J. Neurophysiol 95

95:1263-1273, 2006. First published Oct 19, 2005; doi:10.1152/jn.00879.2005 J NeurophysiolAlexander V. Latanov, David P. Wolfer and Hans-Peter Lipp Alexei L. Vyssotski, Andrei N. Serkov, Pavel M. Itskov, Giacomo Dell'Omo,

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Innovative Methodology

Miniature Neurologgers for Flying Pigeons: Multichannel EEG and Actionand Field Potentials in Combination With GPS Recording

Alexei L. Vyssotski,1 Andrei N. Serkov,2 Pavel M. Itskov,3 Giacomo Dell’Omo,1

Alexander V. Latanov,1,2 David P. Wolfer,1 and Hans-Peter Lipp1

1Institute of Anatomy, Division of Neuroanatomy and Behavior, University of Zurich, Zurich, Switzerland;2Chair of Higher Nervous System Activity, Faculty of Biology, Moscow State University and3P. K. Anokhin’s Institute of Normal Physiology, Department of Systemogenesis, Moscow, Russia

Submitted 19 August 2005; accepted in final form 16 October 2005

Vyssotski, Alexei L., Andrei N. Serkov, Pavel M. Itskov, GiacomoDell’Omo, Alexander V. Latanov, David P. Wolfer, and Hans-Peter Lipp. Miniature neurologgers for flying pigeons: multichannelEEG and action and field potentials in combination with GPSrecording. J Neurophysiol 95: 1263–1273, 2006. First publishedOctober 19, 2005; doi:10.1152/jn.00879.2005. To study the neuro-physiology of large-scale spatial cognition, we analyzed the neuronalactivity of navigating homing pigeons. This is not possible usingconventional radio-telemetry suitable for short distances only. There-fore we developed a miniaturized data logger (“neurologger”) that canbe carried by a homing pigeon on its back, in conjunction with amicro-global position system (GPS) logger recording the spatialposition of the bird. In its present state, the neurologger permitsrecording from up to eight single-ended or differential electrodes in awalking or flying pigeon. Inputs from eight independent channels arepreamplified, band-pass filtered, and directed to an eight-channel,10-bit analog-digital converter of the microcontroller storing data ona “Multimedia” or “Secure Digital” card. For electroencephalography(EEG), the logger permits simultaneous recordings of up to eightchannels during maximally 47 h, depending on memory, whilesingle unit activity from two channels can be stored over 9 h. Thelogger permits single unit separation from recorded multiunitsignals. The neurologger with GPS represents a better alternativeto telemetry that will eventually permit to record neuronal activityduring cognitive and innate behavior of many species movingfreely in their habitats but will also permit automated high-throughput screening of EEG in the laboratory.

I N T R O D U C T I O N

The homing pigeon is an uniquely suited species for study-ing the neurophysiology of large-scale spatial cognition (Bing-man et al. 2005). This would necessarily require the recordingof brain activity during navigation. The commonly used radio-telemetry is unsuitable for this purpose, despite of the manyminiaturized solutions developed already 30 years ago (Kim-mich and Vos 1972; Mackay 1968). Meanwhile, several ele-gant solutions were developed for telemetrical multichannelEEG recording (Perkins 1980; Suess and Goiser 1986; Wertz etal. 1976; Yonezawa et al. 1979) as well as for single-unitrecording (see e.g., Grohrock et al. 1997; Hawley et al. 2002;Nieder 2000; Obeid et al. 2004a,b; Pinkwart and Borchers1987; Takeuchi and Shimoyama 2004; Winter 1998 to name afew). Several manufacturers also offer radio-telemetry now.However, recording from navigating homing pigeons would

require powerful senders and heavy batteries radio-transmittinghigh-quality signals over many kilometers. A bird weighing400–500 g cannot carry such packages. The use of standardradio-telemetry on a flying pigeon might be possible only byfollowing it with a helicopter, causing extremely high-costingexperiments.

Another approach is data logging, for example as employedusing magnetic tape (Ebersole 1987) and nowadays with flashmemory. However, even the latter devices, chiefly used forpatient monitoring in hospitals (Horikawa and Harada 1997),are unsuitable for placing them on small animals, particularlyon flying pigeons, because they weigh �300 g (Siesta, www.compumedics.com).

Here we describe a newly developed miniature multichannelEEG and action and field potential data logger (“Neurologger”)that records and stores EEG simultaneously from eight elec-trodes or from eight differential pairs of electrodes. This devicewas used successfully to record brain activity of flying pigeonsand may also be applied in a variety of other investigationswith common laboratory animals. Action and field potentialscan be recorded from two electrodes simultaneously in itspresent state, but the number of channels can be increasedwithout much gain in weight by sandwiching several of thesedevices.

M E T H O D S

Construction of data logger

A complete view of the logger and of its placement on a pigeon isgiven in Fig. 1. Schematics of the analog and digital parts arepresented in Figs. 2 and 3, respectively. Complete schematics, circuitboards Gerber files, a complete bill of material, binary codes ofmicrocontroller program, and PC interface utility may be found athttp://www.vyssotski.ch/neurologger. The construction of the GPSdata logger was described previously in Steiner et al. (2000); noveldata sheets are available at http://www.newbehavior.com.

