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Staff Profile

Dr Claire Rind

  • Personal Website:
  • Address: Biosciences Institute
    Henry Wellcome Building
    Framlington Place
    Â鶹´«Ã½ NE2 4HH
    UK
Background
Qualifications


1978-1981  PhD Zoology Dept. Girton College Cambridge University   
1972-1976 BSc Hons Animal Physiology (1st Class) Canterbury University, New Zealand.

Employment History
2010-present  Reader, Institute of Neuroscience.   Â鶹´«Ã½ 
2004-2009 Reader, School of Biology, Â鶹´«Ã½.
2002-2003 Lecturer, School of Biology, Â鶹´«Ã½. 
1999-2002 Lecturer, School of Neurobiology, Â鶹´«Ã½.
1989-1999 Royal Society URF, Â鶹´«Ã½.
1985-1990 BBSRC Advanced Research Fellow, Â鶹´«Ã½. During this time I had two periods of maternity leave for the births of my sons, Benjamin and Adam.
1982-1985 BBSRC PDRA, Zoology Dept. Â鶹´«Ã½
1978-1981  Commonwealth Scholar. Zoology Dept. University of  Cambridge

My early research history

Why invertebrate Neurobiology? Insects with their simple nervous systems and rich behavioural repertoire were attractive to students wanting to study the control of behaviour. As a final year undergraduate at Canterbury University in New Zealand I was motivated by two neurobiologists, Larry Field and David Blest, to investigate sensory information used by a New Zealand insect, the Weta, Hemideina maori, to co-ordinate its leg movements. Wetas are large, flightless, cricket-like insects which live communally in galleries hollowed inside trees and tree ferns. With Larry Field I discovered that a sense organ in the hind-leg hip, the femoral chordotonal organ, controlled reflexes in several leg joints. I was fascinated by the sense organ and the way its input allowed the weta to compensate for outside disturbances of its posture to ensure co-ordination of the actions of all the major leg joints. I published two papers from this undergraduate work, received a 1st class Honours degree in Animal Physiology and was awarded a Commonwealth Scholarship. I took up my scholarship in the Zoology Department in Cambridge under the supervision of Malcolm Burrows who had a newly established group and had pioneered the use of intracellular microelectrodes to look at nervous control of invertebrate behaviour. Early in my PhD, Horace Barlow, an expert on direction selective motion detection in the vertebrate retina, shaped my decision to investigate how the tobacco hornworm moth, Manduca sexta, uses vision in the control of its flight. Manduca sexta is a large sphingid moth and is a model system for developmental biologists; it hovers with extreme precision to insert its long proboscis into the nectaries of successive flowers. Its eyes are prominent, suggesting visual control is important. I was the first person to use  microelectrodes  to  characterise  directionally  selective  motion  detecting  neurons  in  Manduca  and showed that visual neurons could guide flight via direct synaptic connections with the motoneurones that controlled the angle of attack, the twisting, and, the power output of  the wing. I found visual control of behaviour fascinating and decided to pursue key questions using the locust, a better insect for behavioural neurobiology. I moved to Â鶹´«Ã½ to join Peter Simmons, a Beit Memorial  Fellow from Malcolm Burrows’ lab, as the named post-doc on his BBSRC grant to study the neuronal basis of feature detection in the locust, Locusta migratoria. Feature detection is a key task for the nervous system and allows an animal to extract important cues that could indicate  danger  while  ignoring others.  I recorded from feature detectors in the  locust  visual  system that gave selective responses to moving objects. I made a complete characterisation of the synapse between two giant feature- detecting-neurons in the  locust  brain  (Lobular  Giant Movement Detector, LGMD and the Descending Contralateral Movement Detector, DCMD), thought to be electrical. The synapse operated at a high gain and could faithfully transmit spikes separated by as little as 2.5 milliseconds, with a spike in the  LGMD  resulting  in  a  spike  in  the  DCMD  with  a synaptic delay of 1 millisecond. I discovered that the synapse, although capable of high speed transmission,  was chemical. The LGMD and DCMD neurons were thought to give advance warning of the jittery erratic movement of a predator. The strong lateral inhibition between the neurons providing  inputs to the LGMD was then interpreted as giving the neurons a preference for small stimuli. This was the accepted view when I described a class of neurons, including a second lobula giant neuron, prosaically named the LGMD2, which shared the same response as the LGMD and DCMD neurons, a brief excitation to movement in any direction of a small stimulus. I developed a two-tiered array of 18 matched intensity LEDs to deliver stimuli to characterize these neurons. To find a whole class of neurons sharing this response suggested to me that they could be signalling something important to  the locust. To test this idea I developed a wide range of edited video sequences that contained different categories of motion: motion of an object toward the viewer, horizontal motion and the flow fields over the