Dynamics of excitatory and inhibitory networks are differentially … · 2018-11-11 · Dynamics of excitatory and inhibitory networks are differentially altered by selective attention

Dynamics of excitatory and inhibitory networks are differentially altered byselective attention

Adam C. Snyder,1,2,5 Michael J. Morais,1,3 and X Matthew A. Smith1,2,3,4

1Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania; 2Center for the Neural Basis ofCognition, University of Pittsburgh, Pittsburgh, Pennsylvania; 3Department of Bioengineering, University of Pittsburgh,Pittsburgh, Pennsylvania; 4Fox Center for Vision Restoration, University of Pittsburgh, Pittsburgh, Pennsylvania;and 5Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania

Submitted 2 May 2016; accepted in final form 20 July 2016

Snyder AC, Morais MJ, Smith MA. Dynamics of excitatory andinhibitory networks are differentially altered by selective attention. JNeurophysiol 116: 1807–1820, 2016. First published July 27, 2016;doi:10.1152/jn.00343.2016.—Inhibition and excitation form two fun-damental modes of neuronal interaction, yet we understand relativelylittle about their distinct roles in service of perceptual and cognitiveprocesses. We developed a multidimensional waveform analysis toidentify fast-spiking (putative inhibitory) and regular-spiking (puta-tive excitatory) neurons in vivo and used this method to analyze howattention affects these two cell classes in visual area V4 of theextrastriate cortex of rhesus macaques. We found that putative inhib-itory neurons had both greater increases in firing rate and decreases incorrelated variability with attention compared with putative excitatoryneurons. Moreover, the time course of attention effects for putativeinhibitory neurons more closely tracked the temporal statistics oftarget probability in our task. Finally, the session-to-session variabil-ity in a behavioral measure of attention covaried with the magnitudeof this effect. Together, these results suggest that selective targeting ofinhibitory neurons and networks is a critical mechanism for atten-tional modulation.

attention; correlated variability; inhibition; V4; vision

NEW & NOTEWORTHY

Excitation and inhibition interact to imbue neuronal net-works with the computational power that enables cognitiveprocesses, such as attention, yet the details of this interac-tion remain unknown. We developed a means to identifyneuronal subtypes in population recordings and leveragedthis to study their functional interactions during attention.We found evidence that selective modulation of inhibitorysubnetworks, resulting in a reduction of functional inter-actions, was a key feature of the attentional state.

ATTENDING TO A REGION OF visual space improves the speed andreliability of our perception at that location. Rigorous quanti-fication of these behavioral improvements due to attention hasbeen documented for nearly 150 years (Helmholtz 1867; Pos-ner et al. 1980; Treisman 1969). In the last 30 years, themechanisms of attention at the neural level have begun toemerge: neurons at the attended location increase their firingrate (Moran and Desimone 1985), whereas at the same time,decrease their trial-to-trial fluctuations in response strength orcorrelated variability (Cohen and Maunsell 2009; Mitchell et

al. 2009). Whereas both of these changes can improve theability of downstream neurons to decode signals from thepopulation of attended neurons (Averbeck et al. 2006; Cohenand Maunsell 2009), the means by which a neural networkinstantiates these changes remain poorly understood. The sim-plest mechanism for selective attention—an excitatory feed-back signal from brain areas implicated in executive controlprocesses—fits well with observed firing-rate increases at theattended location. However, such an elevation in commoninput is seemingly inconsistent with a decrease in correlatedvariability.

Correlated variability in pairs of cortical neurons has beenused both to infer functional connectivity in neural networks(Alonso and Martinez 1998; Moore et al. 1970; Nauhaus et al.2009; Smith et al. 2013; Smith and Kohn 2008; Smith andSommer 2013; Snyder et al. 2014) and also to constrain modelsof cortical coding (Doiron et al. 2016). The structure ofcorrelated variability has a direct impact on information en-coding by a neuronal population, although this impact dependsintimately on the structure of the correlations: their spatialextent, dependence on tuning similarity, and most importantly,the way in which neuronal activity is decoded (Kohn et al.2016). However, whereas the balanced network models that areoften used to simulate neuronal populations do so by usingexcitatory and inhibitory neurons (Litwin-Kumar and Doiron2012; Renart et al. 2010; van Vreeswijk and Sompolinsky1996), the physiological data with which they are compared aretypically agnostic to cell type. In the context of attention, thestudy of correlated variability provides an opportunity to illu-minate both the computational principles of a cognitive processand the circuit-level mechanisms by which it is instantiated.This, in turn, is key evidence that constrains future models ofattentive processes in neural networks.

Targeted pharmacological and optogenetic investigationshave elucidated some of the likely mechanisms of attention andhighlighted the role of different cell classes and neurotransmit-ter systems. Excitatory N-methyl-D-aspartate receptors appearto be necessary participants in attention-induced changes invariability and covariability but not mean firing rate (Herrero etal. 2013), whereas acetylcholine administration potentiatesfiring-rate modulation, and acetylcholine antagonism (via sco-polamine) reduces it (Herrero et al. 2008). Moreover, this effectof acetylcholine may be mediated primarily by GABAergic neurons,which express acetylcholine receptors more than excitatoryneurons (Disney et al. 2006) and are suppressed by acetylcho-line receptor agonists (Disney et al. 2012). The role of

Address for reprint requests and other correspondence: M. A. Smith, Dept.of Ophthalmology, Univ. of Pittsburgh, Eye and Ear Institute, 203 Lothrop St.,9th Fl., Pittsburgh, PA 15213 (e-mail: [email protected]).

J Neurophysiol 116: 1807–1820, 2016.First published July 27, 2016; doi:10.1152/jn.00343.2016.

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GABAergic neurotransmission in correlated variability on afast time scale (synchrony) has been studied extensively (Buz-saki and Wang 2012; Diba et al. 2014; Ledoux and Brunel2011), including in the context of attention (Tiesinga et al.2004). Glutamatergic long-range connections between corticalareas can also recruit local inhibition, which appears to play arole in modulatory effects related to attention (Zhang et al.2014). Taken together, these studies point to the importance ofunderstanding attention via the interplay between excitatoryand inhibitory forces.

Whereas it has recently become clear that many of thecomputational properties of the brain emerge through thedelicate organization of excitatory and inhibitory cell classes(Isaacson and Scanziani 2011), a network-level understandingof how attention can simultaneously produce increases inresponsiveness and decreases in covariability remains elusive.A potential path to resolve this discrepancy lies in recentaccounts of model networks in which balanced, recurrentexcitation and inhibition act to cancel correlations (Renart et al.2010; van Vreeswijk and Sompolinsky 1996) and in the phys-iological findings in which the effect of attention on responsegain is stronger for putative inhibitory neurons than for othercell classes (Mitchell et al. 2007), and attention differentiallyaffects the synchronization of putative inhibitory and excit-atory neurons with ongoing network oscillations (Vinck et al.2013). We reasoned that if attention signals target inhibitoryneurons more strongly, as suggested by the anatomical andfunctional observations to date, then the decorrelating effect ofattention might also be strongest in populations of inhibitoryneurons. The testing of this hypothesis led us to develop anovel maximum likelihood estimation method using multiplemeasures of waveform shape to classify neurons as inhibitoryor excitatory—an improvement on previous attempts largelyfocused on a single waveform shape measure [Anderson et al.(2013); Mitchell et al. (2007), but see Peyrache et al. (2012);Qi and Constantinidis (2012); and Shin and Sommer (2012)].With the use of a spatial-selective attention task, we report thatthe magnitude and time course of decorrelation were strongestamong inhibitory neurons and matched to behavioral measuresof attention. These findings are bolstered by a recent theoreticalstudy reporting that selective modulation of inhibitory net-works is the key to producing the concomitant increases inresponsiveness and decreases in variability observed with at-tention (Wiese et al. 2014). It thus appears that a singlemechanism—selective top-down targeting of inhibitory net-works—may mediate two effects of attention that at first blush,appear irreconcilable.

