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Research Report

Task-related modulation in the monkey inferotemporal cortex

Gyula Sárya, Károly Kötelesa, Zoltán Chadaideb, Tamás Tompaa, György Benedeka,⁎aDepartment of Physiology, University of Szeged, H-6720, Dóm tér 10, Szeged, HungarybDepartment of Neurology, University of Szeged, H-6720, Semmelweis u. 6 Szeged, Hungary

A R T I C L E I N F O

⁎ Corresponding author. Fax: +36 62 545842.E-mail address: benedek@phys.szote.u-sz

0006-8993/$ – see front matter © 2006 Elsevidoi:10.1016/j.brainres.2006.08.106

A B S T R A C T

Article history:Accepted 28 August 2006Available online 2 October 2006

The latencies of the neuronal responses from the inferotemporal cortical cells wereanalyzed in animals performing a visual fixation task and a recognition task with the samestimulus set. A consistent reduction in response latencies of about 10 ms was observed infavor of the recognition task. It was found that behavioral relevance reduces the latency inthe inferotemporal cortex and it was concluded that behavioral significance acceleratesinformation processing. This effect has not been described previously.

© 2006 Elsevier B.V. All rights reserved.

Keywords:ElectrophysiologyInferotemporalLatencyMonkeyTask relatedVision

1. Introduction

The inferotemporal cortex (IT) of the monkey is the last stagein the ventral visual stream (Ungerleider and Mishkin, 1982).The cells in this area exhibit stimulus selectivity (Desimoneet al., 1984; Logothetis and Sheinberg, 1996; Tanaka, 1996) andare essential for the recognition of complex stimuli (Dean,1976). Behavioral modifications of the neuronal responses inthe different cortical visual areas have recently receivedconsiderable attention (Reynolds and Chelazzi, 2004). Arecent finding reported that behavioral response latenciesare predictable on the basis of the degree of gamma-bandsynchronization and are accompanied by reduced neuronalresponse latencies in V4 (Womelsdorf et al., 2006). Tanaka etal. (2001a) concludes that global and local attention activatesposterior and anterior IT cortices differently. Socially relevantcues are known to be processed more rapidly in the IT (Kianiet al., 2005), but less is known about how IT cortical neuronschange their activity when behaviorally relevant stimuli arepresented to the animals (Fuster and Jervey, 1981; Richmond

eged.hu (G. Benedek).

er B.V. All rights reserved

and Sato, 1987; Vogels et al., 1995). In this study we examinedwhether the behavioral relevance of a stimulus alters theresponse of IT neurons to that stimulus.

2. Results

Two monkeys were engaged in a fixation task and in arecognition task (see the Experimental procedure) involving 20color stimuli while single cell activity was recorded in the IT.The two tasks were run in blocks; every stimulus waspresented at least 10 times in a semi-random sequence, oneblock therefore consisted of >200 trials.

The activities of 110 IT cells were recorded from the twomonkeys. Cells for which our algorithm failed to determinethe exact latency were excluded from the analysis. Ourstudy is based on the data from the remaining 87 neurons(Table 1).

In the recognition task the monkeys achieved an averagecorrect performance rate of 91.13%.

.

Table 1 – Latency values, baseline activities, net firingrates and sparseness indices (SP) of the cells analyzed inthis study

Monkeyand task(no. ofcells)

Mean(SEM) oflatency[ms]

Mean (SEM)of baseline[spikes/s]

Mean (SEM)of net

response[spikes/s]

Mean(SEM) of

SP

C FIX (57) 132.6 (3.9) 7.7 (0.18) 29.3 (2.37) 0.52 (0.03)C DIS (57) 122.4 (4.5) 8.0 (0.21) 29.2 (2.58) 0.50 (0.03)S FIX (30) 168.3 (8.4) 8.1 (0.26) 34.2 (4.91) 0.51 (0.04)S DIS (30) 159.1 (6.4) 9.1 (0.27) 31.9 (3.84) 0.54 (0.04)

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There were no inter-individual differences between thetwo monkeys in either task in the baseline activity levels orin the mean responses (t-test for independent samples, notsignificant). Comparison of the neuronal activities did notreveal task related differences in neither the baselineactivity or the response to visual stimulation in monkeyC (paired t-test; p=0.584 (n.s.) and p=0.991 (n.s.); n=50). Inmonkey S, however, there was a small, but significantdifference in the baseline (8.1 spikes/s vs. 9.1 spikes/s;p=0.047), but not in the response level (p=0.347 (n.s.);n=37).

SP, used as a measure of stimulus selectivity, rangedbetween 0.50 and 0.54 across monkeys and tasks. Again, therewere no statistical differences.

Fig. 1 – Examples of task-related temporal modulation in the IT.to the effective stimulus. Continuous line: fixation task, dashed l

The main finding of our study was that, while there wasno difference between the firing rates or the SPs, theresponse latencies were shorter in the recognition task thanin the fixation task. This latency reduction was observedduring recording from the same cell, with the same stimuli,while the animals performed the two tasks. Fig. 1 presents 4cells as examples.

Similar differences were found at the population level.The population histogram of all 110 cells demonstrates aslight difference in the baseline activity, an earlier responseonset and an earlier decline of the response in therecognition task (Fig. 2). The distribution of the spikes timesfor all 110 cells in the two tasks was significantly different(Kolmogorov–Smirnov test, fixation task vs. recognition task,p<0.0001). The average latency for all cells was 144.9 ms inthe fixation task, and 135.0 ms in the discrimination task(paired t-test; p<0.0001). The mean latencies across cells inthe fixation task and the recognition task were 132.6 vs.122.4 ms, respectively, in monkey C and 168.3 vs. 159.1 ms inmonkey S.