retina generated by forward motion. Computer generated stimuli showing motion in depth were not then available. After assessing various sources for these stimuli, I settled on the movie STAR WARS  and  characterised each motion frame by frame, then correlated them with the responses recorded from the DCMD in a locust viewing the clip. I discovered that the LGMD and DCMD neurons responded best to approaching objects - here the rapid, direct approach of Darth Vader in his Tie-fighter space craft. The response tracked the growth in the image and only reached a peak when collision was imminent. Dr Peter Simmons and I discovered that the cues the LGMD and DCMD neurons used to indicate approach were edges that grew rapidly and  moved with increasing speed over the eye, both cues that were strongest when an object approached. This work has been widely cited and has spawned at least 20 other publications in high impact journals such as Nature, Nature Neuroscience, Neuron, and Current Biology. It was also awarded an Ig Nobel Prize in 2005 at Harvard University, for research that “makes you laugh and then make you think”. I have continued this work looking ever more closely at the synapses and the physiology that make the LGMD selective to looming objects.
Establishing a multidisciplinary team. The input to the LGMD and DCMD neurons had been studied using extracellular recording and some features of their static responses to a small darkening stimulus had been modelled. Inspired by Frank Werblin’s model of the salamander retina, I set out to create a computational model of motion detection, reasoning that assembling the known features of the input arrangement of the LGMD  , including  the  time constants,  patterns  and strengths of synaptic  connections,  might  create a system that responds to motion in depth, preferring approaching objects. With a group of undergraduate Electrical Engineers from Â鶹´«Ã½ including David Bramwell, I succeeded in making a 3-tiered computational model of the inputs onto the LGMD based on the anatomy and physiology of the pathway in the locust eye. With the team I also designed a graphical user interface through which we could stimulate the arrays of model photoreceptor with changing patterns of illumination equivalent to an object approaching,  or  translating, across the model retina and then record the output of each neuron in the 3 tiers, the feed forward pathway, and the LGMD. In the very first simulation with the full model, the LGMD responded best to an approaching object and only briefly to a receding one. LGMD excitation in both situations was cut back strongly by feed forward inhibition that was triggered when a large number of photoreceptors were excited simultaneously.  This happened at the beginning of recession and after approach. We also revealed the importance of a second inhibitory process, lateral inhibition between neighbouring input neurons, for the selectivity of the model. The model predicted that feed-forward inhibition should be strongest after the end of approach, which I was able  to confirm by recording intracellularly from the LGMD in the locust during both looming and receding motion. Very few visual pathways selective for motion in depth are understood in as much detail as is the locust  system, an understanding due largely to my work or to studies directly stimulated by  it.  With  an undergraduate student, Sarah Judge, I also showed that the tuning of the locust LGMD for approaching objects was very tight: objects deviating from a collision course by as little as 1.2 degrees reduced the peak response  by half. This performance exceeds that of the collision sensing neurons in the nucleus rotundas of the pigeon, the only other system where this has been measured. To do this work, with the help of Mark Blanchard, a PhD student in my lab, we programmed a Silicon Graphics computer with a large screen and rapid accurate rendering of each frame to simulate an object approaching on a non-collision trajectory. This was just the start of  an exciting collaboration with engineers, computer scientists and other neuroscientists that began in Â鶹´«Ã½ but soon spread internationally. The team have been joined by MRes students, PhD students and postdoctoral Fellows including Dr Julieta Sztarker on a visiting International EU Fellowship from Argentina and Yoshfumi Yamawaki from Japan. 

Research
I look at the brains of insects – brains that weigh a tiny fraction of the human brain but solve many of the same behavioural challenges – and extract from them details of the neural circuits that implement these solutions. As a neurobiologist, I am interested in how the brain controls behaviour, and in particular, I want to know how the internal sensory representations of the world created by the insect’s brain enable it to navigate and to make split-second judgments about imminent threats. I use physiology, anatomy, modelling and robotics to address this goal and to design sensors to perform similar tasks for humans. For example, I discovered detectors for looming motion in the locust visual system, and then developed a computational model demonstrating how looming detectors become selective for approaching objects.