INPUT RANGES. To have optimal quality of digitization, different inputranges were chosen for recording of signals of different nature. EEG(bandwidth: 1–115 Hz) input range was �750 �V, action potentials(bandwidth: 300–3,000 Hz) input range was �500 �V, and wide-band recording (EEG � neuronal activity, 1–3000 Hz) was �1 mV.

AMPLIFICATION. The required amplification coefficient was split upinto three amplification stages. The first stage amplifiers (Headstage

Address for reprint requests and other correspondence: A. L. Vyssotski,Institute of Anatomy, University of Zurich, Winterthurerstrasse 190, CH-8057Zurich, Switzerland (E-mail: [email protected]).

The costs of publication of this article were defrayed in part by the paymentof page charges. The article must therefore be hereby marked “advertisement”in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

J Neurophysiol 95: 1263–1273, 2006.First published October 19, 2005; doi:10.1152/jn.00879.2005.

12630022-3077/06 $8.00 Copyright © 2006 The American Physiological Societywww.jn.org


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amplifiers, labeled as amp 3 at Fig. 2C) were placed on a microboarddirectly connected to the head of a pigeon to diminish artifacts of thedata transmission through the cables between the head and the datalogger on the back of a pigeon. The amplification coefficient of the

first cascade was 5.02. A unity gain source follower was used for thebuffering of the signal from the reference electrode (Fig. 2A, amp 1).Amplifiers inputs were pulled to the headstage analog ground(AGND) by 120 M resistors to prevent polarization of electrodes byinput currents of the amplifiers and to avoid pinning if inputs aredisconnected. Low-noise low-voltage amplifiers AD8607/AD8609(Analog Devices) were employed in all stages of the amplificationcascade because of their small power consumption (40 �A peramplifier). However, other types also can be used (for example,AD8574, pictured in Fig. 1A). After the first stage with no filtration atall, signals went through the differential amplifier (2nd stage, amp 4at Fig. 2C) with a unit gain to subtract the reference potential fromthem. This was done to keep common mode rejection ratio (CMRR)of the differential cascade as low as 100 in the whole frequency rangeof the device. Afterward, the differential amplifier in the 1- to 3,000-and 300- to 3,000 Hz versions of device signals went through the firstorder passive low-pass filter (F-3dB � 3,000 Hz) formed by resistor

FIG. 1. Neurologger and its use on pigeons. A: 8-channel electroencepha-lographic (EEG)/single-unit data logger with 1 GB of storage capacity. B:neurologger for single unit recording attached to the back of a pigeon. Thepreamplifier is integrated into the connectors of the microdrive assembly on thehead of the pigeon (covered with adhesive tape). C: pigeon carrying acombined EEG/global positioning system (GPS) logger on its back and restingon power line. On such (rare) occasions, electrical noise can be picked up bythe logger otherwise largely insensitive to it.

FIG. 2. Schematic for a analog part of the neu-rologger. A: unity gain amplifier of the referenceelectrode. B: analog ground-stabilizing circuitry. C:full analog front end channel (there are 8 identicalchannels in total). Inputs REFIN and CHIN are takenfrom the corresponding electrodes at the head of theanimal. “headstage AGND” output connects to GNDelectrode at the animal head. CHOUT connects to themicrocontroller ADC with a built in 8:1 multiplexer.Values at the scheme are for the wide-band (1–3,000Hz) version of the device. Distinctions of other ver-sions are described in the text.

FIG. 3. Schematic for a digital part of the neurologger and a connector foranalog signals X2. The main components of the digital part are microcontrollerD1 and flash X3. The scheme also includes voltage regulator DA1, microcon-troller quartz BQ1 and connector X1 for the data downloading and microcon-troller reprogramming.


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R26 and capacitor C7. This passive filter was omitted in the low-frequency (1–300 Hz) device. After this stage, signals went throughthe first-order passive high-pass filter (F-3dB � 1 or 300 Hz). Finallythey were amplified by the third cascade with the appropriate gain tomeet a 3.3 V input range of the ADC. This stage also served as firstorder low-pass filter (F-3dB � 115 or 3,000 Hz). Values of resistorsand capacitors at Fig. 2C are for the wide-band (1–3,000 Hz) versionof the device. High-frequency (300–3,000 Hz) version differs invalues of R27 � 5K36, R28 � 174R and C8 � 100 nF. Low-frequency (1–115 Hz) version differs from the wide-band version invalues R28 � 2K55, R29 � 510K and C9 � 2.7 nF.

