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Nicolelis MAL, editor. Methods for Neural Ensemble Recordings. 2nd edition. Boca Raton (FL): CRC Press; 2008.

Chapter 6 Multielectrode Recordings in the Somatosensory SystemMichael Wiest, Eric Thomson, and Jim Meloy.

INTRODUCTIONA fundamental goal in systems neuroscience is to explain animal behavior in terms of the dynamics of neuralensembles. Multielectrode techniques greatly facilitate the approach toward this goal. Aside from the fact that eachexperiment provides a higher yield of data as compared to single-site recordings, some questions simply cannot beaddressed using only one electrode at a time. For example, only multisite recordings can determine whetherdifferent neurons respond independently to stimuli, or covary from trial to trial. The purpose of this chapter is toreview methods used in multielectrode studies of the rat somatosensory system, with an emphasis on the whiskersystem. We present a basic toolbox of methods we have used to probe the functions of populations ofsomatosensory neurons in a behavioral context. The basic toolbox includes techniques for applying controlledwhisker stimuli, behavioral training in tactile discrimination tasks, multielectrode recordings, reversibly inactivatingspecific brain areas, and analysis of the ensemble neural data.

These methods have already revealed fundamental properties of the somatosensory system that would have beendifficult or impossible to uncover using single-electrode recordings. For example, cortical (Zhu and Connors, 1999;Ghazanfar and Nicolelis, 2001; Diamond et al., 1992; Ghazanfar et al., 2000; Schubert et al., 2001) and thalamic(Armstrong-James and Fox, 1987; Nicolelis and Chapin, 1994) neurons have large multiwhisker receptive fields thatare dynamic over poststimulus time (Nicolelis and Chapin, 1994; Ghazanfar and Nicolelis, 1999; Ghazanfar et al.,2000). These data, together with observations of supralinear summation of multiwhisker inputs (Ghazanfar andNicolelis, 1997; Shimegi, 2000), suggest that tactile receptive field dynamics function to integrate time-varyingmultiwhisker inputs (Ghazanfar and Nicolelis, 2001). For example, analysis of multineuron response data revealedadditional stimulus-coding properties of somatosensory ensembles. S1 ensembles code stimulus location in single-neuron temporal patterns and the relative response latencies of their neurons, but not in single-trial covariationsamong the neurons (Nicolelis et al., 1998; Ghazanfar et al., 2000). In S2 of the primate, on the other hand, single-trial covariations among multiple neurons did contribute significantly to coding the location of a punctate stimulus(Nicolelis et al., 1998; Ghazanfar et al., 2000). Even in S1, the contribution of coordinated firing may increase withgreater stimulus complexity, because multiple whisker stimuli lead to a higher prevalence of synchronous responsesbetween neurons in the infragranular layers of S1 than in other layers (Zhang and Alloway, 2005).

Combining methods for inactivating specific neural inputs with ensemble recordings led to the further conclusionthat spatiotemporal RF properties of somatosensory neurons arise not only from intrinsic local properties of neuronsand their neighbor connections, but rather from interactions among multiple levels of the somatosensory system.For example, recording thalamic tactile responses in the presence and absence of cortical feedback revealed thatcorticofugal projections contributed to both the short- and long-latency components of ventral posterior medialnucleus (VPM) responses (Krupa et al., 1999; Ghazanfar et al., 2001). These interlevel interactions were reflectedin simultaneous recordings in trigeminal areas in brain stem, thalamus, and cortex, which revealed widespreadoscillatory synchronization of neural firing (Nicolelis et al., 1995). The correlated activity remains even aftertransection of the facial nerve, which suggests that such synchronous activity is generated centrally. Although thehigh coherence among large populations of neurons associated with this oscillatory 712 Hz brain state suggestedabsence seizures to a number of authors (Marescaux et al., 1992; Shaw et al., 2006; Shaw, 2007), a direct testshowed that rats respond robustly to mild tactile stimulation during bouts of 712 Hz oscillations in S1, contradictingthe absence interpretation (Wiest and Nicolelis, 2003). Thus, widespread synchronized neural firing need notpreclude perception; in fact, it can enhance aspects of sensory representation (Fontanini and Katz, 2006) as well aslong-term plasticity (Erchova and Diamond, 2004).

These demonstrations of fast interactions among neurons distributed across the somatosensory maps at multipleprocessing stages were paralleled by demonstrations of a tight coupling between the two hemispheres of S1(Shuler et al., 2001; Wiest et al., 2005). This cross-talk challenges the classical conception of the S1 barrel cortexas an encoder for exclusively contralateral whisker activity and suggests the potential importance of bilateralinteractions in S1 for whisker-guided discriminations (Krupa, 2001b; Shuler et al., 2002).

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Multielectrode recordings in different layers of S1 while rats performed a bilateral whisker-guided discriminationrevealed that a feed-forward model of tactile signal processing cannot explain S1 response properties (Krupa et al.,2004). For example, firing rate modulations that began before tactile stimulation clearly could not be explained interms of bottom-up propagation of a stimulus signal. Rather, other inputs to S1 must contribute to shaping the task-related responses. Similarly, tactile responses were found to vary significantly in different spontaneously occurringbehavioral states (Nelson, 1996; Fanselow and Nicolelis, 1999; Moore et al., 1999; Nicolelis and Fanselow, 2002;Castro-Alamancos, 2004; Moore, 2004).

These data collectively suggest that widely distributed neurons coordinate their activities on millisecond time scales,and that the functional connectivity among them can be quickly adjusted in different behavioral contexts.

The preceding examples are meant to indicate the range of results that have already been achieved usingmultielectrode arrays (MEAs). In the following sections we present specific methods developed in the past 15 years.The examples have been selected to represent methods from each major phase of a typical study, from electrodedesign and surgical implantation, through ensemble recording involving somatosensory stimulation, behavioralmonitoring, and reversible inactivation of specific brain areas, to analysis of the recorded many-neuron data.

ELECTRODES AND SURGICAL PROCEDURES

Fixed Electrode Array Specifications

To the extent possible, the multielectrode arrays should be tailored to the scientific goals and the anatomical areasunder study (see chapter 1). To study the properties of output neurons in layer V of S1 cortex in the rat (Ghazanfaret al., 2000; Ghazanfar et al., 2001; Shuler et al., 2001; Wiest et al., 2005), we would use 32-electrode arrays with 2rows of 16 electrodes, with 250 m spacing between rows and between electrodes within a row. This configurationaffords broad sampling of the large cortical whisker representation in rats. The standard electrodes are 35-m-diameter tungsten wire insulated by S-Isonel, attached to Omnetics 32-pin connectors. Two stiff prongs protrudefrom either side of the connector by which to hold the array during implantation; these are cut off after the array hasbeen cemented in place during surgery. For a layer-V target (~1.31.5 mm below the cortical surface), 3.5 mm ofelectrode length is left exposed with blunt, i.e., flat-cut, tips (see chapter 1 for details).

Similar arrays, often with a 4 8 configuration, are used to record VPM neurons. Although electrode bundles werecommonly used in the past to access thalamic neurons, even deeper structures such as the brain stem can beprobed using electrode arrays with long wires.

