakemore S-J, Wolpert DM, Frith CD (1998) Central cancellation of self-produced tickle sensation. Nat Neurosci 1(7):635–640

    Article  CAS  PubMed  Google Scholar 

  84. Dean P, Porrill J, Ekerot CF, Jorntell H (2010) The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat Rev Neurosci 11(1):30–43

    Article  CAS  PubMed  Google Scholar 

  85. Dean P, Porrill J, Stone JV (2002) Decorrelation control by the cerebellum achieves oculomotor plant compensation in simulated vestibulo-ocular reflex. Proc Biol Sci 269(1503):1895–1904

    Article  PubMed  PubMed Central  Google Scholar 

  86. Dean P, Porrill J (2008) Adaptive-filter models of the cerebellum: computational analysis. Cerebellum 7(4):567–571

    Article  PubMed  Google Scholar 

  87. Porrill J, Dean P, Stone JV (2004) Recurrent cerebellar architecture solves the motor-error problem. Proc Biol Sci 271(1541):789–796

    Article  PubMed  PubMed Central  Google Scholar 

  88. Porrill J, Dean P (2007) Recurrent cerebellar loops simplify adaptive control of redundant and nonlinear motor systems. Neural Comput 19(1):170–193

    Article  PubMed  Google Scholar 

  89. Dean P, Porrill J (2014) Decorrelation learning in the cerebellum: computational analysis and experimental questions. Prog Brain Res 210:157–192

    Article  PubMed  Google Scholar 

  90. Wolpert DM, Miall RC, Kawato M (1998) Internal models in the cerebellum. Trends Cogn Sci 2(9):338–347

    Article  CAS  PubMed  Google Scholar 

  91. Bastian AJ (2006) Learning to predict the future: the cerebellum adapts feedforward movement control. Curr Opin Neurobiol 16(6):645–649

    Article  CAS  PubMed  Google Scholar 

  92. Porrill J, Anderson S, Dean P (2010) Can cerebellar input calibrate collicular topographic maps? BMC Neurosci 11(Suppl 1):120

    Article  Google Scholar 

  93. Teune TM, van der Burg J, van der Moer J, Voogd J, Ruigrok TJ (2000) Topography of cerebellar nuclear projections to the brain stem in the rat. Prog Brain Res 124:141–172

    Article  CAS  PubMed  Google Scholar 

  94. Moser EI, Kropff E, Moser MB (2008) Place cells, grid cells, and the brain’s spatial representation system. Annu Rev Neurosci 31:69–89

    Article  CAS  PubMed  Google Scholar 

  95. Itskov PM, Vinnik E, Diamond ME (2011) Hippocampal representation of touch-guided behavior in rats: persistent and independent traces of stimulus and reward location. PLoS One 6(1):e16462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Hasselmo ME, Schnell E, Barkai E (1995) Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. J Neurosci 15(7 Pt 2):5249–5262

    CAS  PubMed  Google Scholar 

  97. Scharfman HE (2007) The CA3 “backprojection” to the dentate gyrus. Prog Brain Res 163:627–637

    Article  PubMed  PubMed Central  Google Scholar 

  98. Fox CW, Prescott TJ (2010a) The hippocampus as a unitary coherent particle filter. International Joint Conference on Neural Networks (IJCNN). Barcelona

    Google Scholar 

  99. Taylor GW, Hinton GE, Roweis ST (2007) Modeling human motion using binary latent variables Advances in neural information processing systems (NIPS). 19. p 1345

    Google Scholar 

  100. Fox CW, Prescott TJ (2010b) Learning in the unitary coherent hippocampus. 20th International Conference on Artificial Neural Networks (ICANN). ThessalonIka

    Google Scholar 

  101. Fox CW, Evans MH, Pearson MJ, Prescott TJ (2012b) Towards hierarchical blackboard mapping on a whiskered robot. Robot Auton Syst 60(11):1356–1366

    Article  Google Scholar 

  102. Pearson M, Fox C, Sullivan JCP, T. J, Pipe AG, Mitchinson B (eds) (2013) Simultaneous localisation and mapping on a multi-degree of freedom biomimetic whiskered robot. IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe

    Google Scholar 

  103. Mitchinson B, Pearson MJ, Pipe AG, Prescott TJ (2014) Biomimetic tactile target acquisition, tracking and capture. Robot Auton Syst 62(3):366–375

    Article  Google Scholar 

  104. Pearson MJ, Mitchinson B, Sullivan JC, Pipe AG, Prescott TJ (2011) Biomimetic vibrissal sensing for robots. Philos Trans R Soc Lond B Biol Sci 366(1581):3085–3096

    Article  PubMed  PubMed Central  Google Scholar 

  105. Mitchinson B, Sullivan JC, Pearson MJ, Pipe AG, Prescott TJ (eds) (2013) Perception of simple stimuli using sparse data from a tactile whisker array. Biomimetic abd biohybrid systems. Springer, London

    Google Scholar 

  106. Prescott TJ, Pearson MJ, Mitchinson B, Sullivan JCW, Pipe AG (2009) Whisking with robots: From rat vibrissae to biomimetic technology for active touch. IEEE Robot Autom Mag 16(3):42–50

    Article  Google Scholar 

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Acknowledgement

The authors would like to acknowledge the support of the European Union FP7 BIOTACT Project ‘Biomimetic Technology for Vibrissal Active Touch’ (ICT-215910) and the Weizmann Institute of Science project “Development of motor-sensory strategies for vibrissal active touch”. We would also like to thank various collaborators whose experimental and theoretical research has inspired our biomimetic models including Mathew Diamond, Michael Brecht, Ehud Ahissar, David Golomb, Mitra Hartmann, Chris Yeo, Peter Redgrave and Kevin Gurney.

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Authors and Affiliations

  1. Department of Psychology, The University of Sheffield, Western Bank, S10 2TP, Sheffield, UK

    Tony J. Prescott, Ben Mitchinson, Stuart P. Wilson, John Porrill & Paul Dean

  2. Department of Engineering Mathematics, University of Bristol, BS8 1UB, Bristol, UK

    Nathan F. Lepora

  3. Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Sean R. Anderson

  4. Institute for Transport Studies, University of Leeds, 34-40 University Road, LS2 9JT, Leeds, UK

    Charles W. Fox

  5. Bristol Robotics Laboratory, University of the West of England, T-Block, Frenchay Campus, BS16 1QY, Bristol, UK

    Martin J. Pearson, J. Charles Sullivan & Anthony G. Pipe

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  1. Tony J. Prescott
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  2. Ben Mitchinson
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  3. Nathan F. Lepora
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  8. Charles W. Fox
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  9. Martin J. Pearson
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  10. J. Charles Sullivan
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  11. Anthony G. Pipe
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Correspondence to Tony J. Prescott .

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Editors and Affiliations

  1. Department of Systems Neuroscience, Ruhr University Bochum, Bochum, Nordrhein-Westfalen, Germany

    Patrik Krieger

  2. Institute of Neuroscience, Technische Universitaet Muenchen, Munich, Germany

    Alexander Groh

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Prescott, T. et al. (2015). The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics. In: Krieger, P., Groh, A. (eds) Sensorimotor Integration in the Whisker System. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2975-7_10

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