Bradshaw - Sensory Information Systems - Spring Review 2013

1 DISTRIBUTION A: Approved for public release; distribution is unlimited. 26 February 2013

Integrity Service Excellence

Patrick Bradshaw

AFOSR/RTE

Air Force Research Laboratory

Sensory

Information

Systems Program

8 March 2013

2 DISTRIBUTION A: Approved for public release; distribution is unlimited.

2013 AFOSR SPRING REVIEW

PORTFOLIO OVERVIEW

BRIEF DESCRIPTION OF PORTFOLIO:

•Auditory modeling for acoustic analysis

•Biological polarization optics & vision

•Sensori-motor control of bio- flight & navigation

SUB-AREAS IN PORTFOLIO:

Sensory Information Systems (3003/L)

Program Officer: Patrick Bradshaw


Program Trends and Strategy: TOPIC AREA OVERVIEW

Polarization Vision & Optics:

Sensorimotor Control of Flight & Navigation:

Scientific Question: How do natural photoreceptors detect and how do animal brains interpret polarization information? How is it used for nocturnal navigation or recognition of obscured targets? Can these unique bio-optical systems be emulated?

Scientific Question: How does neural control make natural, low-Reynolds No. flight autonomous, efficient, and robust? Discover principles of multisensory fusion, distributed sensors and actuators. Develop control laws for emulation in MAVs.

Advanced Auditory Modeling:

Scientific Question: How does the auditory brain parse acoustic landscapes, bind sensory inputs, adapt its filters, hear through noise and distortion? Could autonomous listening devices emulate neurology to match or exceed human auditory analysis, e.g., to detect and identify speech targets in noise and reverberation?

40%

12%

47%

Strategy: Forge useful connections between math and biology

AFOSR

BioNavigation

Research

Initiative


TO: Navy: Bio-inspired method to classify acoustic sources, e.g., vehicles,

humans, marine animals etc., using cortical auditory model. Dr. Sam Pascarelle,

Advanced Acoustic Concepts, Inc.

TO: AFIT: Techniques in electrophysiology and neuroanatomy for

mechanical engineering projects to emulate flapping wing flight. Dr. Mark

Willis, Case Western U., Dr. Anthony Palazotto, AFIT

TO: DARPA: System for adaptive, autonomous control of robotic

movement, based upon hierarchical neural model of biological control.

Roger Quinn &. Roy Ritzmann, Case West. U.; G. Pratt, DARPA.

TO: Bloedel Hearing Institute: New, patented method to improve

auditory coding in cochlear implants. Developed by Dr. Les Atlas, U. Wash. Dr. Jay

Rebenstein will develop commercial applications.

TO: AFRL-- Eglin: Measurements and data on biological wide-field-of-view

optical systems to enable 6.2 and 6.3 efforts in vision-based guidance and

navigation. D. Stavenga, U. Groningen, N. Strausfeld, U. Arizona, M. Wehling, et al.

Recent Transitions


Subprogram:

Auditory Modeling for Acoustic Analysis

AIMS

Understand how neural signal

processing in the auditory

brain can advance the design

of acoustic techniques for

noise suppression, directional

hearing, and speech analysis

ACCOMPLISHMENTS (Previously Reported}

• Mathematical and experimental analysis

of acoustic propagation via skull, middle

ear, and soft tissue

• Hybrid feed-forward, feed-back adaptive

noise cancellation

• Restoration of 3D spatial hearing via

headphones

CURRENT PROJECTS

Dynamic response of cochlear

hair cells. Bozovic, UCLA

Advanced methods for binaural

synthesis. Hartmann MSU

Neural Oscillations in Auditory

Cognition. Large, Circular Logic

Spectral, & contextual constraints

on 3D hearing. Iyer, Simpson, AFRL/RH

Analysis & control of Informational

Masking. Kidd, Boston U.

= REPORTED IN 2012 SPRING REV.