Currently I have secured EU funding for scientific exchange programmes between labs that use bio-inspired designs for autonomous navigation and collision avoidance in robots, drones and vehicles. To do this I collaborate with centres of excellence in Europe, South America and the Far-East (2016-2020: Spatial-temporal information processing for collision detection in dynamic environments and 2018-2020: Ultra-layered perception with brain-inspired information processing for vehicle collision avoidance).  In the current projects the biological inspiration will be underpinned by an understanding of the locust, crab and mantis looming motion detecting pathways. This represents an excellent opportunity to visit and collaborate in labs whose focus is looming detection from different disciplines and share findings with potential end users within the consortia (STEP2DYNA, Visomorphic Technology Ltd, London; ULTRACEPT, Jaguar and Land Rover). The consortia bring together neurobiologists, neural system modellers, chip designers, and robotic researchers from Europe, Argentina Japan and China. Biological vision systems provide ideal models to develop artificial vision systems for hazard perception.


My research in the news:
• Improving collision avoidance in cars based on designs inspired by nature:.
• Swarming insects can inspire designs for collision sensors for driverless cars:
• Collaboration on artificial locust inspired robot vision with  Prof Shigang Yue in the Dept of Computer Science Lincoln University:
• Bioinspired Robotics :


Publications

  • Articles

    • Tomsic D, Rind C. . Current Biology 2023, 33(3), R108-R110.
    • Wernitznig S, Rind FC, Zankel A, Bock E, Gutl D, Hobusch U, Nikolic M, Pargger L, Pritz E, Radulovic S, Sele M, Summerauer S, Polt P, Leitinger G. . Journal of Comparative Neurology 2022, 530(2), 518-536.
    • Fu Q, Hu C, Peng J, Rind FC, Yue S. . IEEE Transactions on Cybernetics 2020, 50(12), 5074-5088.
    • Miriyala A, Kessler S, Rind FC, Wright GA. . Current Biology 2018, 28(10), 1585-1594.e3.
    • Cocks E, Taggart M, Rind FC, White K. . Journal of Microscopy 2018, 270(2), 217-234.
    • Rind FC, Wernitznig S, Polt P, Zankel A, Gutl D, Sztarker J, Leitinger G. . Scientific Reports 2016, 6, 35525.
    • Wernitznig S, Sele M, Urschler M, Zankel A, Polt P, Rind FC, Leitinger G. . Journal of Neuroscience Methods 2016, 264, 16-24.
    • Wernitznig S, Rind FC, Polt P, Zankel A, Pritz E, Kolb D, Bock E, Leitinger G. . Journal of Comparative Neurology 2015, 523(2), 298-312.
    • Sztarker J, Rind FC. . Developmental Neurobiology 2014, 74(11), 1078-1095.
    • Yue S, Rind FC. . Neurocomputing 2013, 103, 50-62.
    • Simmons PJ, Sztarker J, Rind FC. . Journal of Experimental Biology 2013, 216, 2266-2275.
    • Yue S, Rind FC. . International Journal of Advanced Mechatronic Systems 2012, 4(5-6), 237-247.
    • Leitinger G, Masich S, Neumüller J, Pabst MA, Pavelka M, Rind FC, Shupliakov O, Simmons PJ, Kolb D. . Journal of Comparative Neurology 2012, 520(2), 384-400.
    • Santer RD, Rind FC, Simmons PJ. . PloS One 2012, 7(11), e50146.
    • George DM, Rind FC, Bendall MW, Taylor MA, Gatehouse AMR. . Pest Management Science 2012, 68(3), 330-339.
    • Rind FC, Birkett CL, Duncan BJA, Ranken AJ. . Journal of Experimental Biology 2011, 214(11), 1874-1879.
    • Yue S, Santer RD, Yamawaki Y, Rind FC. . Autonomous Robots 2010, 28(2), 151-167.
    • Santer RD, Yamawaki Y, Rind FC, Simmons PJ. . Journal of Comparative Physiology A 2008, 194(1), 69-77.
    • Liñán-Cembrano G, Carranza L, Rind C, Zarandy A, Soininen M, Rodríguez-Vázquez A. . IEEE Circuits and Systems magazine 2008, 8(2), 6-24.
    • Rind FC, Santer RD, Wright GA. . Journal of Neurophysiology 2008, 100(2), 670-680.
    • Stafford R, Santer RD, Rind FC. . Animal Behaviour 2007, 74(6), 1813-1819.
    • Stafford R, Rind FC. . Neurocomputing 2007, 70(4-6), 1079-1084.
    • Yue S, Rind FC. . Artificial Life 2007, 13(2), 93-122.
    • Stafford R, Santer RD, Rind FC. . BioSystems 2007, 87(2-3), 164-171.
    • Yue S, Rind FC. . Computer Vision and Image Understanding 2006, 104(1), 48-60.
    • Santer RD, Rind FC, Stafford R, Simmons PJ. . Journal of Neurophysiology 2006, 95(6), 3391-3400.
    • Yue S, Rind FC. . IEEE Transactions on Neural Networks 2006, 17(3), 705-716.
    • Yue S, Rind FC, Keil MS, Cuadri J, Stafford R. . Neurocomputing 2006, 69(13-15), 1591-1598.
    • Santer RD, Stafford R, Rind FC. . Journal of the Royal Society Interface 2004, 1(1), 61-77.
    • Leitinger G, Pabst MA, Rind FC, Simmons PJ. . Journal of Comparative Neurology 2004, 480(1), 89-100.
    • Rind FC, Santer RD. . Proceedings of the Royal Society B: Biological Sciences 2004, 271(Suppl. 3), S27-S29.
    • Rind FC. . Microscopy Research and Technique 2002, 56(4), 256-269.
    • Blanchard M, Rind C, Verschure PFMJ. . Neurocomputing 2001, 38-40, 1113-1119.
    • Blanchard M, Verschure PMJ, Rind FC. . International Journal of Neural Systems 1999, 9(5), 405-410.
    • F. C. Rind and P. J. Simmons. Local circuit for the computation of object approach by an identified visual neuron in the locust. Journal of Comparative Neurology 1998, 395, 405-415.
    • F. C. Rind and P. J. Simmons. Signalling of object approach by the DCMD neuron of the locust. Journal of Neurophysiology 1997, 77, 1029-1033.
    • P. J. Simmons and F. C. Rind. Responses to object approach by a wide field visual neurone, the LGMD2 of the locust: Characterization and image cues. Journal of Comparative Physiology a-Sensory Neural and Behavioral Physiology 1997, 180, 203-214.
    • F. C. Rind and D. I. Bramwell. Neural network based on the input organization of an identified neuron signaling impending collision. Journal of Neurophysiology 1996, 75, 967-985.
    • RIND FC. A CHEMICAL SYNAPSE BETWEEN 2 MOTION DETECTING NEURONS IN THE LOCUST BRAIN. JOURNAL OF EXPERIMENTAL BIOLOGY 1984, 110(MAY), 143-&.

