ISA Transactions 46 (2007) 157–165www.elsevier.com/locate/isatrans

Neurophysiological study on sensorimotor control mechanism in superiorcolliculus of echolocating bat

Yao Lia,1, Y.D. Songb,∗

a Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, United Statesb Department of Electrical Engineering, North Carolina A&T State University, Greensboro, NC 27411, United States

Received 21 August 2006; accepted 24 December 2006

Abstract

This paper investigates the neural processes associated with bat sonar vocal production and their relationship with spatial orientation. The bat’sheavy reliance on sound processing is reflected in specializations of auditory and motor neural structures. These specializations were utilized byinvestigating the mammalian superior colliculus (SC); a midbrain sensory motor nucleus mediating orientating behaviours in mammals, includingvocal motor orientating. Behavioural and neurophysiological experiments were conducted in the insectivorous echolocating bat, Eptesicus fuscus.Chronic neural recording techniques were specifically developed to study neuronal activity. Potential application of the results on control systemsis also addressed.c© 2007, ISA. Published by Elsevier Ltd. All rights reserved.

Keywords: Echolocating bat; Eptesicus fuscus; Sensorimotor control; Superior colliculus; Neural coding; Neurophysiology

1. Introduction

The big brown bat (Eptesicus fuscus) lives in colonies withmillions of bats in some warm caves of the southwest UnitedStates, emerging twice each night to hunt flying insects [1].Hundreds of bats per second make up the traffic stream passingthrough the cave entrance; yet there is no accumulation of deadand wounded bats on the ground below. Possible explanationsfor this include rapid reaction times and high manoeuvrabilityto avoid conflicts at the last seconds [2,3].

Microchiroptera have evolved a biological sonar systemthat enables aerial foraging and navigation in total darkness.These echolocating bats emit sequences of high-frequencyvocalizations and use the returning echoes to create acousticimages of the environment [4]. They orientate their gaze inspace by adjusting their sonar vocalizations, flight dynamics,head aim and potentially pinna movements in a coordinatedmanner as they approach a target [5]. Apparently, bats exhibitsome remarkable capabilities to deal with complexity and its

∗ Corresponding author. Tel.: +1 336 334 7760x218; fax: +1 336 334 7716.E-mail addresses: yaoli@umd.edu (Y. Li), songyd@ncat.edu (Y.D. Song).

1 Tel.: +1 301 405 6596; fax: +1 301 314 9920.

0019-0578/$ - see front matter c© 2007, ISA. Published by Elsevier Ltd. All rightsdoi:10.1016/j.isatra.2006.12.002

associated uncertainty [6–11]. The plethora of highly reliableand adaptive features of such a creature, in contrast to ournon-robust engineering paradigms, suggest that fundamentallydifferent principles and strategies are used in bats to achievethe adaptation and reliability [12,13]. Borrowing the principlesthat sustain the mechanisms and applying them to the design ofengineering systems could result in a radically new approachfor the design of adaptive and fail-safe systems. However, todate, little research effort has been directed at understanding thefundamental principle and integration of auditory informationwith motor programmes for spatially-guided behaviour inmammals [14–19].

In this paper we describe our study on the superiorcolliculus of the echolocating bat related to auditoryinformation processing and adaptive motor control forspatial orientation. By combining the behavioural andneurophysiological experiments, we exploited the acousticimaging system of the echolocating bat, an animal that relieson the spatial analysis of dynamic auditory scenes to guideits behaviour. The detailed experiment design is described inSection 2, with which we conducted oscillating target detectionexperiments and collected large amount of neural response datarelated to the sensori-motor control mechanism in SC of E.fuscus. The results of the offline analysis on the recorded data

reserved.

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Fig. 1. Neural-electrode interface board craniotomy location (left), bat fitted with EIB (middle), schematic of EIB (right).

are presented in Section 3. A mathematical model is proposedin Section 4, where analysis of the results is also provided. Thepotential application to micro UAVs is discussed in Section 5and the paper is closed in Section 6.