POWER SUPPLY. The system was powered through a LP3964 (Na-tional Semiconductor) low drop-out 3.3-V voltage regulator (DA1 atFig. 3) drawing from a 4.2-V 560 mAh polymeric battery serving alsothe GPS logger. The neurologger was sandwiched between GPSreceiver and the flat polymeric battery (Fig. 1C). Analog groundpotential (�1.65 V from the digital ground) was formed by a resistor-based divider R16, R15 and buffered with a unity gain source follower(analog devices OP777ARM, Amp 2 at Fig. 2B). The stability ofAGND was improved by resistors R16, R17. However, because of avery high gain of the amplification, even small noise at the AGNDproduced by filters could degrade the stability of the circuitry. Toavoid such self-excitation, the AGND potential for the headstageamplifier was driven directly from R16, R15 divider.

DATA RECORDING. Signals were digitized by a 10-bit ADC ofPIC18LF452 microcontroller (Microchip, D1 at Fig. 3) running at 24MHz (giving a formal performance of 6 million instructions persecond (MIPS)). The microcontroller was programmed to write dataonto a 1 GB Secure Digital card (SanDisk, X3 at Fig. 3) continuously.For short-lasting experiments, 64 or 128 MB Multimedia cards(SanDisk) were used instead. The physical and electronic specifica-tions of the neurologger are summarized in Table 1. The PIC18LF452microcontroller was programmed in C language (www.ccsinfo.com).The interface program for the PC handling data exchange and deviceconfiguration was written in Delphi 7.0 (www.borland.com). A fastUSB to serial converter DPL-USB232M (www.dipdesign.com) wasused for the data exchange with PC at a speed of 1.5 Mbps. CRC

calculation was used for data integrity verification during datadownloading.

POSSIBLE IMPROVEMENTS OF THE ANALOG PART OF THE NEUROLOG-

GER. The earlier-described analog part works well, but it can beimproved/simplified in several ways. First, 120M resistors pullinginputs to the AGND probably might be omitted. Second, adding onlytwo resistors at the scheme Fig. 2A (10K between pins 1 and 2 of amp1 instead of direct connection and 40K2 between pin 2 and “headstageAGND”) will form a 2-op amp instrumentation amplifier (Horovitzand Hill 1989) at the headstage, eliminating the necessity of differ-ential cascade based on separate amp 4 on the main board. However,this will require buffering of the AGND of the headstage with anoperational amplifier. Third, the low-pass R26, C7 filter may beomitted. Its combination with the R29, C9 filter forms the secondorder filter providing 40 dB per decade attenuation in the infinity.However, as it is equal to a combination of two passive RC filters, itsattenuation near the cutoff frequency does not differ drastically fromthe cutoff of a simple first-order filter (charts and explanations can befound in Horovitz and Hill 1989). For making a sharper cutoff of thefilter without increasing the number of operational amplifiers, onemight realize a high pass Sallen-Key filter at amp 4 and a low-passSallen-Key filter at Amp 5 (similarly as it is done in Obeid et al. 2004a).

USE DURING EXPLORATION. The neurologger permits both simulta-neous writing of data into the memory card and sending it to the PCvia a fast 1.5 Mbps serial link. The last feature allows monitoring ofrecorded signals at the screen of the attached computer in real time.This feature is especially helpful while searching for neuronal activitywhen advancing a microelectrode. The on-line data received can alsobe saved on the PC for further analysis.

SYNCHRONIZATION OF THE NEUROLOGGER WITH GPS AND OTHER

EXTERNAL DEVICES. For synchronization with the GPS, a simplestart synchronization was achieved by means of a signal line comingfrom the neurologger to the GPS (which has additional input possi-bilities for external synchronization with other events). A precisesynchronization of the record with external stimuli provided by othercontrolling devices could be desirable in many cases. Such synchro-nization can be achieved currently only by synchronization of the startof the record with an external clock linked with a stimulating equip-ment. The internal clock of the logger is quartz-stabilized with�50-ppm frequency stability sufficient for most of applications.However, start synchronization might not be convenient or sufficientin some cases. The desired on-line synchronization can be doneconveniently by arranging an infra-red (IR) link between the loggerand external equipment. For example, by placing an IR phototransis-tor with a small supplementary circuitry at the logger and an IRemitter above the experimental arena. Synchronizing flashes of the IRemitter could be detected by the IR phototransistor and stored togetherwith neuronal data in the logger. The possibility of such synchroni-zation was taken into account during device development: the micro-controller of the logger has unused inputs routed at the PCB to thereserved pads and the structure of the stored data has reserved unusedbits suitable for storing synchronization flags.

Animal handling and electrode implantation

Adult homing pigeons served as subjects for these experiments. Allpigeons had been trained to return to their home loft from severalremote release places. They were also habituated to carry a load ontheir back—using PVC dummies of the same weight and shape as thedata logger assembly. The pigeons carried the dummy permanently.Dummy or logger assembly could be attached and removed to andfrom an adhesive Velcro strip attached to the back of the pigeon.