MATERIALS AND METHODS

Experimental procedures were approved by the Institutional Ani-mal Care and Use Committee of the University of Pittsburgh. Aseparate analysis of a portion of these data was previously reported(Snyder et al. 2015).

Subjects. We implanted a 100-electrode “Utah” Array (BlackrockMicrosystems, Salt Lake City, UT) in extrastriate cortex visual areaV4 in each of two adult, male rhesus macaques (Macaca mulatta). Weimplanted in right V4 for Monkey B and in left V4 for Monkey R.Surgeries were performed in aseptic conditions under isofluraneanesthesia. In a separate procedure before the array implants, atitanium headpost was attached to the skull with titanium screws toimmobilize the head during experiments. After each subject was

trained to perform the spatial attention task, we implanted an electrodearray in V4 using the same procedures that we have describedpreviously (Smith and Sommer 2013). The main physiological find-ings were individually apparent and qualitatively similar in the resultsfrom each monkey, so we collapsed across the data from the twomonkeys except where shown.

Microelectrode array recordings. Signals from the arrays wereband-pass filtered (0.3–7,500 Hz), digitized at 30 kHz, and amplifiedby a Grapevine system (Ripple, Salt Lake City, UT). Signals crossinga threshold (periodically adjusted using a multiple of the root-mean-squared noise) were stored for offline analysis. Each waveformsegment consisted of 52 samples (1.73 ms). We first performed asemisupervised sorting procedure to separate putative action-potentialwaveforms from noise, using principle components analysis and anautomatic Fuzzy c-Means clustering algorithm (Shoham et al. 2003),followed by manual refinement using custom MATLAB software(MathWorks, Natick, MA) (Kelly et al. 2007), taking into accountwaveform shapes and interspike interval distributions. These initialsorting steps yielded 103.8 � 2.5 (means � SE) candidate units persession for Monkey B and 80.6 � 1.7 candidate units per session forMonkey R. To focus our analysis better on well-isolated individualneurons (as opposed to multiunit activity), we calculated the signal-to-noise (SNR) ratio of each candidate unit as the ratio of the averagewaveform amplitude to the SD of the waveform noise (Kelly et al.2007) and excluded units with SNR values �2.5 (9.9%). We alsoexcluded units with waveform shapes that were, on average, �40%correlated with the waveforms of the rest of the population (0.4%).This yielded a total of 6,962 units for the analysis. The arrays werechronically implanted and likely recorded some individual neuronsover more than one recording session, but we treated each recordingsession as an independent sample for the analysis. Following spikesorting, we used a separate automated classification procedure (de-scribed below) to identify fast-spiking (putative inhibitory) and reg-ular-spiking (putative excitatory) cells.

Receptive-field mapping and tuning curves. Before beginning theexperiments, we mapped the receptive fields (RFs) of the unitsrecorded on our arrays by presenting small (�1°) sinusoidal gratingsat four orientations, positioned one at a time on the vertices of a latticecovering the likely RF area per the anatomical location of the implant.We subsequently used a grating size and position to cover roughly theaggregate RF area. For Monkey B, this was 5.87° diameter centered4.00° below and 4.00° to the left of fixation, and for Monkey R, thiswas 5.87° diameter centered 8.94° below and 4.99° to the right offixation. We next measured tuning curves by presenting gratings to theRF area in four orientations and at a variety of spatial and temporalfrequencies. For each subject, we chose a temporal and spatialfrequency that evoked a robust response from the population overall(i.e., our stimulus was not optimized for any single neuron).

Attention experiment. We trained the subjects to maintain fixationon a 0.6° yellow dot at the center of a flat-screen cathode ray tubemonitor, positioned 36 cm from their eyes (Fig. 1). The background ofthe display was 50% gray. We measured monitor luminance gammafunctions by photometer and linearized the relationship between inputvoltage and output luminance using lookup tables. We tracked thegaze of the subjects using an infrared eye-tracking system (EyeLink1000; SR Research, Ottawa, Ontario, Canada). Gaze was monitoredonline by the experimental control software to ensure fixation within1.17° of the central fixation point throughout each trial.

After fixating for a randomly chosen duration of 300 or 700 ms(50% probability of each), a peripheral visual cue was presented for120 ms on 89% of trials (no-cue trials were identical in all otherrespects). For Monkey B, the cue was a 0.6° dim gray dot (87 cd/m2;9% contrast) centered on one of the two potential imperative stimuluslocations. For Monkey R, the cue was a yellow annulus (isoluminantwith the display background, 6.45° inner diameter, 7.03° outer diam-eter, masked to prevent crossing the vertical meridian) that encircledone of the two stimulus locations.

1808 NETWORK DYNAMICS IN ATTENTION

J Neurophysiol • doi:10.1152/jn.00343.2016 • www.jn.org


Following another randomly chosen duration of 300 or 700 ms(50% probability of each), two full-contrast, drifting sinusoidal grat-ings were presented, one at 0° orientation and the other at 90°orientation. Orientation was counterbalanced between stimulus loca-tions across trials. One grating was presented in the RF area of theneurons recorded on the array, and the other grating was presented inthe mirror-symmetric location in the opposite hemifield.

The task was to detect a speed change (acceleration or deceleration)of one of the two gratings and to saccade to the stimulus that changed(i.e., the “target”) within 800 ms of the change onset. Correct re-sponses were reinforced with juice or water. The speed change wasgoverned by a triangular ramp function (i.e., a gradual increase ordecrease followed by a return to baseline; Fig. 1C) that lasted 500 ms(except for 3 early sessions with Monkey B, where it was 650 ms). Wetitrated the maximum magnitude of the acceleration and decelerationbefore the experiment using a staircasing procedure to set an overallcorrect detection rate for each type of speed change between 70% and80% correct. Forty percent of trials had no target (catch trials), and theanimals were rewarded for maintaining fixation for the full stimulusduration—1.2 s for Monkey B or 1.5 s for Monkey R. If a cue andtarget both occurred, then the cue validly indicated the correct locationof the target with 80% probability. The range of target-onset timeswas 250–700 ms after grating onset for Monkey B or 250-1,000 ms forMonkey R. In this report, we show the data from time periods thatwere common to both subjects (i.e., up to 1.2 s poststimulus onset,

truncating the last 300 ms of Monkey R’s data). In total, Monkey Bcompleted 40 sessions, and Monkey R completed 42 sessions of theattention task.