A Speech Analysis Breakthrough

Speech intelligibility improves markedly for

normal-hearing (NH) listeners and for hearing-

impaired (HI) listeners

FIRST TECHNIQUE TO APPLY COMPUTATIONAL AUDITORY

SCENE ANALYSIS TO IMPROVE MONAURAL INTELLIGIBILITY

Unpublished data. Status Report 1, STTR Phase II Project: "An Auditory Scene Analysis Approach to

Speech Segregation” Dr. DeLiang Wang, Ohio State University

Individual Speech Hearing Performance against Multitalker Babble


An Auditory Modeling Transition

for Acoustic Analysis

Key stages of hypothesized auditory processing,

from detection of an acoustic signal to

recognition of its source in ambient noise.

AFOSR Sensory System Program grants: FA9550-07-C-0095, 0017, FA9550-12-1-0388 to Dr.

Edward Large, Circular Logic, LLC.. SIBR: John Hall, Michael Spottswood 711HPW/RHCB

Unique dynamical system

model for auditory scene

analysis, based upon neural

gradient frequency networks

& Hebbian adaptation

AFOSR 6.1 Research

SBIR

Topic AF112-024

Listener Performance

Modeling in Urban

Environments


Polarization Biology Sub-Program

AIMS

HIGHLIGHTS (Previously Reported)

NEW DEVELOPMENTS

A CURRENT FOCUS Discover biological mechanisms

for sensing, control, and analysis

of polarized light….Derive new

optical information techniques.

• Mapped genetic landscape for

opsins in polarization vision.

• Discovered photoreceptor for

circular polarization detection.

• Discovered achromatic 1/4 wave

retarder membrane & deduced its

unique optical structure.

• Transitioned bio- polarization

principles for optical scene analysis.

Discover neural information-processing

principles that achieve successful

multiplexing & integration of spectral

and polarization signals. …Model and

emulate this in computational systems.

A multidisciplinary, coordinated grant.

BRISTOL, UK, QUEENSLAND, UMBC, & WUSTL

• Natural Dichroic Polarizer

• Polarization-Neutral Reflector

• High Resolution Pol Vision


A Natural Dichroic Polarizer

Used for Visual Signalling

Animal (Mantis Shrimp) uses

dichroic carotenoid molecule

(astaxanthin) in a movable

antennae to produce time-

varying polarization signals in

specific directions.

UMBC, U. Queensland, UC Berkeley: Chiou, et al., J. Exp. Biology 2012.

Broadband polarization

transmission depends on

antenna orientation

Polarization-active layer in

antennal scale,

Odontodactylus scyllarus

First-reported

biological dichroic

polarizer


Discovered: Mechanism of Biological

Non-Polarizing Reflectors

• T. M. Jordan, J. C. Partridge & N.W. Roberts. Univ. of Bristol.

• Nature Photonics 2012.

Unique optical reflectors:

Suppress polarization at all

angles

• Layers of guanine cytoplasm crystals

are broadband birefringent.

• No refractive index mismatch between

crystal layers and the external medium.

• Each crystal optical axis aligns with its

long axis or orthogonal to its plane.

• Optical design could be exploited in

synthetic devices.

Silvery fish avoid the Fresnel effect

(loss of reflectivity, gain of

polarization at Brewster’s angle):

They maintain polarization

camouflage in all directions


Discovered: High-Resolution

Polarization Vision

Mourning Cuttlefish, Sepia plangon

In most polarization-sensing animals, acuity for polarization orientation (the “e-vector” angle) ranges from about 10 to 20 degrees. This cuttlefish, which lacks color vision, shows (via a change in skin coloration) that it can discriminate between targets and backgrounds based on only a 1 degree difference in e-vector angle … the highest biological resolution yet known. Research continues on how high-resolution polarization sensory systems are employed In covert signaling and camouflage.

S. E. Temple, et al., Current Biology (2012) supported by AFOSR Sensory Information Systems Program


Bombus terrestris Megalopta genalis

(Nocturnal Bee)

Hawks & Falcons

Echolocating Bats

Dragonfly Hawk moth

Span of Natural Flier Research: A Few Program Participants

Vertebrates Invertebrates

http://www.brown.edu/Departments/EEB/EML/images/cynoflying3.jpg

http://www.google.com/imgres?imgurl=http://www.globalcraftsb2b.com/catalog/images/dragonfly.jpg&imgrefurl=http://www.globalcraftsb2b.com/catalog/index.php?cPath=121_69&h=400&w=400&sz=28&tbnid=jDbdE_V39SNQcM:&tbnh=124&tbnw=124&prev=/images?q=dragonfly+pictures&zoom=1&q=dragonfly+pictures&hl=en&usg=__IAK85ePRP-6nUwcU9VQXDGqHrck=&sa=X&ei=v5ULTaz2HYL6lweS-qGlDA&ved=0CCYQ9QEwAw


