Professor Mandyam Srinivasan
Head of Visual and Sensory Neuroscience
Queensland Brain Institute

Email: M.Srinivasan@uq.edu.au

Research Team

Judith Reinhard, PhD

Charles Claudianos, PhD

Dean Soccol, BE (Hons), Mech. Engg

Saul Thurrowgood (Bachelor of Computing with Highest Distinction)

Professor Mandyam Srinivasan FAA, FRS

Short biography

Professor Mandyam Srinivasan moved to the University of Queensland in January 2007 to take up a Professorship in Visual Neuroscience at the Queensland Brain Institute (QBI).

In August 2007, Professor Srinivasan was awarded the Queensland Smart State Premier's Fellowship.

Before coming to UQ, Professor Srinivasan headed a 20-strong team at the Australian National University where – for more than two decades – his laboratory produced some 180 publications, including 21 in high-impact journal  articles in publications such as Nature, Science, PNAS, PLOS Biology and Current Biology.

Professor Srinivasan came to Australia from the University of Zurich in 1985 to research honeybee vision. What started as a one-person operation – where he did the beekeeping, designed and ran the experiments, analysed and interpreted the data, and wrote the papers – has now become a multidisciplinary team that is the focus of widespread national- and international attention.

In October 2006, Professor Srinivasan was awarded the Prime Minister's Prize for Science.

Description of research area

By studying the behaviour of small animals, such as insects, Professor Srinivasan and his colleagues have demonstrated that many relatively simple nervous systems nevertheless display a rich behavioural repertoire. The Srinivasan laboratory seeks to elucidate principles of flight control and navigation, and to explore the limits of the 'cognitive' capacities of small brains.

An understanding of visual processing in insects may provide simple, novel solutions to problems in machine vision and artificial intelligence. Thus, another focus of Srinivasan laboratory is the design of biologically inspired algorithms for 'seeing' machines, and the development of autonomously navigating robots.

Novel discoveries (Low-level vision and navigation)  [top]

• First development of the concept of “Predictive Coding”, leading to a completely novel interpretation of the significance of early visual processing. (Proc. R Soc Lond B 1982). This study models early visual processing in a quantitative fashion and postulates that its role is to eliminate redundant information in the visual image and transmit only the unpredictable components of it to the brain. Thus, information captured by the retina is encoded and transmitted to the brain in an efficient manner. This paper has come to be regarded as a key milestone in the literature on visual information processing, and has practical applications in image compression and encoding.

• First discovery that flying insects use cues based on image motion to gauge the distances to objects and perceive the world in three dimensions (Nature 1988; J. Comp. Physiol. A 1989). This seminal work demonstrated that bees could learn to discriminate between artificial flowers of different heights in an artificial ‘meadow’. Their ability to do so was mediated by discrimination of the relative speeds of the images of the flowers in the eye during flight over the meadow. These findings revealed that most flying insects are likely use image motion cues, rather than binocular stereo, to perceive the world in three dimensions. The results have important applications in robot vision and navigation.

• First demonstration that flying insects negotiate narrow gaps between obstacles safely by balancing the image speeds in the two eyes (Visual Neuroscience 1991), rather than by using complex stereo mechanisms for measuring distance. Image motion appears to be monitored by a system that measures image velocity largely independently of the spatial texture and the contrast of the image, thus enabling centred flight through a gap between, say, two tree trunks, irrespective of the textures of their barks. In an extension of this biological finding, four different laboratories around the world have built robots that use this principle to navigate autonomously along corridors, for applications in the transportation and mining industries (Robotics and Autonomous Systems 1999).

• First discovery that flying insects use an unexpectedly simple and elegant strategy for landing on flat surfaces (Biological Cybernetics 2000). Image velocity is held constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. The feasibility of this landing strategy has been successfully tested by implementation on a robotic gantry, and is presently being implemented for testing on airborne vehicles.

• First convincing demonstration that honeybees gauge distance flown in terms of the extent to which the image of the environment moves on the eye en route to the goal. (Science 2000; Nature 2001). These studies rewrite the classic, textbook notion that distance flown is estimated in terms of energy consumption, as put forward originally by the Nobel laureate Karl von Frisch.