Quantification of waveform shapes. Previous in vitro work hasshown that excitatory and inhibitory neurons have different action-potential dynamics (Bartho et al. 2004; Kawaguchi 1993; McCormicket al. 1985). Thus to classify our units as putative excitatory andinhibitory neurons, we used measures of action-potential waveformshape. We used the following three measures that have each beendemonstrated to differ between excitatory and inhibitory neurons (seeFig. 3A): 1) time to the peak of the positive phase (PP) of theextracellular action potential (Bartho et al. 2004; Kawaguchi 1993;McCormick et al. 1985), 2) duration of PP (Kawaguchi 1993; Mc-Cormick et al. 1985), and 3) relative rate of depolarizing and repo-larizing phases of the spike (McCormick et al. 1985). We found thatno single measure clearly dichotomized our sample (see RESULTS), sowe classified using the three measures in parallel. We also triedclassification with greater numbers of waveform measures beyondthese three, such as the time between successive zero crossings of thewaveforms (Bruno and Simons 2002) and principal component scores,but found no appreciable improvement in classification performance.For ease of visual comparison of waveform shapes in Fig. 2F, wenormalized waveforms to have a maximum deviation of �1 unit bydividing by the most extreme value. Note that because our measuresare time based (measures 1 and 2) or a ratio of two within-waveform

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Fig. 1. Behavioral task. A: task schematic. While the subject maintained central fixation, a peripheral visual cue indicated the spatial location that was more likelyto contain the target (80% validity). Subsequently, 2 drifting gratings appeared. The task was to detect a change in the drift speed in 1 of the 2 gratings and tomake an eye movement to this stimulus. On 40% of trials, neither stimulus changed speed, and the subject was rewarded for maintaining fixation. The dashedcircle and “spotlight” symbol represent the receptive-field (RF) area and focus of attention, respectively, and were not actually present in the stimulus display.For 1 subject, the cue was an isoluminant yellow annulus that encircled the aggregate RF area, whereas for the other subject (shown), the cue was a low-contrastdot at the center of the RF area. B: hit-rate performance. Error bars represent � 1 SE. For both subjects, target-detection performance was improved when thecue validly indicated the target location compared with when no cue was given, and performance was diminished when the cue was invalid [2-way ANOVA,interaction between factors of “cue direction” and target location; Monkey R: F(2, 264) � 10.28, P � 3.5 � 10�5; Monkey B: F(2, 222) � 211.77, P � 5.0 � 10�52].This pattern of results is the hallmark for spatial-selective attention. C: the hazard function for target (speed change) onset time (left) combines with the gradualnature of the target speed change (middle) to define an anticipation function (right). The anticipation function describes the expected amount of task-relevantinformation in the stimulus over the course of a trial. The anticipation function shown here corresponds to the timing of targets from a representative sessionfor Monkey B.

1809NETWORK DYNAMICS IN ATTENTION



measurements (measure 3), this amplitude normalization was onlyaesthetic and had no effect on classification outcome.

To improve our precision in estimating the time courses of action-potential waveforms, we modeled each waveform using parametricfunctions. The model parameters reflect the timing information ofinterest and can be estimated with arbitrary precision. Since eachwaveform is essentially two “peaks” of voltage—one negative andone positive—we first fit (in the least-squares sense) a sum of twoGaussian curves—one negative and one positive—to each unit’saverage waveform. We imposed the constraint that the negative(depolarizing) component must precede the positive (repolarizing)component. Through curve fitting, we reduced the waveform descrip-tion to a set of six parameters: the amplitude, time shift, and width ofthe first Gaussian component and the amplitude, time shift, and widthof the second Gaussian component. We took the value of the time-shift parameter for the second Gaussian component as the time to peakof the PP of the extracellular action potential (measure 1) and thevalue of the width parameter for the second Gaussian component asproportional to the duration of the PP (measure 2). We found that thetime of the PP peak relative to the initial threshold crossing was highlycorrelated to the time of the PP peak relative to the action-potentialtrough (i.e., trough-to-peak time)—a measure that had been used by

previous researchers to classify neuron types (Anderson et al. 2013;Mitchell et al. 2007; Peyrache et al. 2012; Qi and Constantinidis 2012;Shin and Sommer 2012)—but provided more reliable classificationperformance (the poorer classification performance for trough-to-peaktime may be due to combining two noisy timing measurements—onefor the trough and one for the peak—compared with just one noisymeasurement when using threshold-to-peak time). Thus we used thetime to the PP peak rather than trough-to-peak time to classify oursample. To compare the relative rate of depolarization and repolar-ization, we calculated the derivative of each normalized action-potential waveform and approximated the derivative waveform usingtwo Gaussian components, as we did for the voltage waveformsdescribed above. We took the difference of the absolute amplitudes ofthe two Gaussian components fit to the derivative wave as the contrastin rate between depolarizing and repolarizing phases (measure 3).Figure 2A illustrates the three measures of waveform shape, as well ashow the Gaussian components were fit to the waveforms. We alsofound qualitatively similar results using other measures of waveformshape—namely, the time between successive zero crossings (Brunoand Simons 2002) or principal component coefficients—but ulti-mately, used the particular measures reported here because they havebeen validated in vitro.

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1000 units10010

Fig. 2. Multidimensional waveform analysis. A: illustration of waveform shape measures. Top: we fit a sum of Gaussians to each neuron’s waveform to extractestimates of the relative amplitude, time, and duration of each phase of the spike; Norm. ampl., normalized amplitude; bottom: the derivative of the voltagewaveform (dV/dt) above, similarly fitted with a sum of Gaussians. To quantify waveform shape, we used the peak time of the positive phase (PP) of theextracellular action potential (Kawaguchi 1993; McCormick et al. 1985), duration of PP (Kawaguchi 1993; McCormick et al. 1985), and difference between peakdepolarization and repolarization rates (McCormick et al. 1985). We used the SD of a Gaussian fit to the PP (“PP�”) to quantify PP duration (see MATERIALS

AND METHODS). B–D: 2-dimensional, marginal distributions for our 3-dimensional waveform shape data. Each plot point aggregates observations from severalindividual neurons; the size of each plot point is proportional to the number of observations in its neighborhood, whereas the color of each plot point reflectsthe average observed probability of belonging to the fast-spiking cell class (putative inhibitory). The ellipses circumscribe 95% of the fitted probability densitiesfor the fast-spiking (red) and regular-spiking (blue; putative excitatory) component distributions. E: classification performance. Neurons are sorted by theprobability of being fast spiking (red) or regular spiking (blue). The dashed lines denote the zone where neurons have �85% probability of belonging to eitherclass (336 of 6,962 total). Classification performance was similar when performed separately for individual subjects. F: each waveform, colored according tothe probability that the neuron is fast spiking. G: classification performance for synthetic data. We generated surrogate data by modeling a Gaussian mixturedistribution identical to our fitted result but with the means of the 2 component distributions moved toward (relative centroid difference � 1) or away from eachother (relative centroid difference � 1). We next iteratively generated random observations from these surrogate distributions and classified them to test thesensitivity and reliability of our method. When component distributions were closer together, more ambiguous classification resulted, indicating that the Gaussianmixture method does not spuriously dichotomize the data. Conversely, the doubling of the distance between the component distributions does not lead to anappreciable improvement in classification, suggesting that the classification of the observed data was near-ceiling levels. Shading represents 90% bootstrapconfidence intervals (1,000 iterations). The dashed line represents the centroid separation that was actually observed for the dataset.