TABLE 1. Technical data

Parameter Value

EEG logger mechanical dimensions 59�36�4 mm3

Total mechanical dimensions (EEG logger,GPS logger, battery)

66�36�18 mm3

EEG logger weight without batteryand headstage amplifier

7 g

Headstage amplifier weight (with cable) 2 gWeight 560mAb battery 13 gGPS logger weight 13 gTotal weight 35 gBattery life time EEG logger, 8ch � 500sps: GPS logger,

continuous mode 1 Hz3 h 30 min

EEG logger, 8ch � 500sps 2 days 5 hSingle unit logger, 2ch � 10ksps 22 h 20 min

1GB SD card data filling time 8 channels, 500sps 1 day 23 h2 channels, 10ksps 9 h 24 min

1GB data downloading time Through serial interface 2 hThrough MMC:SD reader 20 min

Interface 2 RS232 (3.3V level), 1.5Mbps, converted toUSB via DLP-USB232M

2 GPIO: for GPS synchronization and forStatus LED, power supply

Data format Custom


1265NEURONAL ACTIVITY OF FLYING PIGEONS



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Varnish-covered nichrome electrodes (d � 150 �m) were used forintracranial EEG recording, and gold-covered watch screws for epi-dural EEG recording. Tetrodes were used for the action and fieldpotentials recording. They were manufactured from 25 �m nichromewire (A-M Systems) as described in Gray et al. (1995) and weremounted on a custom-made, manually operated, microdrive placed onthe skull over the hippocampal formation. Pigeons were anesthetizedwith a combination of xylazine (1 mg/kg body wt im) and ketamine (5mg/kg body wt im) and placed in a stereotaxic apparatus. The skin onthe dorsal surface of the skull was opened along the midline, and theappropriate number of holes was drilled in the skull to expose thedura.

All electrodes were fixed to the skull with dental cement (Paladur,Heraeus Kulcer GmbH). Before implantation short elastic cables weresoldered to all electrodes. After implantation of electrodes the freeends of these elastic cables were soldered to a flat 10-pin, 1.27 mmpitch male connector. This connector was fixed with dental cement atthe head of the animal.

The configuration of electrode placements varied according toexperiments. Placement was done according to stereotaxic coordinatesas given by Karten and Hodos (1967), the anatomical terminologyfollowing the new nomenclature as proposed by Reiner et al. (2004).For EEG studies, electrodes were placed over the left and righthippocampus and hyperpallium apicale (formerly hyperstriatum ac-cessorium). A pair of connected electrodes over the area corticoidealaterale (CDL) served as a reference electrode.

Just after implantation, impedance of all electrodes was measured.The impedance at 1 kHz was 50–200 k� for nichrome EEG elec-trodes and �5 k� for screw electrodes. Tips of tetrode electrodeswere not gold-plated and their impedance ranged from 1.0 to 1.8 M�.Impedance of the ground electrode was measured with respect to alarge electrode placed in the pigeon’s beak temporarily. Each pigeonwas given minimum of 3 days to recover after implantation. One daybefore the first flight, a short EEG record was done in the laboratoryfrom each pigeon to test the electrode quality. The first flight afterimplantation was done with a dummy to check that pigeon had not lostmotivation or homing ability after the operation. All operated pigeonshomed without any problems. Operating and testing procedures havebeen approved by the local animal experimentation committees of theVeterinary Office of the Canton of Zurich, in compliance with Swiss,Italian, and Russian legislation, respectively.

R E S U L T S

Combining EEG and GPS recording on pigeons

The rationale of using EEG recordings during homing ofpigeons equipped with GPS is to monitor attentional mecha-nisms during the flight. For example, it is reasonable to assumethat pigeons perceive and use a landmark for navigation if theirflight trajectory as assessed by GPS changes at a recognizabletopographical point (Lipp et al. 2004). However, a pigeonmight perceive many more navigationally relevant cues duringthe flight that may cause a change in direction, but it could alsoignore such cues and maintain an unaltered course. Thus ourprimary goal was to record EEG activity expecting to identifythe presence of cues that elicit the attention of the pigeon. Inhumans and mammals, perception of salient stimuli is oftenfollowed by desynchronization of slow wave electrical activity,e.g., Umbricht et al. (2004, 2005).

IDENTIFYING EVENT-RELATED SPECTRAL PERTURBATIONS. Beforetesting the neurologger during flight, we tested whether ourassumption of EEG desynchronization on perception of sen-

sory stimuli would be valid for pigeons. After implantationwith intracranial electrodes in various locations, pigeons werefirst tested in a sound-attenuated chamber and exposed todiverse auditory and visual stimuli. Every minute a 10-s soundor light was given, thus during 1-h session, a pigeon received60 presentations. Different artificial and natural sounds (wingsflops, pecking, sound of the door of the aviary, cooing) andalso visual stimulation (constant light and blinking) werepresented in a sequential order. Changes in the EEG spectrumproduced by the stimulation during epochs of 60 s were thenanalyzed using the MATLAB environment (www.mathworks.com) with the help of EEGLAB package (Delorme and Makeig2004).