Moveable Electrode Arrays for Layer Analysis

For a study comparing the firing and coding properties of neurons in different cortical layers during tactilediscrimination behavior, it was necessary to record ensembles of single-unit activity through different depths in S1cortex. Krupa (Krupa et al., 2004) developed a movable electrode array that incorporated ultrafine wire electrodes(12 m diameter) into movable arrays of 16 or 24 microelectrodes. The 16-channel arrays were arranged in a 5/6/5pattern, with microelectrodes 250 m apart. The 24-channel arrays were arranged in a 4 6 pattern, 250 m apart.The very small exposed tips of these ultrafine microwires yielded high-impedance electrodes (Z = 2.53 M @ 1KHz) capable of recording high signal-to-noise single-unit activity throughout the different layers of S1.

Implantation and Advancing Through the Layers

The basic surgical procedures for electrode implantation are described in further detail in chapter 2. Briefly, forimplantation into S1 cortex, rats are anesthetized with ketamine (100 mg/kg) and xylazine (8 mg/kg) and placed in astereotaxic head holder. (In some cases Pentobarbital (50 mg/kg i.p.) is used as the anesthetic for female rats.)Using a dental drill, a small craniotomy is made over the barrel cortex (3 mm caudal from bregma, 5.5 mmmediolateral), exposing the brain surface covered by the dura.

For rat implantations we generally transect the dura before inserting the array into the brain, or the electrodes maynot penetrate and the brain would be compressed instead. Our standard method of removing the dura is to use alarge hypodermic needle with a bent tip to hook, lift, and cut the dura, so that it can be retracted with forceps. Thearray is then lowered into S1 no faster than 100 m/min. Finally, the electrode assembly is fixed in place with dentalacrylic.

For the study using moveable arrays (Krupa et al., 2004), prior to implantation of the array a single sharp tungsten

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microelectrode (Z = 1.5 M @1 KHz) was lowered approximately 0.7 mm into several locations within the region ofthe barrel cortex while neural activity recorded by the electrode was monitored. The contralateral large facialwhiskers were manually stimulated to locate the C3 barrel as well as the general orientation of the barrel region.This procedure allows the array to be centered over the C3 barrel. The moveable arrays were only lowered 100 minto the cortex at the time of surgery. They were oriented normal to the S1 cortical surface so that as they traversedmultiple depths they would sample activity from a single cortical column.

In general, rats receive at least 7 days of postsurgical recovery, during which no behavioral training takes place.During this time rats implanted with movable electrodes would have their arrays advanced approximately 2040 mper day until single-unit activity was detected. Prior to data collection, rats involved in a behavioral study received atleast two behavioral discrimination sessions with the multichannel recording headstages and cables attached, butwithout recording, to allow rats to adapt to performing the task with the attached cables. All rats adapted quickly;task performance before surgery and after these adaptation sessions did not differ.

Histology

At the end of all recording sessions, small electrolytic marking lesions (20 A/5 s) were made through severalelectrodes within each array. Rats were injected with a lethal dose of pentobarbital and perfused with saline and10% formalin. Brains were removed, fixed in formalin/sucrose, sectioned, and stained with cresyl violet to identifythe marking lesions. The depth of the marking lesions was used to confirm the depth of the electrode arrays duringdifferent recording sessions. We have thus far chosen to use coronal slices to verify the depth of our electrode tips,but for some scientific questions it may be desirable to section brains tangentially to determine electrodes positionswith respect to the cytoarchitectonic barrel/septa distinction in layer IV, which has been proposed to havephysiological correlates in other layers as well (Castro-Alamancos, 2004).

MULTICHANNEL RECORDING

Hardware and General Recording Procedure

Single-unit (SU), multiunit (MU), and local field potential (LFP) activity are recorded through a Many-NeuronAcquisition Processor (MNAP; Plexon, Inc., Dallas, Texas). The data are sampled at 40 KHz. Units are first sortedonline during each recording session, according to the shapes of supra-threshold spikes. Waveforms whoseamplitude is more than two standard deviations above the background noise are saved for offline sorting (Nicoleliset al., 2003). Unless more stringent criteria are applied to the online-sorted units, such as a test for spike conflictsi.e., multiple spikes falling within a neurons 12 ms refractory periodthen the online-sorted units, which mayinclude spikes from multiple neurons, are termed MUs. Digitized spike waveforms are also stored on computer harddisk for offline analysis, so units are further sorted offline according to their clustering in the principle componentspace representing the spike waveforms. Those sorted units that show a distinct cluster in principle componentspace from the cluster of noise waveforms, and displayed fewer than 0.1% of interspike intervals within a refractoryperiod of 1 ms, are termed SUs.

Following each recording session with movable arrays, the arrays were typically lowered a total of 100 m (in 2040m steps) before the next recording session to ensure that the arrays were sampling different populations ofneurons. This procedure was repeated until the electrode arrays had been driven through the entire thickness ofcortex.

SOMATOSENSORY RECORDINGS IN ANESTHETIZED AND BEHAVING ANIMALS

Precise Whisker Stimulation in Anesthetized Rats

To study somatosensation in the rat we have focused on the system of large facial whiskers that rats rely on formany purposes, such as navigation and object recognition in the dark. The combination of a discrete array ofsensorsthe vibrissaeand a clear corresponding topographical representation at each ascending level of thesomatosensory system provides a convenient framework for addressing questions of sensory coding andintegration.

The absence of gross movements and fluctuations of behavioral state in anesthetized rats affords the opportunity toobserve neural responses to arbitrary finely controlled multiwhisker stimuli. Of particular interest are questions ofmultiwhisker integration, neural plasticity related to repetitive stimulation of particular combinations of inputs, and

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neural responses to complex naturalistic patterns of stimulating many whiskers. To deliver precise multiwhiskerstimulation, Krupa et al. (2001a) developed a multiwhisker stimulator incorporating 16 stainless steel wires (130 mdiameter) attached to computer-controlled miniature solenoids (Figure 6.1a). The other ends of the wires areattached to individual facial whiskers, such that activation deflects whiskers by 12 mm at approximately 1 mm/ms.This system affords greater stimulus control than single-whisker stimulators or air puffs, for instance.

For example, in our study (Krupa et al., 2004) comparing somatosensory neural responses during active tactilediscrimination (see below) to those under anesthesia and passive awake stimulation, we wanted to duplicate in ananesthetized rat the spatiotemporal dynamics of whisker deflections that occurred during the active discrimination(i.e., an obstacle sweeping rostrocaudally across the whiskers). This passive stimuli pattern consisted of ramp-and-hold deflection of 16 individual whiskers in three rats that were lightly anesthetized with pentobarbital (40 mg/kg,subcutaneous). The multiwhisker stimulator was attached to each of 16 facial whiskers (rows BE, arcs 03) on oneside of the face. The patterned stimulation consisted of the following: four whiskers in arc 3 (B3-E3) were deflectedsimultaneously with a ramp-and-hold deflection for 150 ms; 25 ms after the onset of arc 3 deflection, four whiskersin arc 2 (B2-E2) were deflected similarly; 25 ms after arc 2 stimulation onset (50 ms after arc 3 stimulus onset), arc1 whiskers (B1-E1) were stimulated; followed 25 ms later (75 ms after arc 3 stimulus onset) by arc 0 whisker (fourstraddlers) stimulation. Thus, the facial whiskers were stimulated in a pattern that simulated a tactile stimulusmoving caudally across the whisker pad over a period of ~75 ms, similar to the pattern of whisker deflection duringthe active task. Three hundred stimuli were delivered at 1 Hz while S1 activity was recorded.