Sensori-motor Control of Natural Flight and Navigation

= AFOSR BIO-NAVIGATION FUNDING INITIATIVE

P. Krishnaprasad ( U. MD): Modeling formation flight control

T. Daniel ( U. Washington):

Wing mechanosensor functions.

H. Krapp ( Imp. College London):

Neural basis of visual steering

S. Humbert ( U. Maryland):

Modeling sensorimotor control

M. Frye ( UCLA): Higher-order motion detection

S. Reppert ( U. Mass): Clock-compensated navigation

R. Ritzmann (Case Western): Adaptive locomotion control

J. Evers (AFRL/RW):

Natural 3D flight dynamics

G. Taylor (Oxford): Raptor pursuit strategies in 3D

E. Warrant (Lund U.): Nocturnal navigation

P. Shoemaker (Tanner Res.):

Visual detection of small targets

M. Willis (Case Western):

Visual / olfactory target tracking

R. Olberg ( Union College): Dragonfly flight to target capture

S. Sterbing ( U. MD): Wing sensors in bat flight control

M. Wehling ( AFRL/RW):

Neural analysis of optic flow.

S. Sane ( Tata Institute):

Insect multisensory integration


AFRL-DSTL Working Group

Biologically-Motivated Micro-Air-Vehicles

“STATE OF THE ART REVIEW”

Georgia Tech

15-18 June, 2010

Organizers: M. Wehling, AFRL. P. Biggins, Dstl

Presentations: https://livelink.ebs.afrl.af.mil/livelink/llisapi.dll?func=ll&objId=24091294

&objAction=browse&viewType=1

30 Participants from UK, US, Industry, Academia, & Gov.

H. Krapp

J. Niven

G. Taylor

T. Daniel

S. Humbert

M. Willis

U.S. U.K.

International and 6.2 Coordination

http://en.wikipedia.org/wiki/File:Georgia-Tech-Insignia.svg

https://livelink.ebs.afrl.af.mil/livelink/llisapi.dll?func=ll&objId=24091294&objAction=browse&viewType=1

https://livelink.ebs.afrl.af.mil/livelink/llisapi.dll?func=ll&objId=24091294&objAction=browse&viewType=1


Fundamental Question:

What underlying principles drive

biology’s design of actuation and

sensing architectures?

Motivating Observations

from Insect Research :

• Sensors are “noisy,” redundant, distributed

in non-orthogonal coordinates.

• Inputs fuse across modalities prior to

activating flight muscles.

• No conventional distinctions between

estimate/control or inner/outer loop

• Sensors differ radically in bandwidth &

temporal response, e.g., vision lags

mechanoreception. Insect Lab, Eglin AFB

Dr. Jennifer Talley

Sensori-motor Control of Natural Flight and Navigation


Wings are Sensory Receptors - - - A New Research Effort

Hawkmoth, Manduca sexta

MECHANICAL

INPUT

Wing Campaniform Neurons Respond to Mechanical Forces

NEURON

RESPONSE

4-NEURON

COHERENCE

INPUT POWER SPECTRUM

CORIOLIS REGIONS

Hypothesis:

Wings not only drive flight, but also detect inertial moments.

- - - strain receptors modulate wing shape and position.

T. Daniel (biology) and K. Morgensen (Aeronautics) U. Washington.

http://www.biology.washington.edu/users/thomas-daniel

http://www.biology.washington.edu/users/thomas-daniel


Wings are Sensorimotor Surfaces

• Discovering their Role in Flight

Control

BATS

HAWKMOTHS

Measure joint muscle activity during flight.

What is controlled? Force or position?

Discover role of intrinsic (non-joint) muscles.

Record from strain receptors during wing motion.

Measure dynamics of response to mechanical inputs.

Map neural signal from wing to flight motor.

Find and study hair sensors on wings.

Measure response to air flow.