• First to introduce the concept of motion camouflage in target tracking and to investigate the possibility of its existence in flying insects (Proc. R. Soc. Lond. B. 1995; Nature 2003). This finding has potential applications in the design of unmanned aerial combat vehicles (UCAVs).

Novel discoveries (Perception and ‘cognition’) [top]

• First discovery that the honeybee visual system analyses pattern orientation rather like the mammalian cortex (Nature 1993, Phil. Trans. R. Soc. Lond. B 1994). As in mammalian vision, orientation appears to be analysed by a set of orientation-tuned channels, although in the honeybee the channels are fewer in number and more broadly tuned. In functional terms, therefore, the bee appears to possess a “minimal” cortex. This finding overturned the conventional thinking about insect brains.

• First discovery that visual systems of insects possess ‘top-down’ processing: Prior knowledge enhances the detection of otherwise camouflaged objects (Nature 1994). Bees can be trained to detect camouflaged objects and to discriminate between them, by pre-training them on uncamouflaged versions of the same objects. Once bees have learnt how to break the camouflage, they can use this principle to detect and discriminate other, novel shapes. These findings have potential applications in the design of machine vision systems for surveillance.

• First demonstration that honeybees exhibit ‘associative recall’, that is, exposure to one stimulus, such as a scent, can trigger recall of another stimulus, such as a colour – that was associated with it (Nature 1998). This faculty is likely to aid a foraging honeybee in finding, memorizing and reliably returning to a good source of food.

• First demonstration that bees can learn the concepts of ‘similarity’ and ‘dissimilarity’ (Nature 2001). The ability to learn these concepts can facilitate efficient foraging by ensuring the reliable and consistent choice of nectar-bearing flowers.

• First demonstration that scent can trigger complex navigational memories in honeybees (Nature 2004). Scent injected into a hive can stimulate experienced bees to fly to previously visited sites, through previously formed associations between scent and location.

• First elucidation of working memory in an invertebrate, showing that bees can learn to perform delayed match-to-sample tasks just like humans and higher vertebrates. Proceedings of the National Academy of Science 2005).

• First demonstration of lateralization of learning in an invertebrate (Current Biology 2006), revealing that invertebrates have “handedness”, like humans.

Research Team  [top]

Dr Judith Reinhard, PhD – insect behaviour (specialising in honeybees) with 9 years’ postdoctoral experience and 25 publications in high-profile international journals, including Nature.

Dr Charles Claudianos, PhD – invertebrate genetics, neuroscience, molecular biology and evolution (specialising in honeybees), with 7 years’ postdoctoral experience and 30 publications in high-profile international journals, including Science and PNAS. DR Claudianos was one of the Theme Leaders of the recent, highly acclaimed Honeybee Genome Project, published in Nature (2006).

Dean Soccol (B.E. Hons, Mech. Engg) – mechatronics with 6 years’ experience in the field of Biorobotic Engineering, specialising in engineering design and fabrication in relation to aircraft structures and vision systems. Dean has worked on research contracts with NASA and the US Army. Prior experience with running a mechanical workshop at the ANU which served a Research School of about 300 people.

Saul Thurrowgood (Bachelor of Computing with Highest Distinction) – software engineer with 6 years’ experience in the field of Biorobotic Engineering, specializing in aircraft avionics, control and navigation. Saul has worked on research contracts with NASA and the US Army.

Scientific collaborations [top]

At UQ, Professor Srinivasan interacts closely with the laboratories of Prof. Perry Bartlett (neural plasticity), Prof. Pankaj Sah (synaptic plasticity), Prof. Jason Mattingley (cognitive and behavioural neuroscience), Prof. David Vaney (retinal neuroarchitecture), Prof. Justin Marshall (visual ecology), Prof. Janet Wiles (complex systems) and Dr. Gordon Wyeth (mechatronics).

These interactions will be facilitated by the recent ARC Thinking Systems grant Navigation in real and conceptual spaces awarded to UQ, as well as the ARC Centre of Excellence in Vision Science (ACEVS), where he is one of the 3 theme leaders (Theme 2: Vision for action and robotics).