This stimulation was very well controlled and precise, but may not have mimicked the variability or other propertiesof a solid edge sweeping across the whiskers (as in the active task), so we also recorded responses in that studyusing a moving aperture (of the same dimensions as the aperture in the active discrimination) swept across thewhiskers. The moving aperture was computer controlled and powered by pressurized air. The onset of aperturemovement, velocity of movement, and trajectory of movement were pseudorandomly varied from trial to trial basedon velocity and trajectory parameters that were obtained by video analysis of rats performing the activediscrimination. This resulted in whisker deflection dynamics that approximated the trial-to-trial lateral jitter in whiskerdeflections that occurred during the active discrimination. This sliding aperture stimulus was also applied to awakerats by acclimating them to head immobilization as described in the following text.

The multichannel stimulator has been used in the Nicolelis lab to study multi-whisker (Ghazanfar et al., 2000;Ghazanfar et al., 2001) and bilateral integration (Shuler et al., 2001), and has been adapted for use in awake rats(Wiest and Nicolelis, 2003; Wiest et al., 2005) (see following text).

Somatosensory Stimulation of Head-Immobilized Awake Rats

In extending somatosensory electrophysiology to awake animals, we strive to limit the greater variability associatedwith the awake state. One strategy is to prevent bodily movements and the uncontrolled sensory activity theycause, by immobilizing rats heads (Bermejo et al., 1996; Bermejo et al., 1998; Harvey et al., 2001; Wiest andNicolelis, 2003; Wiest et al., 2005). (An alternative approach to limiting both stimulus variability and confoundingmotor-related activity during somatosensory ensemble recordings is to train rats to repeat stereotyped activesamplings of a tactile stimulus, as described in a later section.) For head-immobilized awake recordings, a brasshead post (1/4 width, Small Parts, Inc.) was fixed to the dental-cement head-cap of subjects during the surgery forelectrode array implantation.

These rats were first habituated to head-fixed restraint in a Plexiglas restraint tube in which the rats body and allfour feet were comfortably confined in a light drawstring jacket that minimized the subjects traction against thePlexiglas tube, while its head was held fixed by a mounting post embedded in the dental acrylic. The head post fitsnugly into a pin vise (Small Parts, Inc.) attached to the restraint tube to prevent all head movement. This designwas an improvement over a previous practice of using round screws as head-bolts. Rats were often able to rotatetheir heads slightly when immobilized by a round head-bolt, and any slight freedom of movement seems toaggravate further struggling. To facilitate acclimation to restraint, rats were placed on a mild food deprivationschedule and calorie-dense liquid reward was delivered at random intervals during sessions in the restraint tube.Habituation was achieved by gradually increasing the amount of time that rats were confined in the restraint tubeeach day over a period of ~2 weeks. Prior to implantation, it was helpful to handle the rats daily, and acclimate themto restraint in drawstring jackets as well as to the reward solution (rats are often initially averse to novel tastes).However, attempts to acclimate the rats to full restraint in the Plexiglas tube before implantation of the head boltappeared to be counter-productive, as the rats would tend to struggle and panic, leading to an unpleasantassociation with the restraint tube rather than a comfortable one.

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We adapted the computer-controlled multiple individual-whisker stimulator (Krupa, 2001a) described earlier for useon an awake animal. Specifically, each of two stimulator arms was fitted with a thread noose, which could beconstricted around an individual whisker. In Figure 6.1b (right panel) the stimulator noose (at the tip of the silverstimulator arm) is attached to a single whisker.

The noose could easily be fitted over groups of whiskers as well. For example, the whisker stimulator was used(Wiest et al., 2005) to apply controlled 5 ms rostral deflections of the bottom four whiskers in a single column oneach side of the subjects face (Figure 6.3b, left panel). To lasso four whiskers, the noose was first opened wide,then fine forceps were used to pull through the whiskers one at a time before tightening the noose about 1 cm fromthe rats face. After acclimation rats allow this manipulation, relax their whisker pads, and only attempt to whiskwhen there is a noise or large movement in the room.

In a study of bilateral integration between the S1 hemispheres (Wiest et al., 2005), two basic stimulation protocolswere run. In both experiments, stimuli were presented in random order at random intertrial intervals of between 2and 4 s. In Experiment 1 the stimulus set consisted of a left arc deflection, a homologous right arc deflection, andsimultaneous bilateral deflections. In Experiment 2 the stimulus set consisted of a left, then right, arc deflection atinterstimulus intervals (ISIs) of 0, 30, 60, 90, and 120 ms. TEMPO software (Reflective Computing, St. Louis, MO)controlled the stimulus administration and sent time-stamped stimulus events with millisecond accuracy to the file ofneural recordings. Usually, at least 200 stimulus presentations could be delivered during an hour-long recordingsession.

For another set of experiments (Wiest and Nicolelis, 2003) the head-immobilization setup was expanded toaccommodate a behavioral response from the rat. This study was aimed at comparing rats behavioralresponsiveness during a highly coherent oscillatory cortical state and a desynchronized state. A solenoid-gatedreward tube was positioned at the animals mouth to deliver drops of liquid reward. An optical beam sensor waspositioned directly in front of the reward tube such that it reliably registered the animals licks for reward.Programming the reward solenoid to release a drop gated by the animals licks greatly facilitated the process ofacclimating the animals to head restraint. Drops of reward were initially made available only once per second toavoid satiation or spillage. After acclimation, drops of reward could be made contingent on a perceptual task. Inparticular, we trained rats to inhibit their licks for reward until the stimulator delivered a 5 ms, ~1 mm deflection offour whiskers from a single arc. Sessions were conducted in the presence of white noise to mask any auditory cues,and control sessions with stimulator unattached to whiskers verified that whisker cues were used exclusively todetect the stimulus. To initiate a trial, the rat had to refrain from licking for at least 1 s. A peaked response (licklatency) histogram following stimulation shows that the rats were responding to the stimulus rather than lickingrandomly. Occasionally, the rat is startled by the stimulus, and instead of a lick, the sensor registers a startlemovement of the jaw. These events are obvious to an observer and can be clearly segregated in the responsehistogram as a sharp peak at a latency of about 30 ms. Thus, this head-fixed paradigm combined with a lickdetector and reward dispenser appears to have the potential for a variety of rat somatosensory psychophysics.