Map wing sensor circuits to cortex.


Mechanoreceptors in Flight Control

Sterbing-D’Angelo et al., PNAS 2011, Sterbing-D’Angelo & Moss (in press)

Tactile Hairs have directional bias:

Many favor reverse air flow.

Air flow reverses during low-speed flight

when vortices form and flow separates

Scientific Challenge:

Finding:

Hypothesis:

Tactile hairs indicate stall?

They enable the bat to stabilize

flight when airflow is disrupted?

Discover the role of wing receptors in

flight control.


Mechanoreceptors on Bat Wings

Cortical response to

single-unit stimulation

Flight Velocity

Pro

xim

ity t

o O

bstr

uc

tio

ns

Removing tactile hairs

alters flight steering

Tactile wing hair

200m length, 5m diameter base

ba

• Ringed by Merkel cells

• Responsive to air puffs

• Mapped to sensory cortex

• Needed for flight agility

Current Findings:

Sterbing-D’Angelo et al., PNAS 2011, Sterbing-D’Angelo & Moss (in press)


Biological Flight Coherence Inspires

New Control Laws

Key Insights:

In 2 dimensions, obstacle avoidance and

boundary tracking pose similar problems for

flight coordination, but 3-dimensional

coordinated flight requires a new approach.

Gyroscopic modeling enables biological

interpretations and tests. Illustration of a gyroscopic

boundary- tracking law

following circular motion

on the surface of a sphere.

Scientific Challenge:

Develop control laws that model sensory

interactions and coordination in biological

flight.

Zhang, Justh, & Krishnaprasad, GaTech & U. MD. 2012


STTR AF12-BT03

Direction and Value (RW)

• Biologically-inspired topic provides science base to extend

current seeker state of the art from anthropomorphic narrow

field of view single color intensity imager to arthropomorphic

wide field of view multispectral intensity and polarization

imager

– Enhances situational awareness (120 deg fov vs 1 deg fov)

– Enhances target discrimination capability (multispectral

and polarization discrimination)

– Increases seeker functionality (from terminal guidance

sensor to sensor enabling guidance, navigation and

control and fuzing, applicable to ISR and BDA and

communication channel receiver)

• Supports vision-based sensor development in Nature-Inspired

Sciences Center of Excellence, and vision-based sensor

applications in BioUAS PA


Recent Highlights

AFOSR

Basic Research

Initiative:

Biological Sensing

of Magnetic Fields

Optimal Protocols for

Photic Re-Entrainment

Challenge: Discover how geo-

magnetism can induce

neural signals for spatial

navigation.

Math solution minimizes

time to adjust circadian

phase (‘jet lag’)

D. Forger, U. Mich.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Nature-Inspired

sciences for sensing

& control of

autonomous flight

AFRL/RW

Center of Excellence:

A Program Partnership


Nature-Inspired CoE:

Direction and Value

• Technical focus in RW has been on the science

underlying optical sensors and associated

algorithms, for application to agile autonomous

airframes

• CoE provides opportunity to expand technical

focus to include additional sensory modes

(mechanosensors, chemosensors,

magnetosensors), and address efficient

approaches to combining information from these

sensors

• Opportunity for exchanging personnel and

students in each other’s laboratories


BIO-SENSING OF MAGNETIC FIELDS

A NEW BASIC RESEARCH INITIATIVE

AFOSR SENSORY INFORMATIN SYSTEMS PROGRAM

SCIENTIFIC CHALLENGE:

Discover the receptor mechanism(s)

for biological magnetic sensitivity,

especially at field strengths

comparable to the geomagnetic

background.

PM ADVISORY GROUP:

Patrick Bradshaw, Tatjana Curcic, Hugh DeLong, John Gonglewski,

Willard Larkin (retired, v.e.c.) *Primary co-PM



KNOWN:

What is known? … What is conjectured?

TWO CONJECTURES:

Magnetoreception is found in all

major vertebrate groups, plus

some molluscs, crustaceans, and

insects

Used for local position

finding (e.g. bats homing at

night)

Used for long-range migration

Magnetite Fe3O4 single-domain crystals associated with cell

membranes twist to align with an imposed field – this may

open/close ion channels.