2. Material and methods

2.1. Animal and surgery

Adult insectivorous bats (E. fusucs) ranging from 13–18 gwere collected from the wild and housed in a bat vivarium atthe University of Maryland. Bats were housed under constant12:12 h, light:dark, conditions and given food and water adlibitum. The Institutional Animal Care and Use Committeeat the University of Maryland approved all the proceduresdescribed here.

A custom, lightweight (<0.5 g), 16-channel electrodeinterface board (EIB) (Neuralynx, Tuscon, AZ) was positionedover the craniotomy site. The implant was constructed oftwo to nine, 30-gauge stainless steel cannula, soldered in a3 × 3 matrix configuration to the EIB, with the cannula tipsangled (20◦) toward the central cannula. Adjacent electrodeswere spaced 350 µm apart at the level of the EIB board. TheEIB was a printed circuit board with no electronics, and witha 20-pin Omnetics connector (Omnetics, Corp.) for matingduring experiments to an active head-stage board. All cannulawere insulated externally, served as extra-cranial guide tubes,and functioned as a means of electrical contact between theelectrodes and the EIB as in Fig. 1.

2.2. Schematic of the oscillating target setup

A goal-directed echolocation behaviour experiment isdesigned. Bats are trained to rest on a platform and useecholocation to track and capture a swinging target. Targetis placed on an arm connected to a pendulum, whichswings toward and away from the bat, appealing to the bats’eacholocating reaction, as shown in Fig. 2.

3. Analysis of neural response and acoustic singals

3.1. Basic assumptions

All the experiment design and data analysis are based on thefollowing assumptions:

Fig. 2. Experimental design for oscillating target experiment.

(1) The target distance is coded from the time interval (delay)between the outgoing pulse and incoming echo;

(2) Targets are moving but without Doppler shift in the target-reflected echoes;

(3) The target-reflected echo is an exact replica of the emittedpulse with simply a shift in time to introduce echo delay.

Echolocation signals were recorded using two ultrasonictransducers (Ultrasound Advice) placed within the calibratedspace. Microphone signals were amplified; band pass filtered(10–99 kHz, 40 dB gain, Stewart, VBF-7) and recordeddigitally on two channels at a sample rate of 240 kHz/channel.Multi-unit neural recordings from the SC of a freely behavingbat were first filtered between 300–8000 Hz to remove anybaseline fluctuations and low-frequency field potentials. Wedeveloped customized software by using Matlab (Mathworks,Inc.) for all offline analysis.

We define the response event as deflections of thecontinuously sampled voltage records exceeding an eventcriterion threshold. Events with magnitude of more than twicethe standard deviation from the baseline mean were extracted as‘spikes’. If the power magnitude remained above threshold formore than 3 ms the events were rejected. Event waveforms thatwere not biphasic were rejected. The recent advent of multipleelectrode recording technology makes it possible to study thesimultaneous spiking activity of many neurons. This allows usto explore how stimuli are encoded by neuronal activity andhow groups of neurons act in concert to define the function ofa given brain region. We inspected several properties of themultiunit spikes trains and generate a variety of analysis onthe timing-lock property to different aspects of the sonar callsand echoes. Fig. 3 depicted the original and band-pass filteredmultiunit spike trains recorded from the SC site at one recordingsession of 360 s. From here onward we will write “spikes”

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Fig. 3. (Above) The raw multiunit neural recordings from the SC of a freely behaving bat. (Below) The filtered data between 300 and 8000 Hz.

Fig. 4. Spike firing time (dot) is displayed relative to FM sweep sonar vocalization time (bin), and relative to the final in a series of sweeps with increasing rate.This plot represents the data recorded from a single trial of 12 s.

and “spike trains”, as a short name for “multiunit spikes” and“multiunit spikes train”.

Two important questions challenged us: whether thesestimulus features evoke stereotypical responses indicating theirbehavioural significance, and what mechanisms the sensory-motor integration follows. To this end, three different analyseson neural response and the acoustic sonar call signals wereconducted to explore whether any synchronization or enhancedtiming precision is present during calls relative to other epochs.Fig. 4 shows the consecutive recording session of both the sonarcall timing (solid line) and the spike timing (dot).