Tactile Stimulation and Recording in Freely Moving Rats

Nerve-Cuff Stimulation

For a study comparing tactile responses during different behavioral states (such as the quiet awake state and theexploratory whisking state), it was desirable to stimulate the somatosensory system without constraining theanimals natural behavior in any way. To achieve this, Fanselow and Nicolelis (Fanselow and Nicolelis, 1999)custom made cuff electrodes to encircle the infraorbital nerve, the branch of the sensory division of the trigeminalnerve that carries tactile information from the facial vibrissae to the trigeminal ganglion. Two platinum bands(Goodfellow, Berwyn, PA) separated by 0.8 mm, were connected to flexible three-stranded Teflon-coated wires (AMSystems, Everett, WA) so that current could be passed between the bands. The whole construct was embedded ina thin film of Sylgard (Factor II, Lakeside, AZ) to hold the electrode together and provide electrical insulation. Theinside platinum faces were left exposed for nerve stimulation, and a piece of surgical silk was attached to theoutside of the band for maneuvering the electrode during implantation. The inner diameter of the finished cuff wasabout 1.7 mm.

During surgery a dorsoventral incision was made on the face lateral to the infra-orbital nerve, and tissue wasdissected until the nerve was exposed and perineurium was cleared away. Surgical silk was inserted under thenerve so the cuff electrode could be drawn under and around the nerve, just rostral to the infraorbital fissure. Thecuff was tied off using the attached silk. Wires from the cuff electrode were led subcutaneously to the connector on

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the skull.

During the recording session, stimuli were generated using a Grass S8800 stimulator in conjunction with a GrassPSIU6 stimulus isolation unit. 100 s pulses of 59 mA in amplitude were typically successful in evoking neuralresponses. The current amplitude was set to 1 mA higher than the threshold for evoking responses. The neuralresponses were found to deliver highly consistent stimuli that moreover did not depend on the position of theanimals whiskers. Neural responses to this artificial stimulation were very similar to those obtained by mechanicaldeflection of individual whiskers, except for a shorter latency. To minimize interference with the animalsmovements, recording cables were connected to a rotating commutator at the top of the recording chamber.

This stimulation apparatus was subsequently applied as an alternative means for disrupting pentylenetetrazole-induced seizure activity in rats (Fanselow et al., 2000). In contrast to vagus nerve stimulation studies, no substantialcardiovascular side effects were observed by unilateral or bilateral stimulation of the trigeminal nerve. Moreover, bytriggering trigeminal nerve stimulation on the occurrence of seizure episodes, this method resulted in more effectiveand safer seizure reduction per second of stimulation than with previous methods, and may thus lead to improvedhuman treatments.

Active Tactile Discrimination

The nerve cuff stimulation is appropriate for a study comparing neural responses to identical tactile stimulation indifferent behavioral states. To study the neural mechanisms by which rats actively sample and discriminate distincttactile stimuli, however, another approach is required. To this end, a whisker-dependent tactile discrimination taskwas developed (Krupa et al., 2001b) in which a rat repeatedly samples a variable-width aperture with its facialwhiskers, by advancing its head through the aperture (Figure 6.2). At the end of each trial the rat is required to go toa spout located to the left or the right depending on whether the aperture discriminandum was narrow or wide,respectively.

Computer-controlled behavior boxes (see section Behavioral Testing Chamber Details below) were built in-houseso that the discrimination task with well-trained rats proceeded as follows. At the start of each session, rats wereplaced in the outer reward chamber (the main chamber in Figure 6.2a). The first trial of a session begins when thesliding door between the outer reward chamber and the inner discrimination chamber opens by computer control.Rats quickly proceed into the center discrimination chamber and toward the center nose poke. In doing so, ratsdisrupt an infrared photobeam in front of the variable-width aperture and, immediately after, their large facialwhiskers contact the variable width aperture. Upon breaking the infrared photobeam and sampling the variablewidth aperture, rats then back out into the reward chamber. To receive water reward, rats have to correctly poketheir nose into either the left or right nose poke: left nose poke if the aperture was narrow; right nose poke if theaperture was wide. Rats perform the task in complete darkness to eliminate the use of visual cues. To verify thatthey relied not only on tactile cues, but specifically on their large vibrissae, the vibrissae are cut at the end of anexperiment and the rats are run through the task again to see that their performance is reduced to chance levels.

To motivate them for training, the rats are placed on the following water restriction schedule. Between 15 and 60min after the end of each daily training session, rats receive free access to water for 1560 min; food is available adlibitum. Once a week rats are given free access to water for 24 h followed by water restriction for 24 h, at whichpoint training is resumed. Thus, for each 7-day period, rats receive 6 daily training sessions (with 1 h access towater after each session), followed by 1 day of free access to water. This restriction schedule is repeatedthroughout an entire series of experiments. All rats have shown significant weight gains over the course of training,and no health problems have occurred with this protocol.

Typical narrow- and wide-aperture widths of 60 and 68 mm are moderately difficult for rats to discriminate, but theyare capable of significantly finer discriminations (62 and 65 mm) (Krupa et al., 2001b). Rats receive a 50 L waterreward for a correct response and no reward for an incorrect response. Immediately after rats poke into either thecorrect or incorrect reward nose poke, the center door between chambers closes and the aperture is randomly resetto either wide or narrow. The next trial begins up to 30 s later when the center door was again opened. Wide andnarrow trials are presented randomly throughout each 75 min training session. Well-trained rats typically performed~80 or more trials per session with approximately 8085% correct discriminations using an intertrial interval of 30 s.This long intertrial interval was used initially so that rats would be motivated to correctly discriminate rather thanaccepting the rate of reward associated with 50% correct performance. However, recently we have been able totrain rats to routinely perform on the order of 200 trials per session using intertrial intervals as low as 5 s. The abilityto record a relatively large number of trials per session is an important and attractive feature of this behavioralparadigm: reliable scientific conclusions require sufficiently powerful statistics. The number of trials recorded per

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session becomes increasingly important, for example, in variants of the task that present more than two differenttactile stimuli to be discriminated.

Training continues until the rats correctly perform the discrimination at criterion (typically 75% correct) accuracy forat least three consecutive sessions. Rats are then removed from water restriction before surgical array implantation(as described earlier). Following at least 7 days of postsurgical recovery, rats are returned to water restriction andthe behavioral discrimination training. After reacclimation to the task, neural ensemble activity can be recordedduring active discrimination behavior.

A detail may serve to illustrate the kinds of considerations that go into choosing or designing a particular trainingprotocol. In initial studies (Krupa et al., 2001b), rats were required to trigger the center nose poke (see Figure 6.2)during their sampling of the aperture discriminandum. Later (Krupa et al., 2004), rats were only required to breakthe aperture photobeam and not the center nose poke beam. This was done to minimize the possibility that ratswhiskers might be stimulated as they fully poked their nose into the center nose poke. Moreover, because rats didnot have to break the center nose poke photobeam, the total amount of time that rats were required to sample theaperture, as well as trial-to-trial variability in contact and sampling times, were minimized. This resulted in a veryrepeatable, stereotypic whisker stimulation. A highly consistent stimulus is as desirable as a large number of trials.On the other hand, in the mandatory center nose poke protocol there is a longer delay between whisker contactwith the discriminandum and the beginning of the rats movement toward the left or right reward spout. This delay isactually an advantage in terms of dissociating sensory representation of the narrow or wide stimulus from motor-related somatosensory responding due to left or right movements. During that delay, left or right movement-relatedactivity will not be confounded with wide or narrow sensory representations. There may be an unavoidable trade-offbetween the speed of trials in the photo-beam only protocol and this potential interpretational advantage of themandatory central nose poke protocol.