Blue-light photoreceptive proteins – cryptochromes – are

somehow involved, possibly via quantum spin coherence among

radical pairs.


BIO-SENSING OF MAGNETIC FIELDS Why would this discovery matter?

It would explain the bio-sensory basis for long-

range navigation by orientation to the geomagnetic

field. It could provide a missing scientific link to understand

neurocognitive effects of magnetic stimulation – these

phenomena currently are explored in AFRL 711HPW/RH.

It could reveal the first known quantum-level

biophysical mechanism in sensory biology -- we

conjecture that some natural biological systems acquire

extreme sensitivity via correlated spin dynamics of

certain electron transfer reactions.

It would deepen fundamental knowledge of

cryptochrome proteins in sensory function – these

proteins are implicated in a wide range of non-imaging

photosensitivity, e.g., adaptive camouflage. They may

be key to light-dependent magnetoreception.

1.

2.

3.

4.


BIO-SENSING OF MAGNETIC FIELDS Why enter this research area now?

• Weak experimental methods have hampered past efforts to find a

biophysical transduction mechanism for magnetic sensitivity. Better

techniques are now available, in behavioral, cellular, molecular, and

physics approaches.

A few key papers: Kirschvink, et al., (J. Royal Society, 2010) Biophysics of magnetic

orientation: Strengthening the interface between theory and

experimental design.

Eder, et al. (PNAS, 2012) Magnetic characterization of isolated

candidate magnetoreceptor cells.

Solov’yov & Schulten (J. Phys. Chem. 2012) Reaction kinetics and

mechanism of magnetic field effects in cryptochrome.

Dorner, et al. (Quant. Phys. 2012) Toward quantum simulation of

biological information flow.

• A scientific community with growing interest and expertise has been

forming, especially with respect to “Quantum Effects in Biology,” (Dr.

Sterbing attended QEB meeting in June.) High quality proposals are

expected.


BIO-SENSING OF MAGNETIC FIELDS Where is the top research in this area?

Munich: Michael Winklhofer, at Ludwig-Maximillians Univ.

Duke U.: Sonke Johnson (a PI in Hugh DeLong’s program)

Oxford: P.J.Hore (DARPA-funded expert on quantum chemistry)

Baylor: David Dickman (expert on avian magnetic navigation)

U. Mass: Steven Reppert (a PI in Larkin’s program)

U. Oldenberg, GE: Henrik Mouritsen (Neurosensory Sciences)

Chapel Hill: Kenneth Lohmann (UNC biologist)


Conclusion

• Sensory information systems is a dynamic, cross cutting

discipline that is discovering what nature has perfected

• This program is feeding information to many of the TD’s

(RW, RY, RX) as well as the RTX’s (RTA, RTE)

• The BRI and COE are exciting opportunities that will

provide creative, new information for the science of flight


Backup Slides


Dissociated cell from trout olfactory epithelium

rotates with an imposed 33Hz magnetic field

Red arrows identify a micron-sized magnetic

cluster inside the cell. Such rotatable cells

were rare in this preparation, and had an

elongated shape (aspect ratio 1.6).

BIO-SENSING OF MAGNETIC FIELDS What is known? … What is conjectured?

RECENT FINDING

CONJECTURE

Sources: Eder, et al., (PNAS 2012) and Lohmann (Nature 2010)



What is known? … What is conjectured?

A RECENT FINDING: “Magnetically sensitive light-induced

reactions in cryptochrome are consistent

with its proposed role as a

magnetoreceptor.”

Kinetics and quantum yields of photo-

induced radical pairs (flavin-tryptophan)

were studied for this plant cryptochrome

(in vitro). Rates of spin-coherent

processes were estimated. Magnetic field

effects were seen down to 1 mT.

Maeda, et al. (PNAS 2012)

Arabidopsis thaliana

Cryptochrome AtCry-1

CONJECTURE:

Magnetic sensitivity may be a

general feature of this protein family,

and could extrapolate to

vertebrate cryptochromes.

http://en.wikipedia.org/wiki/File:CRY1Pretty.png



Can AFOSR take the lead in this area?

Current research funding is widely scattered: No

other focused Federal program exists.