The move brings to the QBI a new, but complementary focus of research that elucidates the performance of small, but smart nervous systems and explores applications of this research to robotics.

It will strengthen and bring together vision research across the UQ campus, as well as build a critical mass in the robotics area through interactions with Prof. Janet Wiles and Dr. Gordon Wyeth (School of Information Technology and Electrical Engineering, UQ) and Dr. Peter Corke (CSIRO ICT, Brisbane).

These interactions will be fostered through the Thinking Systems Program and the ACEVS.

Professor Srinivasan’s laboratory has ongoing collaborations with the University of Melbourne (Prof. K. Rao), the University of Monash (Prof. R. Jarvis) and Curtin University (Prof. S. Venkatesh) through my participation in the ARC Centre for Perceptive and Intelligent Machines.

His laboratory also has collaborations with several overseas organisations, including the Universities of Zurich (Switzerland), the Ecole Polytechnique Federale (Lausanne), the University of Maryland (USA), the University of Bielefeld (Germany), the University of Illinois (USA), the University of Wuerzburg (Germany), the University of Lichtenstein, and Tokyo Medical and Dental University (Japan).

Through Professor Srinivasan’s involvement as a Theme Leader in the recently-funded ACEVS, he will continue collaborations with a number of vision researchers at the ANU on related projects.

Representative publications [top]

Srinivasan M.V., Laughlin S.B. and Dubs A. (1982) Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B 216, 427-459.

Lehrer M., Srinivasan M.V., Zhang S.W. and Horridge G.A. (1988) Motion cues provide the bee's visual world with a third dimension. Nature (Lond.) 332, 356-357.

Srinivasan M.V., Lehrer M., Kirchner W.H. and Zhang S.W. (1991) Range perception through apparent image speed in freely-flying honeybees. Visual. Neuroscience 6, 519-535.

Srinivasan M.V., Zhang S.W. and Rolfe B. (1993) Pattern vision in insects: "cortical" processing? Nature (Lond.) 362, 539-540. (This paper was accompanied by a News and Views article)

Zhang S.W. and Srinivasan M.V. (1994) Prior experience enhances pattern discrimination in insect vision. Nature (Lond.) 368, 330-332. (This paper attracted a short article in New Scientist)

M.V. Srinivasan (1994): An image-interpolation technique for the computation of optic flow and egomotion. Biol. Cybernetics 71, 401-416.

Zhang S.W., Srinivasan M.V. and Collett T.S. (1995) Convergent processing in honeybee vision: Multiple channels for the recognition of shape. Proc. Nat. Acad. Sci. U.S.A. 92, 3029-3031.

Srinivasan, M.V., Zhang, S.W., Lehrer, M. and Collett, T.S. (1996) Honeybee navigation enroute to the goal: visual flight control and odometry. J. Exp. Biol. 199, 237-244.

Srinivasan M.V., Zhang S.W. and Bidwell N. (1997) Visually mediated odometry in honeybees. J. Exp.Biol 200, 2513-2522. (This paper attracted an article in New Scientist)

J.S. Chahl and M.V. Srinivasan (1997) Reflective surfaces for panoramic imaging. Applied Optics 36, 8275-8285.

Srinivasan M.V., Zhang S.W. and Zhu H. (1998) Honeybees link sights to smells. Nature (Lond.) 396, 637-638. (This paper attracted an article in New Scientist)

Srinivasan M.V., Chahl J.S., Weber K.S., Venkatesh S., Nagle M.G. and Zhang S.W. (1999): Robot navigation inspired by principles of insect vision. Robotics and Autonomous Systems 26, 203-216.

Srinivasan M.V., Zhang S.W., Altwein M. and Tautz J. (2000) Honeybee navigation: nature and calibration of the ‘odometer’. Science 287, 851 – 853. (With cover illustration and accompanying Perspectives article)

Srinivasan M.V., Zhang S.W., Chahl J.S., Barth E., and Venkatesh S. (2000) How honeybees make grazing landings on flat surfaces. Biological Cybernetics 83, 171-183. (This paper attracted an article in New Scientist)

H. Esch, S.W. Zhang, M.V. Srinivasan and J. Tautz (2001) Honeybee dances communicate distances measured by optic flow. Nature 411, 581-583. (With cover illustration).