This behavioral paradigm for active tactile discrimination is flexible, in that the training boxes and protocols can beadapted for a number of different tasks. For example, Shuler et al. (2002) adapted the wide versus narrowdiscrimination by adding a third, asymmetric stimulus configuration, such that rats were forced to compareinformation from the two sides of their face to successfully perform the task. Recently, Pereira et al. (2005) haveadapted the apparatus to develop a texture discrimination task, by attaching removable magnetic strips (built by JimMeloy) to the aperture. Sandpaper strips of different coarsenesses are glued to the magnetic strips to providetexture cues. Two different textures (60 grit rough and 120 grit smooth) are affixed to two different apertures in asingle behavior box. By retracting one aperture and presenting the other, a single texture can be presented on eachtrial. As a control to verify that the animal uses the texture cue rather than the position of the two apertures, theposition of the texture strips can be reversed during some sessions. Another variant of the task uses four equallyspaced aperture widths to study the neural correlates of category perception (Thomson et al., 2005).

Rat Behavioral Testing Chamber Details

The rat behavioral testing chamber (Figure 6.2) is a multifunctional rat behavioral testing apparatus designed toprovide a platform for tactile discrimination experiments. System capabilities include 20 control inputs, 32 signaloutputs, and a choice of 36 selectable timing outputs.

The chamber (Figure 6.2) consists of the main chamber and the whisker discrimination module. The main chamberis the primary holding area and provides the mounting location of two nose poke modules for liquid reward delivery.The whisker discrimination module contains a center nose poke module and seven discrete whisker discriminationbars with infrared photo-beam detectors. The main chamber and the whisker discrimination module are separatedby a pneumatically operated center door.

There are three reward solenoids valves on each side of the chamber that deliver liquid rewards to the left and rightnose pokes. There is a printed circuit board black and white 1/3 in. CCD (60 fields/s) camera located in both themain chamber and the whisker discrimination module with outputs to Panasonic BW-BM 990 video monitors.

The chamber base consists of a frame and cap. The frame is 2.0 in. 0.75 in. PVC and the cap is high-densitypolyethylene. The Main Chamber and the Whisker Discrimination Module are constructed of 0.125 in. aluminum,and the chamber door and chamber cap are constructed of 0.25 in. clear acrylic sheet. The nose poke modules areconstructed of 1.0 in. black Acetal (Delrin).

Overall unit dimensions are 22.5 in. (L) 16.0 in. (W) 16.5 in. (Height).

A system overview of the behavioral chamber follows:

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Main circuit board: The control board is the primary system electronic interface to the behavioral chamber;it provides power distribution, photo-beam processing, input/output isolation for timing signals, and theMed Associates interface for input/output control.

Pneumatic controller: There are three double-acting pneumatic cylinders used in the chamber that operatethe left nose poke door, right nose poke door, and center door. The pneumatic controller receives inputcommands from the main circuit board and activates four-way pneumatic valves. The pneumatic valvesare fitted with manually adjustable speed controllers to regulate the opening and closing speed of thedoors. An external 20 ft /min compressed air source is regulated to 35 lb/in prior to supply routing to thepneumatic valve.

Manual control: A printed circuit board mounted on the chamber cabinet to provide manual activation ofoutputs such as reward solenoids, pneumatic door solenoids, and house lighting.

Plexon interface: This printed circuit board inputs 36 timing events from the main circuit board and allowsthe user to select and hardwire 14 of the timing events to send to the Plexon DSP module.

Plexon DSP module: The module is configured for 14 individual TTL events, asserted Low True.

Parallax BSII: The BASIC Stamp 2 (BS II) is a 24-pin DIP (dual inline package) microcontroller module. Itis the main controller for the 14 servos that drive the whisker discriminator bars (Figure 6.2; see followingtext). The BS II receives a series of command pulses from a Med Associates DIG-726 output card andthen issues serial commands to the Mini SSII Servo Controller, which in turn position the servos to theappropriate settings. The BS II is programmed in the PBASIC language on the host PC, and the programsare loaded via the host computers serial port to a DB-9 connector on the BS II carrier board.

Mini SSC II servo controller: The Scott Edwards Electronics, Inc., Mini SSC II is an electronic module thatcontrols seven pulse-proportional servos according to instructions received serially from the BSII. There isa Mini SSC II for the left set of whisker discrimination bars and another for the right set.

Servo: The drive servo is a Futaba S148 pulse-proportional servo with a linear servo conversion kit. Theservo receives position information from the Mini SSC II Servo Controller and positions the whiskerdiscrimination bar through a series of push rods and gears.

Med Associates input/output: System command and control is accomplished through products provided byMed Associates, Inc. MedState Notation protocols are translated and compiled with Trans IV, and MED-PC IV is the run-time operating system. Outputs are controlled with DIG-726 Super-Port Output Modulesand inputs with DIG-712 SuperPort Input Modules.

Whisker discrimination module: The whisker discrimination module (Figure 6.3) is used for tactilediscrimination experiments. The primary operating feature is the set of whisker discrimination bars (WDBs)(Figure 6.3). The WDB is constructed from aluminum stock with dimensions 4.0 in. Height (H) 3.5 in.Length (L) 0.25 Thickness (T).On the face of the WDB, there are three infrared photo-beam ports (Figure 6.2b). The bars on the rightside of the module contain infrared emitters, and the bars on the left contain infrared phototransistors.Phototransistor status information is sent to the Main Circuit Board and a circuit containing an OR Gateintegrated circuit triggers a beam break output if any of the three phototransistors signal a beam break.Each WDB is driven by a pulse-proportional servo through a gearing mechanism. The travel distanceachieved is 40.0 mm with a resolution of 500 m. Experimental apertures are typically in the range of 85to 50 mm.

Whisker contact detector: An optional feature of the WDB is a whisker detection device (Figure 6.3b). Thewhisker detector consists of a photo beam produced by an 880 nm infrared emitter and detected by an880 nm phototransistor. The infrared emitter and phototransistor are installed in sealed housings with anorifice of 0.150 mm and attached to the leading edge of the WDB. A special printed circuit board is used todrive the emitter, adjust the sensitivity of the phototransistor, and send a beam break signal to the maincircuit board. Additionally, there is a beam break indicator infrared LED installed on the top edge of theWDB to provide the operator a visual beam break indication through the video system.

COMBINING ENSEMBLE RECORDINGS WITH REVERSIBLE FOCAL INACTIVATIONTechniques for manipulating selected populations of neurons during a recording experiment are a powerful means

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of testing theoretical models of neural dynamics. One such tool that has been used repeatedly in the Nicolelis lab isreversible pharmacological inactivation of local neural populations with muscimol. Muscimol is a GABA agonist.This agent has no effect on fibers of passage that may cross the area being infused and allows cortical activity toreturn to control levels once the effect of the injection wears off. The combination of muscimol infusions withmultielectrode recordings of cortical neuronal activity through arrays of microwires spaced at known distances fromthe S1 infusion site provides a precise measure of the effective spatial spread as well as the time course ofmuscimol inactivation (Figure 6.4). Previous experiments have shown that by varying the dose of muscimol, theeffective spread of the drug can vary from a few hundred micrometers to several millimeters. Because the neuralactivity on each of the microwires is continuously monitored, the effects of the drug can be tracked throughout theentire experiment. This quantitative spatiotemporal monitoring of the drugs effects is a significant improvement overpurely behavioral or single-electrode measures of a drugs effect.

