Bioinspired Imaging: Discovery, Emulation, and Future Prospects

Should research and development efforts in imaging technology pay specialattention to biology for inspiration? An affirmative answer would have tocontend with the obvious technicalascendancy of modern medical im-aging systems, such as computer-assisted tomography, or magneticresonance imagery, not to mentionthe pace of innovation in the ubiqui-tous cell phone camera, none ofwhich can be called ‘‘bioinspired.’’Laser-based devices, such as two-photon microscopy, have revolu-tionized imaging in neurobiology,but owe their fundamentals to discoveries in physics. Satellite imaging systemscommonly exploit hyperspectral approaches, which run counter to almostanything found in biology.On the other side of the ledger, discoveries in biological research conti-nually bring to light an astonishing array of specialized sensory systems thatanimals use to scan the natural environment, or to control their own visibility.Even seemingly unrelated work in molecular genetics can have unpredictableextensions into imaging technology. Tocite one salient example, the discoveryof how to use microbial opsin genes to genetically sensitize neurons to infraredlight has led to what the journalScience has called an ‘‘Insight of the Decade’’[1]. Its subsequent use in optogenetic imaging earned the title, ‘‘Breakthroughof the Year,’’ according to the journalNature Methods in 2010. Optogeneticscombines laser technology with newly developed techniques for the control offluorescence in light-sensitive pro-teins. It enables high spatial and tem-poralresolutioninimagingliveneuraltissue, as well as selective optical con-trol of neural activity [2].Because transformative develop-ments often arise where they are leastexpected, it would be rash to specu-late about where, in the fast-changingarena of biological research, the nextinsight or breakthrough will be foundwith high potential for ‘‘bioinspired’’or ‘‘bioenabled’’ advances in imagingtechnology. (We do not pretend thatthese are pure categories.) Nonethe-less, it is difficult to ignore the grow-ing general interest in the potentialfor biological insights to transformvarious technical endeavors. The pastfew decades attest to the growth ofacademic disciplines and researchspecialties such as biomedical en-gineering, robotics, neuromorphicsystems design, biomaterials, and bio-fabrication, all of which express con-fidence in the transformative role ofbiology as a key partner for interdis-ciplinary progress. The wide scope ofthese activities can be seen in various

[1]  Frank Wippermann,et al.  Micro-optical artificial compound eyes. , 2006 .

[2]  Thomas W. Cronin,et al.  A biological quarter-wave retarder with excellent achromaticity in the visible wavelength region , 2009 .

[3]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[4]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[5]  Sönke Johnsen,et al.  Computational visual ecology in the pelagic realm , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  T. Nagel What is it like to be a Bat , 1974 .

[7]  Miao Yu,et al.  Biomimetic optical directional microphone with structurally coupled diaphragms , 2008 .

[8]  Herbert Bousack,et al.  Designing a fluidic infrared detector based on the photomechanic infrared sensilla in pyrophilous beetles , 2012 .

[9]  H. Krapp,et al.  Sensory Systems and Flight Stability: What do Insects Measure and Why? , 2007 .

[10]  C Chubb,et al.  Cephalopod dynamic camouflage: bridging the continuum between background matching and disruptive coloration , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[11]  David L. Donoho,et al.  Scanning the Technology , 2010, Proc. IEEE.

[12]  Rob R. de Ruyter van Steveninck,et al.  Coding Efficiency and the Metabolic Cost of Sensory and Neural Information , 2000, Information Theory and the Brain.

[13]  S. Laughlin,et al.  Sensor Fusion in Identified Visual Interneurons , 2010, Current Biology.

[14]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[15]  J. R. Lackner,et al.  Influence of gravitoinertial force level on the subjective vertical during recumbent yaw axis body tilt , 2007, Experimental Brain Research.

[16]  J Scott Tyo,et al.  Optimizing imaging polarimeters constructed with imperfect optics. , 2006, Applied optics.

[17]  James Sean Humbert,et al.  Bioinspired Visuomotor Convergence , 2010, IEEE Transactions on Robotics.

[18]  Kedar D. Dimble,et al.  Electrolocation-based underwater obstacle avoidance using wide-field integration methods , 2014, Bioinspiration & biomimetics.

[19]  Radislav A. Potyrailo,et al.  Towards high-speed imaging of infrared photons with bio-inspired nanoarchitectures , 2012 .

[20]  Yi-Jun Jen,et al.  Extended broadband achromatic reflective-type waveplate. , 2012, Optics letters.