M. Giurfa, S.W. Zhang, A. Jenett, R. Menzel and M.V. Srinivasan (2001) The concepts of “sameness” and “difference” in an insect. Nature 410, 930-933.

Barrows G.L., Chahl J.S. and Srinivasan M.V. (2003) Biomimetic visual sensing and flight control. The Aeronautical Journal, London : The Royal Aeronautical Society, vol, 107, No. 1069, pp. 159-168. (This paper was awarded the Royal Aeronautical Society’s Simms Prize for the best paper of the year).

Srinivasan M.V. (2002) Visual Flight Control and Navigation in Honeybees, and Applications to Robotics. In: Neurotechnology for Biomimetic Robots, J. Ayers, J.L. Davis and A. Rudolph (eds.), MIT Press, pp 593 – 610.

Reinhard, J., Srinivasan, M.V. and Zhang, S.W. (2004) Scent-triggered navigation in honeybees. Nature (Lond.) 427, 411.

M.V. Srinivasan., S.W. Zhang, J S Chahl, G Stange and M Garratt (2004) An overview of insect inspired guidance for application in ground and airborne platforms. Proc Inst Mech Engnrs Part G: J Aerospace Engineering 218, 375-388.

S.W. Zhang, F. Bock, A. Si, J. Tautz and M.V. Srinivasan (2005) Visual working memory in decision making by honeybees. Proceedings of the National Academy of Science 102, 5250-5255. (This paper was selected by the Faculty of 1000)

From Living Eyes to Seeing Machines, M.V. Srinivasan and S. Venkatesh (eds), Oxford University Press, U.K. (1997).

Most recent scientific papers [top]

J. Reinhard, M.V. Srinivasan and S.W. Zhang (2006) Complex memories in honeybees: Can there be more than two? Journal of Comparative Physiology A 192, 409-416.

A. Barron and M.V. Srinivasan (2006) Visual regulation of ground speed and headwind compensation in freely flying honey bees (Apis mellifera L.) Journal of Experimental Biology 209, 978-984.

A. Veeraraghavan, M.V. Srinivasan, R. Chellappa, E. Baird, R. Lamont (2006) Motion Based Correspondence for 3D Tracking of Multiple Dim Objects'. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, May 14-19, (ICASSP), Toulouse, France

M.V. Srinivasan (2006) Small brains, smart computations: Vision and navigation in
honeybees, and applications to robotics. Brain-Inspired IT II. International Congress Series, vol 1291, 3-37.

P. Letzkus, W.A. Ribi, J.T. Wood, H. Zhu, S.W. Zhang, M.V. Srinivasan (2006).
Lateralization of olfaction in the honeybee Apis mellifera. Current Biology 16, 1471-
1476.

E. Baird, M.V. Srinivasan, S.W. Zhang, R. Lamont and A. Cowling (2006) Visual
control of flight speed and height in the honeybee. Lecture Notes in Artificial
Intelligence, S. Nolfi et al. (eds), 4095, 40-51, Springer Berlin/Heidelberg.

M.V. Srinivasan, S. Thurrowgood and D. Soccol (2006) An optical system for guidance of terrain following in UAVs. Proceedings, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS ’06), Sydney, pp. 51-56.

U. Schroeter, S.L.J. Wilson, M.V. Srinivasan, M.R. Ibbotson. The morphology,
physiology and function of suboesophageal neck motor neurons in the honeybee.
Journal of Comparative Physiology A (in press).

M. Dacke and M.V. Srinivasan. Honeybee navigation: Distance estimation in the third dimension. Journal of Experimental Biology (in press).

C. McCarthy, N. Barnes and M.V. Srinivasan: Real time biologically-inspired depth maps from spherical flow. Proceedings, International Conference on Robotics andAutomation (in press)