For inactivating the deeper layers of S1 cortex, for example, a 27-gauge (0.016 in. OD.) thin-walled stainless steelcannula is implanted in the infragranular layers of the area of interest, just adjacent to a 32-microwire array. Thecannula is positioned either in the middle or at the end of the microwire array, approximately 300500 m awayfrom the rostral-most microwire. The depth of the cannula is approximately 1.0 mm below the pial surfaceslightlysuperficial to the depth of the electrode tips. A partially bent wire stylet is left inserted in the cannula when not in useto prevent clogging. The bend in the stylet holds the stylet in place. During recording sessions, the stylet is takenout and an inner injector cannula (33 ga, 0.008 in. O.D.) is lowered through the guide cannula so that the tip of theinjector extends up to about 0.5 mm beyond the base of the cannula. The depth of the tip of the injector cannula willbe approximately 1.2 mm below the pial surface. Muscimol (or saline vehicle) is then infused at a rate of 0.3 L/min.The dose of muscimol will depend upon the amount of cortex to be inactivated, and typically range between 50 and500 ng in 50500 nL of saline vehicle. Infusions are carried out with a precision syringe pump (Sage 361) driving a1 L Hamilton syringe. By monitoring the movement (with a calibrated gauge) of a small bubble in the polyethylenetubing connecting the Hamilton syringe to the injector cannula along with the precise displacement of the syringe,infusion volumes as low as 15 nL can routinely be achieved. Approximately 35 min after the infusion, the injectorcannula is removed and the internal stylet replaced. Previous experiments in our laboratory have demonstrated thatthis technique can reliably inactivate localized regions of S1 cortex for periods up to 68 h in a completely reversiblemanner (Figure 6.4a and [Yuan et al., 1986]), and that concurrent multielectrode recordings provide a real-timemeasure of the spread of the drug (Figure 6.4b). At the end of all recording sessions, infusions of fluorescentdextrans (10,000 MW) into S1 cortex can be used to visualize the central area of infusion.

One application of muscimol inactivation was to evaluate the contribution of corticothalamic feedback projections tothe definition of tactile responses in populations of VPM neurons. Before cortical inactivation, Ghazanfar et al.(2001) recorded control data by stimulating a large number of single whiskers. During the period of corticalinactivation, the same single- and multiple-whisker stimuli were delivered again. Upon reversal of the corticalinactivation, a subset of these stimuli were delivered to verify whether thalamic and cortical responses had returnedto control values. The reversible nature of the cortical inactivation was important for their experimental paradigmbecause multiple experiments could be carried out in the same animals. Waveform analysis, autocorrelation, andISI histograms were used to confirm that the same single neurons were being recorded throughout the experiments.The results showed that supralinear summation of VPM neural responses to multiwhisker stimuli depends onfeedback from S1.

Similarly, Krupa et al. (1999) inactivated S1 cortex to show that peripheral deafferentation-induced immediateplasticity of tactile responses in VPM is substantially affected by input from S1 neurons.

Muscimol inactivation has also been applied to determine the path of sensory whisker signals as they propagatecentrally into the brain. By inactivating individual hemispheres of S1, Shuler et al. (2001) showed that S1 single-neuron responses to ipsilateral whisker deflection are due to projections across the corpus callosum from thecontralateral S1 hemisphere, where neurons receive the primary afferent sensory signal from the whisker pad.

These examples demonstrate the utility of muscimol inactivation for dissecting the electrophysiological functioningof a complex brain circuit. Another powerful class of applications dissects the role of specific neural populations inproducing different levels of behavioral performance during tactile discrimination. For example, Shuler et al. (2002)inactivated individual S1 hemispheres in a task in which certain stimuli required integration of bilateral whiskersignal for successful discrimination. Performance deficits specific to those particular trials during unilateral S1inactivation suggested that perceptual bilateral integration depended on callosal cross-talk between the S1hemispheres.

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NEURAL ENSEMBLE DATA ANALYSIS

Single-Neuron Responses

Having collected neural ensemble data using a particular behavioral and stimulation paradigm such as thosedescribed earlier, it remains to analyze and interpret the data. Ultimately, we would like to understand the behaviorof neural ensembles in terms of the properties of individual neurons. Therefore, computing single-neuron firingproperties is a reasonable starting point for most analyses. A basic question is, which neurons respond to astimulus with a statistically significant modulation of their firing rate?

Figure 6.5 reproduced from the study (Krupa et al., 2004) of S1 neural response properties during tactilediscrimination, illustrates how a variety of properties can be derived from single-neuron response data. In this figureKrupa et al. have tabulated the distribution across cortical layers of several single-neuron properties. These includethe duration and magnitude of firing modulations in the different layers, but also the incidence of early-onsetmodulations that occur before whisker contact with the stimulus, and the incidence of different temporal patterns ofexcitation and inhibition. (Only the column labeled LVQ in Figure 6.5 reflects a multineuron measure; see followingtext). The results were calculated using the commercial analysis package NeuroExplorer. Excitatory responsedurations were defined as the time interval in which firing rate significantly exceeded (p < 0.01) background firingrate, based simply on the standard deviation of counts in individual bins. The results from many neurons weretabulated and combined by hand.

Another method based on the statistical distribution of cumulative-summed spike counts (e.g., Ghazanfar, 2001;Ushiba et al., 2002) has also been used in the Nicolelis lab to find the poststimulus latency at which the actualresponse deviates more than 99% of the null distribution of cumulative spike counts. The null distribution is basedon a prestimulus window of activity. This approach takes into account the statistics of all the spiking activity up to agiven moment, as opposed to considering each bin independently. It provides precise response latencies withoutsmoothing or otherwise distorting the peri-event spike histograms, which would introduce complexities into thedetermination of confidence intervals. Moreover, the cumulative sum method requires no assumption of a particularparametric form of the spiking statistics.

We have recently modified and automated (together with Ranier Guttierez) this method by writing a Matlab routineto calculate temporally precise response offsets, and response magnitudes as well as onsets. To assess thesignificance of deviations from the expected cumulative sum, an empirical distribution of the cumulative-summedspike count at each time bin before the stimulus is constructed from 1000 bootstrapped samples (with replacement)of the prestimulus spike histogram. This empirical distribution of prestimulus firing is then used to find thepoststimulus bin (if any) at which the cumulative-summed poststimulus spike count exceeded or was less than 99%of the cumulative spike counts from the baseline distribution (Martinez and Martinez, 2002). This bin was recordedas the onset of an excitatory or inhibitory response.

The response offset is identified as the first zero crossing of the derivative of the cumulative deviation from thebaseline spike count, meaning that at that time point the cumulative sum is no longer deviating from its expectedgrowth with time. This procedure gives the time at which the poststimulus time histogram has returned to a baselinefiring rate. Given onset and offset, of course, response duration is also implicitly defined. The response magnitudewas calculated as the number of excess spikes between response onset and offset, compared to the baselineexpected number, divided by the number of trials. Thus, the response magnitude measures the average number ofspikes per trial fired in response to a particular stimulus.