Classification of cell types. We used maximum likelihood estima-tion to classify neurons as putative excitatory or inhibitory on thebasis of our three waveform shape measures. Specifically, we as-sumed that each class of neurons forms a roughly Gaussian distribu-tion in the three-dimensional space describing waveform shapes (Fig.2, B–D). In the raw data, these two clouds overlap, and our goal is toestimate best the separate parameters of the two component distribu-tions. To achieve this, we used an expectation maximization algorithm(using the MATLAB “gmdistribution” class). For the expectationmaximization, we used only our best-isolated units: neurons with SNRvalues of at least 4.0 (n � 1,434 units; see Microelectrode arrayrecordings above). Because expectation maximization can be sensi-tive to locally optimized solutions, we repeated the analysis 10 timeswith random initial conditions and used the globally optimal solution.Note that the use of maximum likelihood estimation for classificationconfers the benefit (compared with other methods, such as lineardiscriminants or k-means clustering) of using higher moments ofwaveform shape distributions (in particular, the variances) to improveidentification of the two cell classes. Once we determined the maxi-mally likely parameters for the two distributions based on our best-isolated units, we used the fitted probability distributions to calculatethe posterior probability of belonging to either class for all of the units(Fig. 2, B–F). This ability to quantify probability of class membershipis another advantage of using maximum likelihood estimation toclassify neurons compared with linear discriminants or k-meansclustering. We considered units with �85% probability of belongingto either class ambiguous, and we excluded them from subsequentanalyses (336 of 6,962 units excluded, or 4.8%, resulting in a total of6,626 units classified as either fast spiking or regular spiking). Thequalitative pattern of our results was robust to changes in the specificvalues of these thresholds.

Our expectation maximization approach assumes that the datacome from two separate underlying distributions, and we wished toensure that this assumption would not lead to a spurious dichotomi-zation of the sample. To test this, we generated surrogate data thatwere similar to our observed data in every way except with respect tothe degree of bimodality in the mixture distribution, which we variedparametrically by moving the centroids of the component distributionstoward or away from each other when generating the surrogate data.At each centroid separation, we drew a number of observations fromthe modified mixture distribution equal to our sample size (n �6,962), submitted these surrogate data to the same expectation max-imization and classification procedure, and noted the proportion ofneurons classified as ambiguous (Fig. 2G). Finally, we iterated thisentire procedure 1,000 times to derive a distribution of classificationperformances at each centroid separation. We reasoned that if ourprocedure does not spuriously dichotomize unimodal samples, thenwe would observe the number of resultant ambiguous classificationsrapidly increase as the centroids of the component distributions weremoved closer to each other, which was precisely what we found (seeRESULTS).

Spike count correlation. To assay functional connectivity betweenpairs of neurons, we measured spike count correlation (rsc), whichdescribes the tendency for the responses of a pair of neurons toco-vary over repeated trials of an identical stimulus (Kohn and Smith2005; Smith and Kohn 2008). Since the stimulus is unchanging in thiscase, such covariation is not due to demands of stimulus processing,but rather reflects correlated trial-to-trial variability in the neuronscomprising the pair, perhaps due to the presence of common endog-enous input. Since we were interested in how excitatory and inhibitoryneurons were targeted by endogenous attention signals, we examinedrsc as a function of cell class.

We analyzed trials for which the subjects correctly identifiedvalidly cued targets, as attention was likely to be consistently de-ployed to the cued location for those trials. Since rsc assumes repeatedidentical trials, we calculated rsc at each time point using the data onlyfrom trials in which the grating had not yet started to change speed (at

which point, trials were no longer identical). For each unit, wesummed the spike count in bins 150 ms wide, sliding by 5 ms steps,following grating onset on each trial. In the data figures, we plot rsc atthe right edge of the corresponding time bin so that each point in thetime series is influenced only by past events. Since two orientations ofgrating were used, we first z-scored each unit’s spike counts sepa-rately for each orientation so that orientation-tuning preferenceswould not confound our measurements of rsc. Then, we calculated rsc

as the Pearson product-moment correlation of spike counts acrosstrials for each pair of units (excluding pairs recorded on the samechannel, which could be affected by the spike-sorting process). Wecalculated rsc separately for trials following a cue near the RF area ofthe array (“attended” trials) and for trials following a cue contralateralto the RF area of the array (“unattended” trials). We treated eachrecording session as a separate sample of neurons and calculated rsc

only between neurons that were recorded simultaneously within asingle session.

To measure the dynamic change in rsc over the course of an entiretrial, we also analyzed data from trials in which neither stimuluschanged (“catch” trials), and the subjects correctly withheld re-sponses. Because catch trials occurred unpredictably, the subjectswould have deployed attention identically as on target trials, and sincethe gratings drifted at a fixed rate on these trials, we could calculatersc for the entire duration of the stimulus, using sliding, 150 mswindows, as described above.

Statistical analysis. We calculated the effect of attention as thedifference between conditions (attended minus unattended) in firingrate or rsc for each individual neuron or neuron pair, respectively.Neurons with an average firing rate across conditions of �1 spike(sp)/s were excluded (327/6,626 units, or 4.9%); this resulted in 4,958regular-spiking neurons (79% of the remaining sample) and 1,341fast-spiking neurons (21%) in the analyses. This sample yielded48,347 (60%) simultaneously recorded, regular-spiking pairs; 4,431(6%) simultaneously recorded, fast-spiking pairs; and 27,651 (34%)“mixed” pairs, comprised of one neuron from each class. Statisticalhypothesis testing for significantly nonzero attention effects wasperformed using two-tailed, one-sample t-tests at each time point,corrected for multiple comparisons. Testing for differences in atten-tion effects between cell classes was performed using two-tailed,independent-samples t-tests. For each test, we used a significancecriterion of � � 0.05. We corrected for multiple comparisons byrequiring a minimum run length of consecutive, significant timepoints. This correction relies on the fact that increasing numbers ofconsecutive significant t-scores become increasingly unlikely underthe null hypothesis. The run-length threshold was determined by usingthe autocorrelation of the signals (since adjacent samples are notentirely independent) to determine the minimum run length, such thatthe false discovery rate for finding a single effect over all time pointsunder the null hypothesis is the desired 5% (Altschuler et al. 2012;Guthrie and Buchwald 1991), which in our case, we found to be ninesamples. For the purposes of hypothesis testing, Fisher’s r-to-z trans-formation was first applied to rsc values to normalize the samplingdistributions. Furthermore, spike trains of individual neurons wereconvolved with Gaussian functions (20 ms full width at half maxi-mum) before hypothesis testing and determination of minimum runlength for significance of firing-rate effects.

RESULTS

Behavioral task performance. We trained two rhesus ma-caque monkeys (Monkey B and Monkey R) to detect gradualchanges in the drift speed of sinusoidal gratings in a cuedspatial attention paradigm (Fig. 1A). Most of the task trialswere preceded by visual cues that indicated the location of thegrating that would change with 80% validity. Cues that validlyindicated the location of a subsequent target led to a perfor-




mance improvement, whereas invalid cues led to a performancedetriment (Fig. 1B), verifying that the animals selectively attendedduring this task [two-way ANOVA, interaction between factors of“cue direction” and “target location”; Monkey R: F(2, 264) �10.28, P � 3.5 � 10�5; Monkey B: F(2, 222) � 211.77, P �5.0 � 10�52; Fig. 1B]. We observed this effect not only forhit rate, but also for the discriminability index [Monkey R:F(2, 264) � 87.76, P � 6.0 � 10�30; Monkey B: F(2, 209) �58.22, P � 7.97 � 10�21]. For Monkey B, 4 of 40 sessionswere excluded from the discriminability index analysis,because information on the target chosen in false alarms wasnot saved. This pattern of benefits for valid information andcosts for invalid information is the hallmark of selectiveattention.