A third-order regression polynomial was subtracted fromeach epoch to remove low-frequency channel floating. Thispractically eliminated the first harmonics from the Fourierspectrum, but this did not cause any problem in the furtheranalysis because we were interested in high frequencies only(�3 Hz). A further step was the subtraction of channels fromeach other to get the differential signal from two electrodes ofinterest. In this type of analysis, a pair of electrodes in the lefthippocampus was used (Fig. 4B).

In most cases, sensory stimulation visibly diminished theEEG amplitude as seen in Fig. 4A. EEG reduction occurredsynchronously in all channels located in different brain re-gions, however. This implies that the exact position of elec-trodes was not important for the analysis. The responses fromthree pigeons during 132 presentations with an epoch length of60 s were then averaged and analyzed for event-related spectralperturbations (ERSP) according to Makeig (1993) and De-lorme and Makeig (2004). The color-coded plots of theseERSP clearly signal systematic event-related shifts in thepower spectrum (Fig. 4B). The most significant reduction ofrelative power induced by the stimulation occurred near afrequency of 7 Hz, i.e., in the theta frequency range, which is3–7 Hz in pigeons (Siegel et al. 2000). The absolute EEGpower in this theta frequency range is stronger than in thehigh-frequency ranges (alpha: 7–12 Hz; gamma: 12–30 Hz).Thus the power of the theta frequency range appeared to be asuitable measure of desynchronization caused by sensorystimulation.

EEG/GPS ANALYSIS DURING FLIGHT AND RESTING. Pigeons im-planted with epidural electrodes were released from severallocations between 1.1 and 22 km from the home loft. EEG(5–8 channels) was digitized with a sampling rate of 500 Hz,while the GPS logger stored positional data every second(Steiner et al. 2000) (www.newbehavior.com). Both data log-gers were synchronized. After returning to the home loft, thedevices were removed from the birds. A standard MMC/SDmemory card reader read data from the SD memory card whiledata from the GPS logger were downloaded to the PC througha serial port.

For EEG data analysis, an epoch length of 1 s was chosen,according to the sampling rate of the GPS logger. For calcu-lation of the EEG power during flight, two pairs of electrodeswere used, one over the left hemisphere, over the left areacorticoidea dorsolateralis (left CDL)—left hyperpallium api-cale (left HA), the other pair over the right one (right CDL—right HA). Artifact-containing epochs were rejected by the





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methods included in the EEGLAB package: finding of abnor-mal values (threshold �200 �V), finding improbable data (5SD), finding abnormal distributions (5 SDs), and finding ab-normal spectra (�30 dB in range 0–50 Hz), according toDelorme et al. (2004). This procedure yielded an acceptablepercentage of rejected data both in the flying (33.7%) and inthe sitting (10.9%) pigeon.

Samples of EEG records obtained in a resting and flyingpigeon are presented in Fig. 5. One may note the absence ofany regular artifacts from rhythmic muscle activity in theflight, despite of the fact that the average speed of the pigeonin the flight was near 50 km/h. This allows analyzing such EEGrecords without any special preprocessing like principal com-ponent analysis (PCA) or independent component analysis(ICA). However, this is only the case when using low-imped-ance epidural electrodes. The intracranial wire electrodes are

much more sensitive to such disturbances and special prepro-cessing of data are needed to eliminate them.

As no hemispheric asymmetry was detected, the total powerof the EEG was calculated as average of powers of thedifferential pairs (left CDL—left HA; right CDL—right HA) todiminish variability (Fig. 6). Additionally, charts weresmoothed using a moving average filter with a span of 5 (Fig.6A). This procedure clearly indicates that the EEG power intheta range (and also the total EEG power) was significantlydiminished during the flight phases (Fig. 6A). This finding mayindicate ongoing attentional processing in the brain of thepigeons due to sampling of familiar visual cues underlyingspatial orientation in the vicinity of the home loft (Lipp et al.2004). Figure 6B shows the color-coded power of the thetaEEG range superimposed on the pigeon’s trajectory as re-corded by GPS. Evidently, power is not constant during the

A

B

FIG. 4. Desynchronization of EEG in pigeons causedby stimulation. A vertical red line at time 0 labels the startof the video/audio stimulation lasting for 10 s. A: originalrecords of an epoch of 60 s. B: color-coded average of 132presentations showing even-related spectral perturbations(ERSP), the strongest decrease in power indicated in blueand occurring strongly in the theta range (near 7 Hz). Thevertical panel to the left shows the baseline mean powerspectrum; the horizontal panel below the color-coding theERSP envelope (low and high mean dB values, relative tobaseline, at each time in the epoch).





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flight, but further data are needed to elucidate the physiologicalmeaning of EEG power variations during pigeon flight andhoming. Nonetheless, the combination of flight tracks on to-pographical maps and satellite pictures (Fig. 6C) with changingEEG data appears to be a highly promising approach. Forexample, the trajectory of the flight first follows a road (andnot the home direction). Then the pigeon took a rest, afterwhich it followed the road till an intersection where itcorrected its course toward the loft reaching it across forestsand meadows.