To better describe the analysis in a systematic andmathematic way, we defined the following notations throughout the analysis. In this experiment, the recording length of asingle trial is chosen as twelve seconds. The neural responsefrom superior colliculus, the vocal sonar call and the targetdistance are recorded simultaneously. A complete recordingsession consists of thirty single trials; the trial number isindicated by letter k.

T (k)S = [t s

1 , t s2 , . . . , t s

m]T Timestamp of neural spike firing

from kth trial

T (k)C = [tc

1 , tc2 , . . . , tc

n ]T Timestamp of sonar calls from kth

trialD(k)

C = [d(tc1 ), d(tc

1 ), . . . , d(tcn )]T Target distance at time

T (k)C

T (k)d = [td

1 , td2 , . . . , td

n ]T Timestamp of returning echo with

T (k)C .

We use the four vectors to represent the timestamp of thediscrete time events involved in the sonar localization from onerecording session.

3.2. Pre-motor vocalization control mechanism

How the bats initialize the sonar vocalization directlydetermines how they perceive the return echoes. To find outthe control module for the pre-motor vocalization, a spikedistribution raster (Fig. 5) is generated as follows: label allthe sonar call onset time as zero in the axe, count the spikenumber within the range of 10 ms before and after the vocalonset, generate the histogram for all the sonar calls producedin a single recording session. In this figure we present the

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Fig. 5. Analysis of timing relative to sonar vocalization for selected 9 trials.

results from nine out 30 trials. The generated histograms showthat there exist two clusters of intensive neural response (trial#1–#6), centred at −3 ms and −2 ms, which are all before thevocal onset time.

An interesting result is a huge suppression of neuronresponse right after the first cluster but before the secondcluster. We expected the first cluster of neural response wouldbe related to the control of vocalization, including the mouthand nose actions. Right after generating the sonar call, batsimmediately suppress sonar producing action and then startto pay close attention to the reception of the returning echo.Accordingly, bats will adjust their ear shape and direction, andalso elicit signals to the auditory cortex; this control functionmodel is believed to relate to the second cluster.

We applied Gerstner’s spike response model to formalize thepre-motor vocalization control mechanism. The state of one SCcell i can be described by its membrane voltage, denoted bya single variable ui , a function of time t . In the absence ofspies, the variable ui is at its resting value zero. The functionFi describes the time course of the response to an incomingspike. After the summation of the effects of several incomingspikes, ui reaches the threshold θ , an output spike is triggered.

Fi = {t ( f )i |1 ≤ f ≤ n} = {t |ui (t) = θ}. (1)

Assume the spike train for the excitatory input to the SC cell is

E = {t ( f )|1 ≤ f ≤ Ne} (2)

and inhibitory spike train

I = {t ( f )|1 ≤ f ≤ Ni }. (3)

The explicit dependence of the membrane potential upon thelast output spike allows us to model neuron response as acombination of three components: a reduced responsiveness

after an output spikes; an increase in threshold after firing; and ahyerpolarizing spike after action potential. Then, the state ui (t)of superior colliculus neuron i at time t is defined by:

ui (t) =

∑t ( f )i ∈Fi

ηi (t − t ( f )i ) +

∑t ( f )∈E

exc(t − t ( f )i )

−

∑t ( f )∈I

inh(t − t ( f )i )

. (4)

The kernel ηi (t − t ( f )i ) for t > t ( f )

i describes the form ofthe action potential. The kernels exc(t − t ( f )

i ) and inh(t −

t ( f )i ) as functions of t − t ( f )

i can be interpreted as the timecourse of a post-synaptic potential evoked by the firing ofthe presynaptic neuron at time t ( f )

i , depending on the signfrom ti−1 to ti . The function exc(t − t ( f )

i ) represents theexcitatory postsynaptic potential (EPSP) and inh(t − t ( f )

i )

represents inhibitory postsynaptic potential (IPSP). These twofunctions describe the positive pulses and the negative spikeafter-potential that follows the pulse.