  • Book Chapters

    • Rind FC. . In: Christensen, T.A, ed. Methods in Insect Sensory Neuroscience. London, New York: CRC Press, 2005, pp.213-235.
    • Rind FC, Santer RD, Blanchard JM, Verschure PFMJ. . In: Barth, FG; Humphrey, JAC; Secomb, TW, ed. Sensors and Sensing in Biology and Engineering. New York, USA: Springer, 2003, pp.237-250.
    • Subramaniam K, Shukla S, Dlay SS, Rind FC. . In: Advances in Physics, Electronics and Signal Processing Applications. World Scientific Engineering Society, 2000, pp.252-257.
    • Subramaniam K, Dlay SS, Rind FC. . In: Computers and Computational Engineering in control. Singapore: World Scientific and Engineering Society Press, 1999, pp.335-340.

  • Conference Proceedings (inc. Abstracts)

    • Shigang Y, Rind FC. . In: Proceedings - IEEE International Conference on Robotics and Automation. 2005, Barcelona, Spain: IEEE.
    • Rind FC, Blanchard M, Verschure PFMJ. . In: Proceedings of SPIE - The International Society for Optical Engineering. 2000, Boston, USA: The International Society for Optical Engineering.

  • Editorial

    • Rind FC, Bels V. . Frontiers in Behavioral Neuroscience 2024, 18, 1367484.

  • Letter

    • Rind FC, Simmons PJ. . Trends in Neurosciences 1999, 22(10), 438-438.

  • Reviews

    • Rind FC. . Current Opinion in Insect Science 2024, 63, 101180.
    • Rind FC, Simmons PJ. . Trends in Neurosciences 1999, 22(5), 215-220.