Wiest et al. (2005) used this method to automate the calculation of response latencies and magnitudes in anelectrophysiological study of bilateral integration of whisker signals in head-immobilized waking rats. This facilitatedthe calculation of bilateral integration measures for many units recorded over multiple sessions, resulting in acharacterization of the distribution of different levels of sub- and supralinear summation of bilateral inputs bydifferent infragranular S1 neurons.

LVQ-Based Ensemble Analyses

We use the learning vector quantization (LVQ) (Kohonen, 1997) pattern recognition algorithm to predict the identityof a stimulus based on neural activity recorded during a single trial. We use the percentage of correctclassifications, over all trials, as a measure of performance on the stimulus estimation task (Krupa et al., 2004). Thepercentage correct reflects the amount of overlap, in the space of neuronal responses, of the responses to differentstimuli (Thomson and Kristan, 2005). For instance, when there are two stimuli such as narrow and wide apertures, if

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LVQ gives 50% correct, then that means there is no difference in the neural response to wide or narrow apertures. Ifthe algorithm yields 100% correct, then there is no overlap between the response distributions to narrow and wideapertures. We have found that for neuronal ensemble data, LVQ outperforms methods such as linear discriminantanalysis, and performs as well as support vector machines (Mark Laubach, unpublished observations) or neuralnetworks trained via backpropagation (Nicolelis et al., 1998).

We implement LVQ as an artificial neural network consisting of two layers of artificial neurons. We implement it inMatlab (Mathworks) with the Matlab Neural Network Toolbox (Demuth and Beale, 1998). The input to the network isthe neural response to be classified. The first layer in the network is called the competitive layer, which is a winner-take-all network. The activation of a unit in the competitive layer is the distance between the input to the networkand the weights onto that unit. The output is 1 for the unit whose weights are closest to the input, and zero for allthe other units. Hence, the unit whose input weights are closest to the actual input return a 1 as output.

LVQ networks are trained via a supervised learning algorithm, LVQ1 (Kohonen, 1997). Before training, eachcompetitive neuron is assigned to a class in the training data (e.g., trials with narrow- or wide-aperture stimuli).Weights are modified only for the unit that wins the competition in the competitive layer (i.e., the unit whose outputis 1). If the winning unit corresponds to the correct class for the training trial, then its input weights are nudgedcloser to the input on that iteration. If, on the other hand, the winning unit corresponds to the incorrect class for thattrial, the input weights are pushed a small distance away from the inputs for that trial, so that similar inputs will beless likely to activate that node in the future. The weights remain unchanged for the competitive units that output 0.Mark Laubach modified the built-in Matlab Toolbox code so as to implement the optimized LVQ (OLVQ1) learningrule (Kohonen, 1997). This learning rule reduces the learning rate during each iteration of the algorithm so thatweight changes are smaller for each iteration.

The second, output, layer of the network typically consists of the same number of units as there are classes ofinputs to be estimated. For instance, with the narrow-wide discrimination there are two output units. The outputlayer is simply a reporter of which class won the competition at the competitive layer. Quantitatively, all thecompetitive units preassigned to the same class have a weight of 1 onto the corresponding class output unit, and 0to the other output units. Hence, if any narrow-class competitive unit wins the competition, the correspondingoutput unit will return a 1, and the other output units will return a 0.

When training is complete the test trial data are input to the network, and the networks output classifies the test trialon the basis of regularities in the training data set. We use all but one trial as the training set, and testing thenetwork on a single hold-out trial. By taking each trial in turn as the hold-out and training the network on theremaining set, one tests the network using every trial (instead of only on some arbitrarily chosen training subset).This procedure is known as leave-one-out cross validation. The results of an analysis are quantified in terms ofpercentage of single trials classified correctly.

For example, we applied LVQ to study information about actively sampled stimuli coded in ensemble activityrecorded from different cortical layers of S1 as rats discriminated a wide from a narrow aperture using their facialvibrissae. Single-trial peri-event histograms (50 ms bins) were constructed for the epoch starting 200 ms beforeuntil 500 ms after the time when the rats sampled the tactile stimuli. Leave-one-out cross-validation was used asoutlined earlier. The training data were used at each iteration to initialize the LVQ networks by setting thecoefficients for each competitive neuron dedicated to a given type of trial equal to the mean neuronal ensembleresponse for that type of trial (plus a small random noise term). In this case we used four competitive units: two foreach class to be represented, i.e., two for wide and two for narrow. Performance of the network for each analysiswas compared to a chance level of 50% correct. This analysis allowed us to compare the ability of ensembles atdifferent cortical layers to discriminate between the two aperture widths. We found a significant trend toward betterLVQ performance through the deeper cortical layers (r(20) = 0.60, p < 0.01). By manipulating the single-trial data toselectively omit excitatory or inhibitory modulations of neural firing rate, we were also able to compare stimulusdiscrimination for these different response types. Because inhibitory modulations are a rare form of response topassive stimulation (Sachdev et al., 2000; Krupa et al., 2004), it was interesting to find that widespread inhibitorymodulations during discrimination behavior carried significant information about the actively sampled stimulus. Incontrast, previous studies that examined potential coding mechanisms in S1 following passive whisker stimulationsuggest that tactile information is encoded mainly by excitatory S1 activity (Ghazanfar et al., 2000; Petersen andDiamond, 2000; Arabzadeh et al., 2003).

We also used a moving window analysis to assess the time course of information about the wide and narrowapertures. LVQ was applied sequentially to a 500 ms epoch of neuronal ensemble activity that moved in 100 mssteps through the 1 s epochs before and after the time of whisker contact. An example of this analysis is shown in

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Figure 6.6. This analysis produced a continuous quantitative readout of the recorded populations ability todistinguish between the wide and narrow apertures. The time course of LVQ performance around the time ofwhisker contact can be used to verify that the animals brain does not have access to task-related cues beforesampling the discriminandum, and to show how task-related information rises and falls after the stimulus. Ininterpreting the LVQ performance time course, it is important to note that stimulus information reflected in the LVQperformance need not be directly related to the stimulus. For example, in the aperture discrimination task in whichthe animal is required to pass its face through the discriminandum to trigger a central nose poke beyond theaperture, there is a period after contacting the aperture until triggering the center nose poke during which theanimals motor behavior is statistically identical in wide and narrow trials. During that period, stimulus-relatedinformation in S1 is most likely a direct sensory image of the stimulus. After that period (~300500 ms), the animalbegins to move to the left or rightand as soon as it does, the stimulus-related information quantified in S1 usingthe LVQ may be contaminated with movement-related activity.