Cell-type classification. While the animals performed thetask, we recorded their neuronal population activity using100-electrode Utah arrays implanted in visual area V4. Ourgoal was to identify subclasses of neurons on the basis ofwaveform shape and to test for evidence that one of these twoneuronal populations is preferentially targeted by top-downattention signals. A number of in vitro studies of neuronsidentified various action-potential features that are character-istic of inhibitory neurons, such as relatively short latencies topeak depolarization (Bartho et al. 2004; Kawaguchi 1993;McCormick et al. 1985), brief hyperpolarization durations(Kawaguchi 1993; McCormick et al. 1985), and rapid peakrates of repolarization (McCormick et al. 1985). A neuron withone or more of these action-potential features is said to exhibit“fast spiking” (or “narrow spiking”) compared with “regular

spiking” (or “broad spiking”), and it is this distinction betweenfast spiking and regular spiking that we used to classifyneurons as putatively inhibitory or excitatory, respectively(Fig. 2). Although this heuristic is not perfect—a minority ofinhibitory neurons has relatively slow spikes (Kvitsiani et al.2013; Nowak et al. 2003)—cell-type classification based onwaveform shapes has become a popular approach for tappinginto the roles of excitation and inhibition in the cortex (An-derson et al. 2013; Mitchell et al. 2007; Peyrache et al. 2012;Qi and Constantinidis 2012; Shin and Sommer 2012). How-ever, unlike previous researchers that used single electrodes(Anderson et al. 2013; Mitchell et al. 2007; Qi and Constan-tinidis 2012; Shin and Sommer 2012), we did not find that oursample was clearly dichotomized into separate classes by anysingle waveform shape measure (Fig. 3). In fact, for our data,we did not find the distribution of the most commonly usedmeasure (trough-to-peak time) to be at all bimodal, accordingto Hartigan’s Dip statistic (dip � 0.0028; P � 0.997; bootstraptest with 2,000 iterations) (Hartigan and Hartigan 1985). Wedid find, however, that combining three waveform shape mea-sures (Fig. 2A) led to distinct clusters in the higher-dimensionalspace (Fig. 2, B–D) corresponding to two classes of waveformshape. Because we were interested in firing rate as a dependentvariable of attention and wished to avoid introducing artificialeffects, we intentionally did not use firing rate to aid inclassification, despite that fast-spiking neurons are known tohave faster firing rates than regular-spiking neurons (Connorsand Gutnick 1990; Nowak et al. 2003) (see also Fig. 6Abelow).

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Fig. 3. Classification performance with single measures of waveform shape. A–C: neurons are sorted by the probability of being fast spiking (red) or regularspiking (blue). The dashed lines denote the zones where neurons have �85% probability of belonging to either class. A: with the use of only the time to the peakof the positive phase (PP) of the extracellular action potential, 19.3% of neurons were ambiguous. B: with the use of only the width of the PP, 81.4% wereambiguous. C: with the use of only the fall/rise contrast, 18.9% were ambiguous. For comparison, when all 3 measures were used together to classify neurons,only 4.8% were ambiguous (see description of Fig. 2E in main text). D–F: normalized histograms of waveform shape measures for the neurons in our sampleoverlaid with the fitted Gaussian mixture distribution (dashed black lines). The 2 components comprising each mixture are plotted in red (putative fast-spikingclass) and blue (putative regular-spiking class).




The estimation of the probability that each neuron belongedto each class enabled us to quantify classification performance(Fig. 2E) and to exclude neurons with ambiguous waveforms(Fig. 2F). Moreover, we are confident that our approach did notspuriously dichotomize the sample: when we repeated ourprocedure using simulated data drawn from component distri-butions that were highly overlapping, many ambiguous classi-fications resulted (Fig. 2G). Since very few ambiguous classi-fications were observed from the real sample (4.8%; Fig. 2E),this suggests the dichotomization was valid.

One potential concern is that the preamplifier filter settingson our recording equipment (7.5 kHz high cutoff) might have“smeared” the waveforms in time, making the fast-spikingneurons appear to have slower waveform time courses, therebymaking it more difficult to discriminate them from regular-spiking neurons. We checked for this possibility in two waysand determined that filter settings did not limit our ability toquantify waveform shape accurately (Fig. 4). First, we ob-tained recordings of action potentials recorded using equip-ment with a higher-frequency filter cutoff (10 kHz) in theprimary visual cortex area (V1; see Fig. 4 legend for details).We found that those action potentials were representative ofthe waveforms that we recorded for our experiment (Fig. 4A)and that the 10-kHz-filtered waveforms did not have meaning-ful spectral content above our 7.5 kHz cutoff (Fig. 4B). Second,we filtered the data that we recorded from the experiment atsuccessively lower cutoff frequencies to determine the range inwhich filter settings might impact our classification (Fig. 4C).We found that classification remained �95% correlated withour original analysis when the data were low-pass filtered aslow as 4 kHz, indicating that a 7.5-kHz cutoff is more thanadequate to measure the waveform shapes accurately.

Spatial structure of rsc. In previous work in both V1 (Smithand Kohn 2008) and V4 (Smith and Sommer 2013), we foundthat the magnitude of correlated variability decreased withincreasing distance. To compare our results with these previousstudies and to investigate any differences based on cell classusing our classification method, we performed the same anal-

ysis on pairs of neurons that were fast spiking and regularspiking and of mixed-class membership (Fig. 5). We analyzedonly catch trials with correctly withheld responses, averagingover all time bins and conditions. We found that each of thesethree neuron-pair types showed a trend of decreasing rsc withincreasing distance (Pearson product-moment correlation, fastspiking: r � �0.080, P � 2.4 � 10�7; regular spiking: r ��0.065, P � 2.4 � 10�45; mixed pairs: r � �0.047, P �1.2 � 10�14). These trends were qualitatively similar to pre-vious findings in V4 (Smith and Sommer 2013), although amore direct, quantitative comparison is difficult because of taskdifferences and the period over which spike counts weremeasured. We tested for main effects and interactions ofdistance and cell class on rsc using a two-way ANOVA withfactors of cell class (2 levels: fast spiking and regular spiking)and distance (10 levels of distance bins; Fig. 5). We found asignificant main effect of cell class, with greater correlation

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Fig. 4. Effects of low-pass filtering on waveform classification. A: two units recorded with a 10-kHz, low-pass preamplifier filter from macaque visual area V1(in contrast to the 7.5-kHz filter that was used for our study) are shown in bold (data courtesy of Dr. Adam Kohn, Albert Einstein College of Medicine, Bronx,NY). Plotted in lighter shades are 50 example waveforms chosen at random from each class from our manuscript (light red: fast spiking; light blue: regularspiking). Note that the spikes recorded with the 10-kHz filter are representative of the spikes that we recorded with the 7.5-kHz filter. B: frequency-domainrepresentation of the waveforms in A. The dashed line shows the 7.5-kHz cutoff. Note that there is little power above this range regardless of which preamp filterwas used, showing that there was virtually no power in the signal at these frequencies. The 2 conditions do not have different spectral content from each otherat frequencies above �4 kHz. C: we filtered our original waveforms at various frequencies and repeated our classification analysis. Then we measured howcorrelated the classifications on the filtered waveforms were with our original reported classification at 7.5 kHz. Again, the classification was unaffected byfiltering down to �4 kHz, suggesting that information above this frequency range is not useful for distinguishing the 2 waveform classes. Insets: averagewaveforms for each class at the different cutoff frequencies. SEs are smaller than the line thickness.

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Fig. 5. Spike count correlation diminishes over distance. Pairs were binnedbased on the distance between recording sites. There was a significant maineffect of cell class, with greater correlation among fast-spiking neurons thanregular-spiking neurons [F(4, 52789) � 136.35, P � � 0.001] and a main effectof distance [F(9, 52789) � 20.21, P � � 0.001], but the interaction of cell classand distance was not significant [F(9, 52789) � 1.71, P � 0.08]. Shadingrepresents � 1 SE.




among fast-spiking neurons than regular-spiking neurons[F(4, 52789) � 136.35, P � � 0.001], and a main effect ofdistance [F(9, 52789) � 20.21, P � � 0.001], but the interactionof cell class and distance was not significant [F(9, 52789) � 1.71,P � 0.08].