Recording action and field potentials during restingand flight

The final goal of our approach is to identify neuronal activityrelated to navigational behavior. This, however, can only beachieved if the neurologger has the same capacity in identify-ing and analyzing neuronal activity as found in conventionalon-line systems.

EEG AND ACTION POTENTIAL RECORDING WITH A DISTANT REFER-

ENCE ELECTRODE. Before experiments with a freely flyingbird, single units were recorded using the neurologger in theanesthetized pigeon fixed in the stereotaxic apparatus in anacute experiment (Fig. 7A) and also from a chronically im-planted tetrode in the awake pigeon sitting in the chamber (Fig.7B). An epidural watch screw in the vicinity of the tetrodeserved as reference electrode during acute experiments, whilean intracranial implanted nichrome wire of 150 �m diamserved as reference in the chronic experiment. A single-chan-nel 16 ksps record was done to check the quality of thewide-band recording (1–3,000 Hz) by the system. A relativelygood signal-to-noise ratio was observed in the acute experi-ment and several spikes from a selected neuron clearly indicatethe ability of the neurologger to record single-unit activity. Inthe chronic implantation, the signal-to-noise ratio was lessdistinct, mainly because of the smaller spike amplitude, pos-sibly related to the less controllable advancement of the min-iature microdrive on the head of the pigeon. However, single

FIG. 5. EEG record from the flying (A) and resting pi-geon (B) that are practically indistinguishable with respect tosignal quality. Note in A the absence of any flight artifactsdespite of a flight speed of �50 km/h.





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spikes were still visible and could be separated from thewide-band EEG-single-unit record (Fig. 7B). The spectrum ofthe low-frequency activity did not differ much from the EEGspectrum as obtained with epidural electrodes (data not shown).Hence, the neurologger can be used to record simultaneouslyboth EEG/field potentials and action potentials from tetrodes.

FIELD AND ACTION POTENTIAL RECORDING WITH IN-TETRODE REF-

ERENCE. However, we have found that recordings with adistant reference electrode in a wakeful pigeon are highlysensitive to artifacts caused by locomotion, probably becauseof penetration of muscular electrical potentials into the brainfrom different places. In fact, these artifacts mostly exceeded

A

B

C

FIG. 6. Joint logging of EEG and GPS position datain a pigeon released over a short distance of �1.2 kmfrom the loft. A, in blue: theta power plots covering thetime before release, during flight and a rest phase, andafter arrival to the loft where the pigeon was first sittingon the top before moving in. Red: changes in speed asassessed by GPS recording. Note the strong decrease inpower of theta range (3.5–7.0 Hz) during the 2 flightepisodes and during flying from the roof to the loftentry. B: color-coded variations of theta power coded inepochs of 1 s (corresponding to �12–15 m whenflying). Note the power reduction and the variationsduring flight that may indicate attentional processing ofvisual landmarks. C: superposition of GPS tracks on aninfrared satellite image of the region (lakes: dark; roads:bright; forest: dark green; open vegetation: shades fromreddish to yellow-green). Note that the pigeon firstfollows the road and not the home direction. Afterresting, it followed the road till a bifurcation pointing tothe loft from where it took a shortcut across the mead-ows. This strongly implies the use of local landmarks.





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the input range of the device. If only action potentials are ofinterest, a high-pass filter may eliminate low-frequency arti-facts due to locomotion, but recording wide-band activityneeds another approach to dampen locomotion-induced arti-facts during walking and particularly during flying. Thus wechecked the possibility of using one channel of the tetrode asreference electrode for the remaining three electrode tips. Thisremoves (long-distance) EEG potentials from the signal, butlocal field and action potentials will be recorded. On the otherhand, the price paid for the reduction of movement artifacts isthat using a high-impedance tetrode electrode as referenceincreases the total noise in the channels.

A sample of a 10-kHz two-channel record with an in-tetrodereference (Fig. 8A) shows one main difference to conventionalextra-cellular recordings: spikes not only shoot down but alsocan shoot up when a cell is firing near the reference tip (markedwith asterisks in Fig. 8A). The wing-flapping artifacts are stillclearly visible but no longer exceed the input range of theneurologger (Fig. 9A), making the raw data amenable tovarious types of filtering.

SPIKE DETECTION. The continuously recorded wideband sig-nals were digitally band-pass filtered (Hamming window-basedfinite impulse response filter, cutoffs at 300 and 3,000 Hz, filterorder 250). The input data were processed both in forward andreverse directions. The resulting sequence had precisely zero-phase distortion and doubled the filter order. The average valueof the filtered signal was computed in a sliding window (0.2ms) for spike detection. Standard deviations were calculated toestimate the variance of the baseline noise and to establishdetection thresholds. The estimation of the noise variance was

improved in the second iteration by including into calculationonly nonoutlier time points (with a deviation �5 of the previ-ously calculated SD). Spikes with the deviations Ai and Bi inchannels A and B (at time point i) were extracted if (Ai/�A)2 �(Bi/�B)2 � 25 (�A and �B indicating SD of noise in thecorresponding channels).