DARPA’s program on quantum effects in biology (QEB)

has a much broader scope, but it has relevance to our

BRI. We have encouragement and continuing interest

from DARPA. (Matt Goodman and Guido Zuccarello)

Dr. Susanne Sterbing is deeply capable in the

physics, biophysics, and sensory neuroscience

needed for AFOSR’s PM leadership on this topic.


Visuomotor Convergence


Visual Sensory Equipment in

Flying Insects

1. Study the functional and physiological organization of two visual mechanisms (compound eye, ocelli)

in Locusts, Tabinids, and Asilids (Krapp)

2. Characterize observability properties of tangential cells and ocelli across species (Humbert)

3. Extract species-specific adaptations of visual control structures and determine general principles for

multisensory integration (Krapp, Humbert)

4. Implement bio-inspired visual feedback for stabilization and compare to traditional approaches

(Humbert)

How is visual sensor specification linked to flight performance?

Tabanidae (horseflies);

varying number of ocelli, 18-

25 VS cells, 6 HS cells

Asilidae (robber flies); no

ocelli, no VS cells, 5-18

HS cells

Calliphoridae (blowflies);

3 ocelli, 10/11 VS cells, 3

HS cells

Orthoptera (locusts); 3 ocelli,

likely to have no VS cells,

several HS cells, no halteres


Insect Sensorimotor Control

Power

Muscles

Steering

Muscles

Apply control- and information-theoretic tools to models of sensory systems and flight

dynamics to understand potential benefits to unmanned aerial systems (UAS)

What is novel/unique about insect sensorimotor systems?

• Measurements made in highly non-orthogonal axes

• Sensors configured to measure composite quantities

• Traditional separation between estimation/control and

inner/outer loops is absent

Fundamental Question: What underlying principles drive

biology’s design of actuation and sensing architectures?


Multidisciplinary Methods /

Approach

Novel combination of experimental and analytical methods from biology,

aerodynamics, flight dynamics and control/information theory

• Electrophysiology to study

functional and physiological

organization and generate

models of sensory systems

• High speed videography and

automated kinematics extraction

to determine control strategies

• Computational fluid dynamic

simulations (based on extracted

kinematics) to estimate flight

dynamics models

• Control- and information-theoretic

analysis of closed loop behavior

(observability/controllability)

+ _

x(1 )x(t)


Novel Contributions • Formalization of operational principles for insect sensorimotor control systems

• Novel approach combining biological experimental techniques, high fidelity

simulations with solid grounding in control- and information theoretic analyses

• Are natural systems optimized for information extraction, maneuverability, robustness

or some combination of the above?

• Are sensory systems matched to the flight dynamics (Mode-Sensing Hypothesis)?

• What are the potential improvements in Air Force capabilities?

• Increased levels of agility, gust tolerance and autonomy for small scale UAS

• Comparisons of novel hardware implementations with traditional engineered solutions

(computational requirements, bandwidth, SWaP, closed loop performance)

Biological Principles Novel Hardware Implementations

(analog VLSI) Increased Capabilities for Small UAS

(Agility, Robustness, Autonomy)


Visual flight control in dim light:

How moths see and maneuver in low light.

Objectives:

(1) Determine what part of the visual environment is

most important for flight stabilization and steering.

(2) Determine the spatial and temporal resolution of

motion-detecting neurons in the visual system.

(3) Determine the spatial and temporal resolution of

visual flight control mechanisms by challenging

freely behaving M. sexta moths to fly through

environments using identical stimuli and conditions.

Technical Approach: (1) Record responses of freely flying moths to

visible patterns in areas of their visual

surround (i.e., above, below, to the side). Then

determine the combination of pattern density

and decreasing light level that causes flight

stabilization and maneuvering to degrade.

(2) Determine the spatial and temporal resolution

of the wide-field motion detecting neurons in

the visual system that supports adaptive flight

control in these moths.

(3) Coordinate these two approaches to show how

moths generate adaptive flight control at night.

DoD Benefit: (1) Basic knowledge of important visual

principles that enable flight in dim light.

(2) Useful insights for the development of control systems allowing autonomous micro air vehicles to fly missions in dim light.

WIND Parallel studies of the behavioral responses of freely flying moths and the responses of neurons in the visual systems of the same species, to the same visual stimuli, will reveal the performance envelop of adaptive flight maneuvering in these specialized night flying animals.