This model simplifies the pre-motor vocalization controlmodule at superior colliculus as a linear superposition ofexcitation and inhibition and a nonlinear shunting effect frominhibition when the membrane voltage is zero. Such a spikingneural model emphasizes the importance of timing betweenexcitatory and inhibitory spike train, and the output of the SCcell carries this timing information to the next stage.

3.3. Pulse repetition rate control mechanism

Bats localize objects by emitting ultrasonic pulses andprocessing the resulting echoes form objects. Their small head

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Fig. 6. A partitioned trial with different PRR (Pulse Repetition Rate) region.

Fig. 7. Selective neural response corresponding to different PRR.

size and the use of high frequency sound make the vocal pulserepetition rate (PRR) their primary cue for range echolocation.Based on the difference of pulse repetition rate, a complete insetpursuit with successful capture ending could be partitioned intothree stages: searching phase, approaching phase and terminalcapturing phase.

In Fig. 6 three different rectangles are used to label thepartitioned phases. The searching phase with PRR ranges from20 to 60 Hz, thin dash line rectangle highlights these vocal onsettimes as an early alert. Similarly, the approaching phase whichproduces the calls with PRR between 60 and 80 Hz, thick dashline rectangle is used for the confirmation of the target withinthe reachable range. The final stage, which we use solid dashline rectangle as a symbol of execution, bats produce the callswith PRR in the range between 80 and 100 Hz.

Applying the same raster-histogram data analysis for pre-motor vocalization in Section 3.2, we generated the spike rasterfor different pulse repetition rates as shown in Fig. 7 (leftcolumn). The corresponding histograms of average spike per

call are generated accordingly (right column). The raster andhistogram are plotted in pairs at each row corresponding to thePRR range of 20–60 Hz, 60–80 Hz and 80–100 Hz respectively.The numbers of call in different PRR ranges decrease as therepetition rates increase. From 953 calls in searching stage to252 calls in approaching stage, and only 133 calls producedduring the capture stage. Surprisingly, the neural response in thesmall solid line rectangle completely suppressed while the PRRincreased to high capture range. More explicitly, at the earlysearching stage with a relatively slow PRR (Maximum 20 Hz),there exists two well-separated clusters of neural response. Atthe approaching stage, the earlier cluster of neural responsecentred at −3 ms, which is 3 ms before the sonar call onset,starts to fade, but still visible from the raster. While the batentered into the capture stage, PRR increased to the range of60–80 Hz, this early response is completely disappeared. Thisseries of changes can be seen more salient from the spikeaverage histogram. This is a very interesting extension for ourprevious findings in the pre-vocalization control mechanisms.

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Fig. 8. An example of simulated approach sequence of sonar call pulse and paired echo after certain delay.

Fig. 9. Delay-tuning curve of a neuron in the superior colliculus of the E. Fuscus as target distance varies: Neuron exhibits broad delay tuning at long range, and asharper delay tuning at 1.5 ms (target distance is about 0.5 m) after the vocal onset time.

3.4. Echo delay tuning control mechanism

The delay-tuning properties of a neuron can be detectedfrom its delay-tuning curves. A neuron’s delay-tuning curveis a response property defined by the probability of the neuralfacilitative response for different values of echo delay.

Based on these delay-tuning properties, a delay-sensitiveneuron can be classified as delay-tuned or tracking. Since thetarget is moving along the range axis, a compound echo iscreated by several overlapping pulses delayed in time, of whichthe density of overlapping components determines how closethe targets are. Figs. 8 and 9 simulated and recorded thisprocess. The firing pattern of the population of delay-tuning(delay-sensitive) neurons severs as an important cue for targetrecognition and classification. Those features include responsemagnitude of the neurons, sound frequency at which the neuronis tuned, the time at which the echo arrived, and the best

delay of the neuron that responded. Different types of targetinformation can be derived from the different combinations ofthese features.