Effect of attention on firing rates. We next consideredclassical effects of attention on firing rates for the two cellclasses that we identified. When we examined the firing-rateresponses of the neurons to grating stimuli as a function ofwhich stimulus was cued, we found several effects of attention.In this and the following section, we discuss together theresults of trials in which targets were correctly detected (Fig. 6)and catch trials in which a cue was presented but a target nevershown (Fig. 7). Both fast-spiking and regular-spiking neuronsexhibited a higher firing rate in trials in which the cue wasdirected toward their RFs (attended) compared with trials inwhich the cue was directed away (unattended). We found this

to be true in analyses of trials in which targets were correctlydetected (Fig. 6A) and also during catch trials in which a cuewas presented, but the animal correctly remained fixatingbecause the target was never shown (Fig. 7A). A pairwisecomparison in which we computed the differential response foreach neuron between the attended and unattended trials madethis effect more apparent [Fig. 7C; regular-spiking firing-ratedifference (FR) � 0.26 sp/s, t4957 � 16.29, P � 3.5 � 10�58;fast-spiking FR � 0.53 sp/s, t1340 � 11.92, P � 3.2 � 10�31;analyzed in the window from 700 to 1,000 ms after gratingonset on catch trials]. There were three notable trends in ouranalysis of firing rate. First, the magnitude of the effect ofattention on firing rates was significantly greater for thefast-spiking class of cells than for the regular-spiking classof cells (Figs. 6C and 7C), consistent with a prior report(Mitchell et al. 2007). This pattern of greater attentioneffects for fast-spiking cells was true when we considered

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Fig. 6. Attention results from target-present trials. Shading represents � 1 SE. A and B: firing rate (Hz; A) and spike count correlation (rsc; B) for fast-spiking(red; n � 1,341 neurons; 4,431 pairs) and regular-spiking (blue; n � 4,958 neurons; 48,347 pairs) cells when the stimulus in the receptive field was attended(solid lines) and when the stimulus in the receptive field was unattended (dashed lines). Mixed-class pairs are plotted in gray, and pairs of cells recorded on thesame electrode were excluded. rsc was calculated in 150 ms sliding time bins; note that the magnitude of rsc can be small when measured in short time windows(Cohen and Kohn 2011). The rsc value for each bin is plotted at the right-most edge (i.e., the first plot point reflects rsc, calculated using spike counts in thegray-hatched time window). C and D: attention effect on firing rate (C) and rsc (D), calculated for each neuron or pair by subtracting the unattended responsefrom the attended response (). Firing rates were greater, and rsc was lesser for attended (att.) than unattended (unatt.) stimuli. Effect sizes of attention weregreater for fast-spiking than for regular-spiking cells. Lines along the bottom of the plots reflect time points with significant nonzero attention effects forfast-spiking cells (red), regular-spiking cells (blue), and pairs of cells with mixed-class membership (gray), as well as significant differences between classes(black); P � 0.05, corrected for multiple comparisons. Only data up to the time of target onset on each trial were used for analysis (n � 246.7 � 9.5 trials/sessionfor each condition until the start of the target-onset window at 0.25 s, decreasing roughly linearly thereafter to n � 51.0 � 5.7 trials/session by 0.7 s).




absolute differences in firing rate (t6297 � 7.00, P � 2.8 �10�12, analyzed in the same epoch as above) and also whenwe considered differences in firing rate relative to eachneuron’s average firing rate across conditions (t6297 � 2.60,P � 0.01). Second, the effects of attention on firing rateseemed to match qualitatively the temporal structure of ourtask, with larger firing-rate effects noted at the time whenspeed change was most likely to be detectable. Third,although our waveform categorization algorithm did not usethis as a variable, we found significantly higher firing ratesin fast-spiking neurons (9.01 sp/s) than in regular-spikingneurons (6.65 sp/s; t6297 � 7.71, P � 1.4 � 10�14),consistent with previous reports (Connors and Gutnick1990; Nowak et al. 2003). Taken together, our findings—that fast-spiking neurons show a greater effect of attentionthan their regular-spiking counterparts—suggest that theinhibitory subpopulation is more strongly driven by endog-enous attention signals.

Effect of attention on rsc. Next, we examined functionalconnectivity by measuring the shared trial-to-trial fluctuationsin response strength among neuron pairs over repeated identi-cal trials (“noise correlation” or rsc), isolating attention effectson different neuron classes (Figs. 6, B and D, and 7, B and D).One proposed mechanism for attention is a normalizationsignal introduced by modulation of inhibitory neurons (Mitch-ell et al. 2007; Reynolds and Heeger 2009). We hypothesizedthat if inhibitory neurons are preferentially targeted by atten-tion signals, then we might see larger effect sizes of attentionin that population. Moreover, we predicted that the dynamicsof those attention signals would follow the time course of theexpected target strength throughout the trial.

We found a robust reduction in rsc in the attended comparedwith the unattended state across our entire population ofneurons (Figs. 6B and 7B). Once again, this effect was madeparticularly salient by analyzing the differential signal [thedifference in rsc (rsc) between attended and unattended trials]

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for each pair of neurons (Fig. 7D; regular-spiking rsc ��0.004, t48347 � �4.51, P � 6.4 � 10�6; fast-spiking rsc ��0.023, t4430 � �6.93, P � 5.0 � 10�12; analyzed in thewindow 700–1,000 ms after grating onset on catch trials, as forfiring-rate effects above). The magnitude of the drop in corre-lation was greater for the fast-spiking neurons than for theregular-spiking neurons (t52776 � �6.65, P � 3.0 � 10�11),much as the effect of attention on firing rates was greater forfast-spiking than for regular-spiking neurons. We also ana-lyzed the effects of attention in pairs of neurons comprised ofone fast-spiking and one regular-spiking cell. These mixed-class pairs showed attention-related decorrelation that wasintermediate in magnitude but similar in time-course to that ofthe class-matched pairs (rsc � �0.010, t27650 � �8.60, P �8.2 � 10�18). However, since our study aimed to contrastfunctional connectivity profiles across cell classes, we focusedour analyses on pairs with matching cell types.

Our task involved the detection of a gradual, rather thanabrupt, change in speed with some jitter in the onset latency

(see MATERIALS AND METHODS and Fig. 1C). Thus the strength ofthe information about the target waxed and then waned overthe course of the trial. Because it is difficult to sustain selectiveattention to a single location for more than a few hundredmilliseconds at a time (James 1890; VanRullen et al. 2007;Wilschut et al. 2011), we reasoned that our subjects wouldconcentrate their attentive efforts during the periods later in thetrial, during which the drift speed of the target was likely to bemost clearly different from the baseline speed, and this tem-poral expectation would be reflected in our measurements ofthe neurophysiological correlates of attention. In support ofthis prediction, the attention effects on firing rates of neuronsrecorded in lateral intraparietal sulcus (Janssen and Shadlen2005), V1 (Sharma et al. 2014), and V4 (Ghose and Maunsell2002) have been shown to track the expected value of task-relevant information (i.e., a time-dependent “anticipation func-tion”). We observed that the effect of attention on rsc appearedaligned with the temporal structure of our task, most notably,for the fast-spiking neuronal pairs. Taken together, the stronger

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effect of attention on the fast-spiking cells and the alignment ofthis effect to the time course of the task could indicate that thefast-spiking subpopulation receives stronger attention-relatedfeedback signals than does the regular-spiking subpopulation.