SPIKE SORTING. The extracted spike waveforms were sepa-rated on the basis of their spike amplitude and wave shape(Csicsvari et al. 1998). The spike waveforms were recon-structed to 40 kHz by using the principles of the samplingtheorem (Press et al. 1992), and the peaks of the reconstructedwaveforms were realigned. A time point i where the equation(Ai/�A)2 � (Bi/�B)2 reaches its maximum was taken as a centerof the spike form. Instead of simple peak-to-peak measurementof the spike amplitude, all sampled amplitude values �1.35 msfrom the peak were used to reduce noise-induced variance.Signals were down-sampled back to 10 kHz to reduce compu-tational load. Thus each spike waveform consisted of 27 points.The information encoded in the amplitude values was com-

A

B

FIG. 8. Two-channel 10-ksps recordings of action potentials using anin-tetrode reference. A: band-passed filtered activity patterns in the twochannels. Note that neurons in the vicinity of the reference electrode givespikes directed upwards, clearly distinguishable from the activity near otherelectrode tips. This helps in separating neurons. B: cluster plots of spikeactivity in the 2 channels identifying 4 neurons and their average waveformduring 2.7 ms. Amplitude range of the boxes is 120 �V. Note good separationof the 4 units made by recording from 2 channels only. Neuron 1 appeared nearthe electrode tip 1 of the tetrode, neuron 3 near electrode 2, and cells 2 and 4near the reference electrode. Neuron 1 is visible at the 1st half of the recordsample at Fig. 8A, neuron 4 fires in the second half of this sample, its spikesindicated by asterisks.

FIG. 7. Samples of neuronal wideband recordings and filtering obtainedwith the neurologger in an anesthetized pigeons (A) and after chronic implan-tation of electrodes while the pigeon was placed in a recording chamber but didnot move (B). Only 1 channel of the tetrode was digitized with a rate of 16ksps; the external reference electrode was a low-impedance 150-�m nichromewire. Band-passed filtered recordings (300–3,000 Hz) are shown below thewide-band records in A and B, respectively. Note the ability of the logger torecord EEG/local field potentials together with action potentials. The increasednoise level (or reduced spike amplitude, respectively) in the chronic recording(B) probably reflects the poorer mechanical properties of the head-mountedmini-microdrive permitting less precise adjustments of electrode position ascompared with the microdrive in the stereotaxic apparatus.





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pressed using PCA. The first three principle components werecalculated for each channel. Thus a single spike was repre-sented by six waveform parameters as a six-dimensional fea-ture vector. An automatic algorithm (“KlustaKwik”, availableat http://osiris.rutgers.edu/Buzsaki/software), was used for iso-lating neurons, followed by manual clustering.

NEURONAL ACTIVITY IN RESTING PIGEON. The data from thelogger clearly permit isolation of different neurons accordingto cluster plots and waveform (Fig. 8B). Neuronal activity at atetrode placed in the Area parahippocampalis (stereotaxic co-ordinates AP6.0, L2.0, H1.0; 1 tetrode channel was used asreference) was recorded during 1 h in a resting pigeon. Duringrecording, the pigeon was kept in a large cage separately fromother birds. From the recordings, four cells were identifiedusing information from two channels of the tetrode only.During this period the averaged firing rates of these fourneurons were 0.092, 1.10, 0.21, and 0.73 spikes/s, respectively.These are low firing rates, even for pigeons (Siegel et al. 2005).

Thus temporal coincidence (overlapping) and annihilation ofspikes at the reference and another electrode is unlikely. Thenoise-induced SDs were 5.37 and 5.76 �V for the first and thesecond channels, respectively. This level of noise is acceptablebecause the spike-detection thresholds (26.85 and 28.8 �V)calculated at the basis of these values do not cut clusters out ofthe detected neurons: these clusters have a decent oval shape(Fig. 8B). Possibly gold-plating of the electrode tips (Gray etal. 1995) might help to reduce noise in future experiments.

Our number of four separable units fits well with previouslypublished results showing an average number of detected unitsof 1.8 for stereotrodes, and of 5.4 for tetrodes, respectively(Gray et al. 1995). The additional recording from the in-tetrodereference electrode obviously helps to identify single-unit ac-tivity without increasing the number of recording channels,limited to two at 10 ksps in the current logger version. This isespecially valuable for miniature portable systems that havelimited computational power and data storage but should havea wide input range permitting to record slow field potentialsalso.

NEURONAL ACTIVITY DURING FLIGHT. The data logger clearlypermits to identify single-unit activity during both flight andresting using an in-tetrode reference for recording (Fig. 9).Two channel wide-band extraction revealed the rhythmic wingartifacts yet superimposed to them action potentials (Fig. 9A).Extracting the same signals with band-pass filtering (300–3,000 Hz) permitted to separate clearly the action potentials. Acomparison of single spikes with high magnification, oncefrom flight (Fig. 9B) and once from sitting just after landingoutside (Fig. 9C), showed that the latter was preceded andfollowed by a slow potential drift. These potential drifts weretypical for this neuron under sitting conditions, being associ-ated with every spike, yet sometimes occurring without anaction potential. While the significance of this neuronal behav-ior is unknown yet, it nicely illustrates the quality of the loggerrecordings.