Integrating the pendulum distance recorded while each sonarcall produced, the delay tuning curve is generated accordingly.Following the same procedure in analysis above, the spikeswere cumulatively depicted within the range 25 ms after eachcall. We also generated the distribution of the echo delaytimes, and computed the spike/echo PSTH plot (Peri-stimulusTime Histogram). Since neurons typically have some baseline“spontaneous” firing rate; with no stimulus applied, the PSTHwould show a low-level random pattern, because there wouldbe no correlation between the neural discharge spikes and anystimulus signal. If a stimulus is applied, such as sonar call burstor returned echo that repeats once per sweep, the neuron willbe much more likely to discharge at the start of the stimulus.Using this approach, we checked whether the bat had a selective

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Fig. 10. Simplified echolocation system.

tuning response corresponding to different delay time, it can beseen that the generated spike/echo PSTH is almost flat as oneneuron spike to one returned sonar echo. The bats consistentlyrespond to the echoes with almost the same firing rate.

At an early stage, SC only involved in the head angleadjustment and body movement, since the echolocation processat the searching phase requires more complicated computationand analysis. SC is not the kernel sensory processing module.After entering the capture phase, a bat emits sonar calls muchfaster but with the enormous prior spatial and target informationit has already acquired, the SC enhances its perception analysisfunction for the returning echo. This extra sensory computationmodule builds up a more efficient and fast feedback controlloop, the motor control and the sensory perception is perfectlyintegrated at this stage.

This mechanism allows the bat to accurately and quicklydetect a target or tell a friend while flying freely and receiving

enormous sonar echoes from the complicated environment. Atthis stage, flying adjustment is mostly controlled by SC. Whenthe bat confirms the target and is ready to finalize the capture,SC takes the place of the advanced computation module anddirectly processes the echoes in an express and precise way. Tobetter describe this variable feedback control loop we depicteda simplified echolocation system in Fig. 10. The solid lineshows the pathway of the auditory perception signal; dash lineshows the pathway of the motor control signal including thevocalization and orientation.

4. Kinematics’ model for sensorimotor control

We define V the vocal sensor perception inputs, U is themotor control outputs. B is the configuration and behaviour ofthe bats, which is controlled by U , and the last is E for theenvironment and surrounding, which contain disturbances and

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uncertainties. Assume that the body position is controlled bythe multi-dimensional motor outputs through an updating lawµ. The vocal sensory delivers a multidimensional input thatis a function ηof the body configuration B = µ(U ) and theenvironments E . Then we have V = η(B, E) defined as:

V def= f (U, E) = η(µ(U ), E). (5)

The vocal sensorimotor control is related to the factorsof sonar call frequency and pulse repetition rate, as we cantell from the corresponding neural activity in the previoussection. It will be highly interesting to find out the controlpattern of the real neural circuits and develop a correspondingalgorithm, which, through time, could be able to research a setof sensorimotor laws linking its inputs to its outputs:

f (·, ε)def={U 7→ f (U, E), E ∈ ε}. (6)

These are a set of functions linking the variable V to U ,parameterized by the environmental state E . Note ε is the setof all E . Our goal is to extract from this set that depends onthe way the sensory information is provided, namely, the vocalsensorimotor orientation mechanism.

Unlike any other control systems, living creatures do notneed unrealistic (usually optimum) levels of accuracy to per-form accurate control [20]. The high flexibility fault toleranceis the most attractive virtue of the biological control system.In this study, we are first trying to develop a stable method,which furthermore uses time derivatives as a more plausibleway to estimate the differential. Indeed, the non-linear func-tional relationship between the motor output and the auditorycortex inputs implies an exact linear relationship between theirrespective time derivatives at a given motor output,

v(t) = f (U (t), E0) (7)

v̇(0) =∂ f∂u

[U (t), E0]U̇ (t)|t=0 (8)

and this linear relationship can be estimated as the linear map-ping associating the U̇0, for any curve in the motor commandspace such that U (0) = U0, to the resulting V̇0.