Flight Control Experiments

• Behavioral responses of freely flying moths

tracking odor plumes upwind will be recorded

as they encounter different visual patterns at

different light levels from daylight to starlight.

• Male moths track female pheromone plumes

upwind – supported by motion-sensitive

visual flight stabilization and steering control.

• The lowest pattern contrast (combination of

finest stripe density and moth flight speed)

that continues to allow stable flight control at

each light level reveals the maximum spatial

and temporal resolution of the flight control

system.

• Behavioral response will be compared to

physiological responses of neurons in the

moths visual system to the same stimulus

and light conditions.

Normal

control

Flight

disrupted

over low

contrast

pattern

WIND


Visual Motion Experiments

• Recordings of the physiological

responses of neurons in the visual

system sensitive to wide-field motion

will be made at different light levels

from daylight to starlight (using

neutral density filters).

• Neurons respond preferentially to

sinusoidal stripe gratings moving in

one direction – they are directionally

sensitive to motion.

• The fastest pattern motion and finest

stripe density that can distinguish at

each light level reveals the maximum

spatial and temporal resolution of the

visual system.

• Responses of these neurons will be

compared to responses of freely

flying moths in the same stimulus

and light conditions.


Haematopota pluvialis,

A horse fly

Stomatopod compound eye; six

midband rows contain spectral

and polarization sensing diversity


Insect Bird Bat

( ) with potential to be controlled during flight

Natural Fliers’ Wing Joints

Plagiopatagiales Muscles

Bat wing has Intrinsic, non-joint muscles

Research Questions: • Discover joint coordination timing • Determine functional redundancy • Do joint muscles control force or position? • Discover what the intrinsic muscles do.

Wing Sensorimotor Activation - - A New Program Initiative


Intrinsic Muscles Active

only

During Wing Upstroke:

Discovering Bat Wing Musculature Dynamics during Flight

Large bat (1.2kg) in low speed flight

has precise control via wing skeletal

muscles and wing intramembranous

(intrinsic) muscles

• Electromyography during flight

• Thermal videography for metabolic load)

• Selective, reversible muscle paralysis

• 3D X-ray mapping of skeletal motion

ResearchTechniques:


SUMMARY: Transformational Impacts & Opportunities

Advanced auditory modeling:

Hearing protection:

Optical processing:

Autonomous flight control:

• Adaptive airfoils based upon bio-sensory mechanisms

• Steering based upon neural autonomous systems

• Discover sensorimotor basis of formation flight

• Polarization vision and signaling adapted from biology

• Achromatic 1/4 wave optical retarders

• Emulating compound eye in new optical devices

• Mathematics for coherent modulation analysis

• Neural-Inspired analyses to parse acoustic scenes

• Massive improvements in high-noise attenuation.



Robber Fly

No Ocelli

Halteres

No VS cells

5-18 HS cells New Lab for Insect Vision

Spectral & Polarization

Electroretinography

AFRL/RWG PI: Martin Wehling

http://images.google.com/imgres?imgurl=http://www.bath.ac.uk/ceos/images/blowfly/bfeye.jpg&imgrefurl=http://www.bath.ac.uk/ceos/Insects1.html&usg=__LyvmheDSpgzJ2ptfZfqUsRqNyiA=&h=526&w=786&sz=235&hl=en&start=7&um=1&itbs=1&tbnid=smh0KQHngQdHoM:&tbnh=96&tbnw=143&prev=/images?q=Insect+compound+eye&um=1&hl=en&sa=X&tbs=isch:1


Large bat (1.2kg) in low speed flight

maintains precise control via wing

skeletal muscles and wing

intramembranous muscles

Dynamics of Bat Wing Musculature S. Swartz, T. Roberts, Brown Univ., 2011

Electromyography during flight

Thermal videography (to measure metabolic load)

Selective, reversible muscle paralysis

3D X-ray mapping of skeletal motion

Techniques:

Bradshaw - Sensory Information Systems - Spring Review 2013

Technology

public release distribution

human auditory analysis

auditory brain

auditory coding

auditory cognition

acoustic analysis aims

advanced auditory modeling

auditory modeling transition