D I S C U S S I O N

The current article describes design and application of aneight-channel EEG- and two-channel action- and field-poten-tial data logger that were used successfully to record EEG andneuronal activity in flying pigeons. The systems are capable toquantify and store eight analog channels simultaneously atEEG sampling rates and two channels at sampling rates neededfor single-cell activity recording. They are characterized bysmall size, low weight, low power consumption, and absenceof noise and channel crosstalk yet permitting high-quality datarecording of EEG and action and field potentials. Thus theyfully meet the demands for short- and long-term recordings infreely moving animals.

One of the main benefits of data logging in comparison withradio-telemetry is the possibility to use it under natural condi-tions where a big distance between animal and the experi-menter makes radio-telemetry difficult because of size/powerlimitations. Up to now, we used EEG/neuronal recording inpigeons flying �22 km from the home loft. To link a pigeonwith the home loft by radio at such distance is practicallyimpossible given the small size of the bird. Moreover, accurateradio transmission depends much on topography and weather

A

B

C

FIG. 9. Neuronal activity in a pigeon during flight and rest as recordedusing an in-tetrode reference electrode implanted into the area parahippocam-palis, coordinates AP 6.0, L 2.0, H 1.0; sampling rate 9 ksps. A: widebandextraction showing regular wing-flap artifacts with superimposed spikes in 2channels (red and green). Below, the band-passed filtered (300–3,000Hz) spikeactivity in the 2 channels. B: magnified part of the inlet in A showing widebandand band-passed filtering of the 2 channels. C: activity of the same neuronduring resting of the pigeon. Note the potential shift prior to spiking, charac-teristic for that neuron.





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conditions. Thus for such investigations data logging remainsthe only tool so far.

Another benefit of data logging is the quality of EEGneuronal activities recorded by multichannel registration. Intraditional radio-telemetry of analog data, the transmitted volt-age is not always fully proportional to the real voltage. Inone-channel telemetry, the nonlinearity is usually not impor-tant, but when one subtracts two signals close to each other, asin case of multichannel EEG telemetry, the relative error canincrease massively. Another problem intrinsic to multichannelradio-telemetry of analog signals is channel crosstalk. For suchsystems, it is assumed that a satisfactory crosstalk should be�2% (Perkins 1980). In our system, the nonlinearity andchannels crosstalk are not detectable at all (�0.1% of thescale). Finally, multichannel telemetry systems with analog-digital conversion in the sender suffer from high power con-sumption, complexity, and weight. In addition to the transmis-sion circuitry, they should have a microcontroller with ADCthat also consumes significant amount of current.

Multichannel neurologgers can serve as an elegant tool tostudy brain-behavior interaction in freely behaving animals notonly under naturalistic conditions. A final, not so obvious,advantage of our data logger is that it operates usually inenvironments without electrical noise typically present in lab-oratory installations without shielding.

On the other hand and given the high quality of multichannelrecording, it could prove useful also in laboratory-based appli-cations that do not require on-line supervision, for exampleepilepsy and sleep monitoring. For one, the costs are muchlower than in conventional radio-telemetry. The productioncost of the neurologgers fabricated in small amounts can beestimated as $300. This sum splits into approximately equalparts between cost of 1GB flash, cost of other components, andcost of assembly work. The other point is that it allows theoperation of many simultaneous recordings—something noteasily achieved with radio-telemetry requiring different trans-mission frequencies or shielding of sender-receiver pairs.However, using the neurologger in the laboratory for interac-tive applications instead of radio-telemetric systems carries aminor inconvenience. The recorded signals are not visible tothe experimenter in real time. This means that an on-linecorrection is not possible. This is an inherent feature of theneurologger that hardly can be compensated.

At present, the main disadvantage of the neurologger used isthe necessity to link it with cables to the electrode socket on thehead of the animal and its dependence of the battery carried onthe back. However, having achieved satisfactory functionalitywith these prototypes, we are confident in our ability tominiaturize these loggers to the extent that they will fit entirelyon the head of a small animal, including battery. This willallow the ultimate freedom for animal movement and neuronalrecording in whatever environment.

A C K N O W L E D G M E N T S

We appreciate the help, advice, and support from A. Loizzo at the IstitutoSuperiore di Sanita in Rome and thank I. Panova and A. Abramchuk for helpwith neurologgers development and manufacturing. We are particularly grate-ful to N. Bologova, S. Pazhetnova, N. Ben Abdallah, and C. and M. Calderonifor expert care of pigeons. We also thank two anonymous reviewers for helpfuland constructive comments.

G R A N T S

This work was supported by the Swiss National Foundation for ScientificResearch, Scientific Co-operation between Eastern Europe and Switzerland7IP62145, and the National Competence Center for Research Neural Plasticityand Repair.

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