A study by Ghazanfar et al. (2000) illustrates other data manipulations that can be used to investigate the role ofspecific coding mechanisms in a given neural ensemble data set. The experimenters stimulated individual whiskersin anesthetized rats while recording ensemble activity in layer V S1 and VPM. They applied LVQ to quantifyensemble performance at discriminating between four different whiskers (B1, B4, E1, and E4; chance performance= 25%). To ask whether stimulus location information was local or distributed, they applied a neuron-droppinganalysis. The procedure is to sequentially remove the best predictor neuron from the data set and recalculate LVQperformance to calculate a performance curve as a function of the number of remaining neurons in the ensemble.The best predictor neuron is determined by running the analysis with each neuron left out in turn. The neuronwhose omission had the most detrimental effect on performance is identified as the best predictor. A smoothdegradation in performance as neurons were dropped suggested the coding of the location of single-whisker stimuliin layer V is distributed rather than local to a single barrel.

To determine whether the temporal modulation of neural firing rates contributed to the ensemble information aboutstimulus location, Ghazanfar et al. varied the bin size of the single-trial poststimulus time histograms that define theinput data for the LVQ analysis. The bin size was varied from 1 to 40 ms. Because increasing the bin size degradesthe temporal resolution of the recorded response, a decrease in LVQ discrimination performance upon increasingthe bin size shows that temporal patterning of the ensemble response contributed to the ensemble informationabout stimulus location.

Finally, one can manipulate the data to perturb relations among different neurons, to test whether coordinationacross neurons contributes to the ensemble stimulus information. (Ghazanfar et al. actually used linear discriminantanalysis (LDA) rather than LVQ for neural activity pattern classification in this part of their analysis.) At least twomethods can be distinguished for this disruption of across-neuron coordination. Randomizing the trial numberseparately for each neuron (known as trial-shuffling) preserves single-trial latency information, but disrupts anyacross-neuron coordination that varies from trial to trial. To the extent that neural responses are consistentlystimulus-locked, on the other hand, their covariance can be preserved after trial shuffling. Alternatively, one canintroduce random jitter into the entire spike-train of each neuron individually, disrupting both single-neuron latencyinformation and across-neuron correlations, without affecting single-neuron temporal patterns. Latency jitterreduced the ensemble stimulus information, but trial-shuffling did not, supporting the importance of temporal codingin terms of relative latencies, but not across-neuron coordination, in the context of single-whisker stimuli.

Another study, on location coding in primates (Nicolelis et al., 1998), used the same LVQ approach to examinedifferent coding strategies used by ensembles in different areas to carry information about the location of a tactilestimulus. Again, it was found that S1 ensembles code single locations primarily by a distributed stimulus-lockedlatency code. In contrast, however, trial-shuffling showed that S2 ensembles carry location information in thetemporal pattern of activity distributed across many neurons that varies from trial to trial.

CONCLUSION AND OUTLOOKMultielectrode, behavioral, pharmacological, and quantitative methods have evolved to the point where we canfruitfully address questions about somatosensory coding by neuronal ensembles in the awake behaving rat.Applying these methods has already shown that principles of somatosensory coding depend strongly on thebehavioral state and context the rat finds itself in at each moment.

Technical improvements in three directions will facilitate the discovery of the neuronal underpinnings ofsomatosensory behavior. First, more fine-grained circuit analysis techniques will reveal specific cell-types, theirprecise locations, geometry, and channel distributions; leading to refinements of biologically realistic models of the

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specific transformations being carried out by the somatosensory system. Markram (2006) discusses a researchprogram along these lines.

Second, techniques to evoke neuronal activity in precise spatiotemporal patterns will shed light on the effects ofsuch perturbations on downstream networks and behavior. Boyden et al. (2005) discusses recent advances in suchtechniques.

Third, as the focus of somatosensory electrophysiology shifts to questions of somatosensory function in awakebehaving animals, behavioral methods will assume increasing importance. For example, advances in whiskersensory physiology will likely follow improvements in our ability to monitor whisker movements using opto-electronicwhisker monitoring (Bermejo et al., 1998), video analysis (Knutsen et al., 2005), and physical modeling of whiskerdynamics (Hartmann et al., 2003). With these advances, the whisker system will continue to provide fertile groundfor understanding how rats use tactile sensation to pursue their constantly varying goals.

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Figures

FIGURE 6.1

(See color insert following page 140.) Multiple individual whisker stimulator for (A) anesthetized and (B) head-restrained awake rats.

FIGURE 6.3

Whisker discrimination module. (A) Photo of the module showing center nosepoke and seven adjustable whiskerdiscriminandum bars in the fully retracted position. (Krupa, D.J, Brisben, A.J., and Nicolelis, M.A.L. (2001a) A multi-channel whisker stimulator for producing spatiotemporally complex tactile stimuli. J Neurosci Methods 104: 199208.) (B) Schematic of single-pair whisker discriminandum bars showing placement of infrared beams triggered bythe rats head passing between the bars (horizontal beams) and by whisker contact with a bar (vertical beams).(Wiest, M.C., Bentley, N., and Nicolelis, M.A. (2005) Heterogeneous integration of bilateral whisker signals byneurons in primary somatosensory cortex of awake rats. J Neurophysiol 93: 29662973.)

FIGURE 6.2

Whisker-dependent aperture width discrimination task. (A) Schematic of the behavioral task. A rat begins in themain chamber, and enters the rear whisker discrimination chamber when the sliding center door opens. The ratsamples the variable width aperture with its whiskers and retreats to the left for reward if the aperture was narrow(60 mm) and to the right if the aperture was wide (68 mm). (B) Video frame capture showing a rat sampling anarrow aperture.

FIGURE 6.4

(see facing page) Muscimol time course and spatial spread. (A) Control experiments demonstrate the stability ofour neural ensemble recordings during cortical inactivation with muscimol (150 ng dose). Each row showspoststimulus time histograms (PSTHs) from a neuron on a different electrode, at different postinfusion times. In thiscase inactivation lasted about 9 h before preinfusion responses reappeared. (B) Ensemble recordings are also usedto map the spread of muscimol inactivation in the S1 cortex. Each column shows PSTHs from a different electrode,spaced 250 microns from its neighbors. Inactivation spread less than a millimeter for the lower dose (50 ng), butmore than a millimeter for the higher dose (150 ng; adapted from Krupa, D.J., Ghazanfar, A.A., and Nicolelis, M.A.(1999) Immediate thalamic sensory plasticity depends on corticothalamic feedback. Proc Natl Acad Sci U.S. 96:82008205. With permission). (Krupa, D.J., Ghazanfar, A.A., and Nicolelis, M.A. (1999) Immediate thalamicsensory plasticity depends on corticothalamic feedback. Proc Natl Acad Sci U.S. A. 96: 82008205.)

FIGURE 6.5

Summary diagram showing salient response properties of single neurons in different layers of S1 (Krupa, D.J.,Wiest, M.C., Shuler, M.G., Laubach, M., and Nicolelis, M.A. (2004) Layer-specific somatosensory cortical activationduring active tactile discrimination. Science 304: 19891992. With permission). The vertical axis measuresrecording depth in millimeters. Early-on: Percentage of units showing early-onset responses that begin beforewhisker stimulation; LVQ: Mean (+SEM) wide versus narrow categorization performance of an artificial neuralnetwork using population neural activities recorded at the different depths. Duration: Mean (+SEM) excitatoryresponse duration. Magnitude: Mean (+SEM) magnitude of excitatory responses. MPI: Distribution of units withmulti