The idea is then to estimate the time derivative ofthe “correct” vocal sensory input combinations along the“correct” movements so that this linear relation is diagonaland the decomposition unnecessary: using singular valuedecomposition at each step to provide an indication of whatvectors seem to be of interest. At the end of the process, whenthe linear relationship is judged to be sufficiently diagonal, thesingular values are taken as the diagonal elements, and are thusestimated with the precision of the time derivative estimator.

Using this method of the first stage of the experimentwhen the environment is unchangeable makes it possible forthe algorithm, at the same time as it finds a basis for thespace, to calibrate the signals coming from the ear: it extractsvocal sensory input combinations that are meaningful to theear’s movement. Then during a second stage, using thesecombinations, it estimates the space to the auditory cortexinputs resulting from movement of the environment while itkeeps its motor output fixed at U0. Finally, using the orientation

spaces estimated in the first two stages, it computes theirintersection: if ΦU is a matrix containing the basis of the firstorientation space, and ΦE a basis of the second orientationspace, then the null space of [ΦU ,ΦE ] allows us to generatethe intersection of the two spaces:

[ΦU ,ΦE ]λ = 0 ⇒ ΦU λU = −ΦEλE (9)

where λ = (λTU , λT

E )T. Using the pseudo-inverse of thesensorimotor law, the algorithm computes measuring target thathave a sensory image in that intersection; and this computationis simple since the adaptation process made the orientation lawdiagonal.

5. Potential application on micro UAVs

Micro robotic vehicles (Unmanned Ground Vehicles-UGVsand Unmanned Aerial Vehicles-UAVs) are envisaged to besmall-scale autonomous vehicles intended for reconnaissanceover land, in buildings, in confined spaces and on irregularterrains. While some progress has been made in the relatedareas, the current performance of the micro-UAVs/UGVs isyet unsatisfactory. In contrast, nature has evolved thousandsof miniature flying/walking machines (insects, small birds,bats) that perform far more difficult missions. The results weobtained here could provide valuable insight into the potentialapplication to micro-UAVs. A general picture is emergingthat indicates that the overwhelming superiority of bio-sonarlocation and navigation system over existing micro-UAVs stemsfrom three fundamental factors: ability to generate variableprobing signals efficiently than existing technologies; ability toprocess and make motor action efficiently; and ability to operateunder dynamic environment adaptively.

Bats show us the beauty of simple input and variablefeedback control schemes, which could inspire innovative ap-proaches to adaptive and reconfigurable control of micro un-manned vehicles (UAVs). Our current research is focused onidentifying, characterizing and ultimately incorporating adap-tive and bio-inspired principles and operational strategies asso-ciated with bats for developing radically new control algorithmsfor system adaptation, fault-tolerance and reconfiguration. Thestudy will involve a biologically inspired guidance, navigation,sensoring and actuation. It also involves autonomous decisionmechanisms, which, upon the identification of some failuresin a system, can determine a proper control reconfiguration tomaintain a certain level of system performance.

6. Conclusion

This paper investigates the neural mechanism associatedwith bat sonar vocal production and its relationship with spatialorientation. By combining behavioural and neurophysiologicalexperiments, we explored the acoustic imaging system ofthe echolocating bat, an animal that relies on the spatialanalysis of dynamic auditory scenes to guide its behaviour. Theneurophysiological experiments were conducted to understandthe behaviours of the bat during tracking and capturing a target.Our study thoroughly analysed the pre-motor vocalization

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control mechanism, neuron firing pattern at different phasesduring the target capture and the echo delay tuning response.

Based on our initial theoretical and experimental studies weobtained and confirmed that the motor control for the flyingbehaviour and head movement is the most important functionfor SC while the perception computation is complicated andenormous; while the bat approaches the target very closely,the perception processing module starts to integrate with themotor control in SC; the bat smartly uses an adjustable feedbackcontrol loop to make its action fast and without losing anyprecision.

Our experiment’s results and analysis, though preliminary,could help us to better understand the fundamental principleof the integration auditory information with motor programmesfor spatially-guided behaviour in mammals, and how the batdoes the flight aerodynamics encoding and decoding. Theseresults provide us with some insight into the development ofbio-inspired guidance, navigation and control strategies.

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