As we mentioned above, fast-spiking neurons tend to have agreater firing rate than regular-spiking neurons (Connors andGutnick 1990; Nowak et al. 2003) (Figs. 6A and 7A). This is animportant consideration, because firing rate has been shown toimpact the measurement of correlation directly (Cohen andKohn 2011; de la Rocha et al. 2007), raising the possibility thatany correlation effects that we found might be epiphenomenal.However, since attention inversely affected these two variablesin our study—increasing firing rate but decreasing correla-tion—the decorrelation effect of attention that we observedcannot be explained merely by an effect of firing rates. Acontrol analysis using firing-rate-matched subsamples of neu-rons confirmed that all of the differences between cell classesthat we observed were robust when firing rate was matchedacross classes (Fig. 8).

Dynamic allocation of attention. We described how theattention effects on firing rate and on spike rsc appeared toco-vary with the evolution of the expected value of targetstrength over the course of the trial (the anticipation function).To quantify the strength of this covariation, and reasoning thatthe neural effects of attentional deployment might lead or laga bit from the task’s temporal statistics, we calculated cross-correlations (Fig. 9) between the attention-effect time seriesfrom catch trials (Fig. 7, C and D) and the anticipation functioncalculated from the actual target-onset times and duration ineach recording session (Fig. 1C). We found a significantzero-lag correlation between the anticipation function and theeffects of attention on firing rate (Fig. 9A; fast spiking: r �0.09, t1340 � 14.22, P � 7.7 � 10�43; regular spiking: r �0.05, t4957 � 17.29, P � 4.6 � 10�65), as well as between theanticipation function and the effects of attention on rsc (Fig. 9B;fast spiking: r � �0.05, t4335 � �11.72, P � 2.8 � 10�31;regular spiking: r � �0.01, t46539 � �11.94, P � 7.8 � 10�33;mixed pairs: r � �0.03, t26883 � �15.53, P � 3.7 � 10�54).These effects were significant at the single-subject level, withthe exception of that for the decorrelating effect of attention onthe fast-spiking population in Monkey R, which trended in thesame direction (see Fig. 9 legend).

The dynamics of the attention effects on both firing rate andcorrelation of fast-spiking neurons followed the anticipationfunction more closely than the attention effects of regular-spikingneurons did (Fig. 9; firing rate, two-sample t-test: t6297 � 5.58,P � 2.6 � 10�8; rsc: t50874 � �10.47, P � 1.2 � 10�25). This isnot simply a consequence of the larger attention-effect size for thefast-spiking cells as the cross-correlation is a normalizedmeasure.

With the use of this normalized measure, we were able toexploit the behavioral variability of our subjects across ses-sions to test the behavioral significance of the neural effectsthat we observed. Specifically, we measured the zero-lag cor-relation between the attention effects on firing rate and rsc andthe anticipation function for each session. We found that thisvalue had the greatest magnitude in sessions in which thesubjects showed stronger behavioral effects of attention (Fig.10). In other words, the subjects’ behavioral performance wasbest when the neural effects of attention were most in align-ment with the timing of the task. This correlation between the

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Fig. 9. Cross-correlation between the time course of attention effects and thetime course of the anticipation function. Shading represents � 1 SE. A: theeffect of attention on firing rate had a significant positive cross-correlation withthe anticipation function, peaking �0 lag. That is, firing-rate modulationsmanifested in proportion to the expected value of target strength. The strengthof this cross-correlation was significantly greater for fast-spiking units than forregular-spiking units. The lines along the bottom of the plot show the lags atwhich the cross-correlation is significantly nonzero for fast-spiking cells (red)and regular-spiking cells (blue) or at which the cross-correlation differssignificantly between classes (black; point-wise t-test; P � 0.05, Bonferroni-corrected for multiple comparisons). B: the effect of attention on spike countcorrelation was significantly negatively cross-correlated with the anticipationfunction. That is, the strength of attention-related decorrelation was propor-tional to the evolution of the expected target information. The extremum ofcross-correlation was near-zero lag, and the strength of cross-correlation wassignificantly greater for fast-spiking units than for regular-spiking units. Thelines along the bottom of the plot show the lags at which the cross-correlationis significantly nonzero for fast-spiking pairs (red), regular-spiking pairs(blue), or mixed pairs (gray) or at which the cross-correlation differs signifi-cantly between fast- and regular-spiking classes (black; point-wise t-test; P �0.05, Bonferroni-corrected for multiple comparisons). All of the effects fromeach animal’s individual data were consistent with the population resultsshown (Monkey B firing rate: fast spiking: r � 0.12, t821 � 14.71, P � 1.3 �10�43, and regular spiking: r � 0.07, t2502 � 16.43, P � 1.2 � 10�57; spikecount correlation, fast spiking: r � �0.07, t3412 � �11.80, P � 1.6 � 10�31,regular spiking: r � �0.02, t22183 � �11.5, P � 9.4 � 10�31, and mixed pairs:r � �0.03, t17508 � �15.63, P � 2.5 � 10�54; Monkey R firing rate, fastspiking: r � 0.03, t518 � 3.83, P � 1.5 � 10�4, and regular spiking: r � 0.03,t2454 � 7.48, P � 1.0 � 10�13; spike count correlation, fast spiking: r ��0.01, t922 � �1.15, P � 0.25, regular spiking: r � �0.003, t24355 � �3.52,P � 4.3 � 10�4, and mixed pairs: r � �0.003, t9374 � �2.20, P � 0.028).




behavioral and neural effect sizes was significant for bothfast-spiking and regular-spiking neurons when measured overall sessions from both animals [Fig. 10; firing-rate effects, fastspiking: Spearman’s rank correlation (�) � 0.49, P � 0.001,and regular spiking: � � 0.46, P � 0.001; rsc effects, fastspiking: � � �0.57, P � 0.001, and regular spiking: � ��0.37, P � 0.002]. In general, we did not find these relation-ships to be statistically significant at the level of single sub-jects, although Monkey R did show a significant correlationbetween firing-rate attention-effect size and behavioral effectsize for the fast-spiking subpopulation (� � 0.32, P � 0.04; forall other comparisons, P � 0.29). These data are consistentwith previous studies demonstrating the behavioral impact oftrial-to-trial changes in population variability (Cohen andMaunsell 2010; Mayo et al. 2015).

DISCUSSION

Our goal was to test for differential effects of attentionbetween excitatory and inhibitory neurons to understand betterthe roles of excitation and inhibition for cognition. We trainedmonkeys to attend selectively to one of two grating stimuli.The animals endogenously enhanced their processing of theattended stimulus at the expense of the unattended stimulus,revealing the behavioral effects of attention (Fig. 1B). Thisendogenous attentional deployment was accompanied by in-creases in firing rate (Figs. 6C and 7C) and decreases in rsc(Fig. 6D and 7D) that tracked the time course of anticipationduring the task (Fig. 9). The magnitude of all of these attentioneffects was greater for the fast-spiking (putative inhibitory)subpopulation of neurons than for the regular-spiking (putativeexcitatory) subpopulation (Fig. 7, C and D), suggesting thatinhibitory cells might be targeted more directly by attentionfeedback signals than excitatory cells.

Although many have speculated about distinct roles forinhibition and excitation in cognition (Isaacson and Scanziani2011; Reynolds and Heeger 2009), testing these hypotheseshas proved elusive. Selective modulation of inhibitory in-terneurons is central to several attention models (Reynolds andHeeger 2009), but finding in vivo experimental support forsuch models has proved challenging. Two recent studies

showed that putative inhibitory neurons (identified by wave-form shape) have stronger attentional modulation in firing rateand variability than excitatory neurons (Mitchell et al. 2007)but less adaptation in spike height during bursting (Anderson etal. 2013), supporting the notion that inhibitory neurons mediateattentional gain in sensory cortex. Our study provides three keyinnovations over previous work. First, we measured functionalconnectivity (in the form of rsc) between neurons classified bytype in the context of an attention task. We found that putativeinhibitory neuron pairs had higher correlation overall, andattention had a stronger decorrelating effect on their activitywhen compared with putative excitatory neurons, suggesting akey role for inhibitory networks in attention mechanisms.Second, we measured the time course of two electrophysiolog-ical correlates of attention and found that the magnitude of theattention signals that we identified tracked the anticipationfunction and that this relationship was also stronger for thesubpopulation of putative inhibitory neurons. The third inno-vation was the use of multidimensional waveform analysis toidentify neuronal subpopulations, instead of a single metric, asin previous work. This method provides access to analysis ofputative inhibitory and excitatory neurons in massive multi-electrode data in which the individual neurons are not targeted,opening up numerous possibilities for future research on theroles of inhibition and excitation in alert, behaving animalsperforming complex cognitive tasks.

Previous researchers have been interested in the question ofhow correlated variability might differ between excitatory andinhibitory cell classes, although the lower incidence of inhib-itory neurons in cortex has made the study of interactionsamong them particularly challenging. To date, these effortshave examined interactions between pairs of excitatory neu-rons and between pairs comprised of one neuron from eachclass, but the details of correlated variability between pairs ofputative inhibitory neurons have not yet been reported. Forexample, Qi and Constantinidis (2012) examined how corre-lated variability between excitatory and inhibitory neurons inthe prefrontal cortex changed due to training on a workingmemory task. They found a reduction in correlations betweenpairs of neurons with similar tuning properties, consistent withimproved information capacity in the neuronal population after

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Cor

rela

tion

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an

ticip

atio

n fu

nctio

n (r

) Fast-spiking Fast-spikingRegular-spiking Regular-spiking

Firing rate rSC

A B C D

Monkey BMonkey R

ρ = -0.57p < 0.001

ρ = -0.37p = 0.002

ρ = 0.49p < 0.001

ρ = 0.46p < 0.001

Fig. 10. Relationship between neural and behavioral effect sizes. The zero-lag correlation between the effect of attention on firing rate (A and B) or spike countcorrelation (C and D) and the anticipation function (Fig. 9A) is plotted against the improvement in hit rate in percentage points for trials with valid cues comparedwith trials without cues (i.e., the “validity benefit”). Lines depict the best-fitting trends for Monkey R (dark gray, short dashes) and Monkey B (light gray, longdashes) and for all sessions of both subjects (black, solid). Sessions with stronger behavioral effects of attention had neural effects that more closely tracked theanticipation function. Spearman’s rank correlation coefficient and the corresponding P value are shown (inset). The relationships were significant at the grouplevel but not within individual subjects (� � 0.32, �0.04, �0.14, �0.16; P � 0.04, 0.81, 0.83, and 0.35 for Monkey R in A–D, respectively; � � �0.18, �0.10,�0.04, �0.05; P � 0.30, 0.57, 0.83, and 0.78 for Monkey B in A–D, respectively).




training. They also found that this effect was driven primarilyby pairs of putative excitatory neurons compared with pairswith mixed-class membership. They did not report effects forpairs of inhibitory neurons, most likely because with eightrecording electrodes, they did not encounter a sufficient num-ber of pairs of inhibitory neurons to perform an adequatelypowered analysis of their correlated variability. In anotherexample, Middleton et al. (2012) studied correlated variabilityof spiking activity of neurons in rat barrel cortex duringwhisker deflections. They reported that excitatory neuronsmaintain relatively low levels of correlation throughout spon-taneous and stimulus-driven activity. In contrast, pairs withmixed-class membership had fairly high levels of correlationduring spontaneous activity, with a marked reduction in cor-relation during whisker stimulation. These two studies, to-gether with our results, point strongly to differences in func-tional interactions between excitatory and inhibitory subpopu-lations. Our 96-channel recordings, combined with the use ofmultiple measures of waveform shape and a Gaussian mixturemodel, enabled us to access sufficient numbers of inhibitoryneurons to isolate and study this subpopulation in the contextof an attention task, a feat not previously possible with smaller-scale recordings or simpler waveform classification techniques.Whereas our focus here was on the distinction between puta-tive excitatory and inhibitory neurons, there is interest infurther subclassifications of neurons within these broad groupsbased on functional properties, such as temporal spike-trainstatistics (Ardid et al. 2015; Nowak et al. 2003). A particularadvantage of our methodology is the ease of incorporating suchadditional neuronal characteristics.

A critical difficulty in understanding the neural circuits thatgive rise to attention has been the conceptual difficulty inreconciling various neural attention effects, such as increasesin firing rate and decreases in correlated variability. This hasled to the rise of network modeling as a means to understandinput-output relationships at a network level and to explainhow synaptic and other neuronal properties are transmitted intocorrelation (Ostojic et al. 2009; Shea-Brown et al. 2008). Anumber of influential network models rely on a precise balanceof inhibition and excitation to model neural activity (Middletonet al. 2012; Renart et al. 2010; van Vreeswijk and Sompolinsky1996), and a recent study used such a model to show thatselective modulation of inhibitory networks may be a key toexplaining the neural effects of attention (Wiese et al. 2014).Our findings are compatible with the intuitions gleaned fromsuch a network-level model and strongly support a specializedrole for inhibitory networks in spatial-selective attention.

ACKNOWLEDGMENTS

The authors thank Ms. Samantha Nelson for help with data collection andDr. Adam Kohn for providing the spike waveform data used in Fig. 4.

GRANTS

Support for A. C. Snyder was provided by the National Eye Institute(Grants F32EY023456 and K99EY025768). Support for M. J. Morais wasprovided by a National Institute on Drug Abuse undergraduate researchtraining fellowship through the University of Pittsburgh’s Program in NeuralComputation (R90DA023428). Support for M. A. Smith was provided by theNational Eye Institute (Grants R00EY018894, R01EY022928, andP30EY008098); Research to Prevent Blindness (a Career Development Grantand an Unrestricted Award); and the Eye and Ear Foundation of Pittsburgh.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

A.C.S. and M.A.S. conception and design of research; A.C.S. performedexperiments; A.C.S. and M.J.M. analyzed data; A.C.S. and M.A.S. inter-preted results of experiments; A.C.S. prepared figures; A.C.S. and M.A.S.drafted manuscript; A.C.S. and M.A.S. edited and revised manuscript;A.C.S. and M.A.S. approved final version of manuscript.

ENDNOTE

At the request of the authors, readers are herein alerted to the fact thatadditional materials related to this manuscript be found at the institutionalwebsite of one of the authors, which at the time of publication, they indicateis: http://www.smithlab.net. These materials are not a part of this manuscriptand have not undergone peer review by the American Physiological Society(APS). APS and the journal editors take no responsibility for these materials,for the website address, or for any links to or from it.

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Dynamics of excitatory and inhibitory networks are differentially … · 2018-11-11 · Dynamics of excitatory and inhibitory networks are differentially altered by selective attention