Fig. 3 presents a scatterplot of the individual latency data ofthe two monkeys in the two tasks. The majority of the valuesare below the line representing equal latencies, indicatingshorter values for the recognition task as compared with thefixation task. Fig. 4 depicts the distribution of the differencesof the pooled latency values for the two monkeys in the twotasks. The distribution is shifted to negative values (mean:

Each histogram represents the mean activity of 10 trialsine: recognition task. Stimuli were presented at time 0.

Fig. 4 – Task-related differences in neuronal responselatencies. Distribution of task-related latency differences inthe two monkeys (recognition task− fixation task). Thedistribution is shifted to negative values; the mean of thedistribution is: −9.87 ms.

Fig. 2 – Task-related differences in neuronal responselatencies. Population PSTH of the responses of the twomonkeys. The solid line indicates the mean of the results on110 cells in the fixation task, while the dotted line shows themean of the results on the same cells in the recognition task.There is a reduction of the response latencies in therecognition task (mean ∼10 ms).

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−9.87 ms), indicating shorter latencies for the recognition taskthan for the fixation task.

3. Discussion

We found that the neuronal response latencies in the IT ofawake, behaving monkeys were shorter in a recognition taskthan those in a fixation task. The sequence of the tasks cannotexplain this effect because the taskswere run in an alternatingfashion. Moreover, one or other task (or even both) wassometimes repeated to ensure that the responses from thesame cell had indeed been recorded.

The difference in latencies could be caused by two factors:either by the change in color of the fixation spot between thetwo tasks or by the tasks themselves. The diameter of the

Fig. 3 – Task-related differences in neuronal responselatencies. The scatterplot shows the neuronal responselatencies in the recognition task as a function of the fixationtask. The majority of the values lie below the linerepresenting equal values for the recognition task and thefixation task. This indicates shorter latency values for therecognition task than for the fixation task.

fixation spot is small, at 6 min of arc (radius=3 pixels), theanimal has to observe the whole stimulus, measuring 5×6°,and the fixation spot has less luminance in the recognitiontask than in the fixation task. For these reasons, we consider itvery unlikely that such a small area of the stimulus (even if itchanges color) could be responsible for the effects, althoughwith the methods we used this can not be ruled outcompletely.

The attentional modulation of cellular responses has beenreported in various cortical areas, e.g., in the MT (Martinez-Trujillo and Treue, 2004), the V1 (Roelfsema et al., 1998) andthe IT (Moran and Desimone, 1985; Desimone and Ungerlei-der, 1986) but as far as we are aware, there have been noreports of a task-related reduction of latencies at the singlecell level.

There are a number of ways to evaluate neuronalresponse latency data (Sato, 1988; Azouz and Gray, 1999;Baylis et al., 1987; Rolls et al., 1993; Roelfsema et al., 1998; Liuand Richmond, 2000; Tamura and Tanaka, 2001; Edwards etal., 2003; Friedman and Priebe, 1999; Hanes et al., 1995;Thompson et al., 1996; Sary et al., 2004; Kovacs et al., 2003;Commenges and Seal, 1985). The typical latency values inthe IT are around 100 ms, depending on the (in some casesundefined) method, though values around 150 ms have alsobeen reported (Tanaka et al., 2001b). In this study, weconcentrated on the biological aspects of the phenomenonrather than the mathematical details. We needed a robustand reliable method with which to calculate neuronalresponse latencies. Poisson spike train analysis yieldedstable results, which fitted well with the estimations derivedfrom inspection of the PSTHs (Sato, 1988; Azouz and Gray,1999). Moreover, for spike trains where the latency valuewould have been dubious, it indicated so (it did not return avalue).

Poisson spike train analysis operates on a trial-by-trial basis,looking for possiblemodulation of the firing rate. Evidently, thismostly occurs at the onset/offset of the visual stimuli. Acrossseveral trials, an array of modulation times is used to calculatethe latency values, thereby making the method sensitiveenough to detect small differences (see also Fig. 5).

Fig. 5 – Latency determination on the basis of the Poisson spike train analysis. Left side: fixation task, right side: recognitiontask. Each of the upper rows represents one trial to the effective stimulus. Stimuli were presented at time 0. The ticks show thespikes, and the shaded areas show significant changes in activity as calculated by Poisson spike train analysis. The heights ofthe shaded areas are related to the significance of the activation, and the lengths are related to the duration when the activitychanged significantly. The last row in each panel shows the corresponding PSTH. Dotted lines above zero indicate the onset ofthe stimulus, while solid lines show the calculated latency. The latency was calculated from the beginning of the first periodwhere a significant change in activity occurred (thick gray lines).

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The color cue in the recognition task in our experimentsignifies that the stimulus requires cognitive processing andappropriate selection of action from the monkey. In contrast,in the case of the fixation cue, no such processing isanticipated. The questions may arise of whether ourmonkeys performed covertly the other task during thefixation task and how this would affect our data. Indeed,during the first trials of the fixation task, we observed eyemovements revealing that the monkeys were attempting todo the recognition task. These attempts always appearedduring the first few trials and disappeared very quickly sincethe animals were not rewarded for them. Each cell in eachtask participated in at least 200 successful trials, and thus theeffect of the first few incorrect trials might not show up at all.Furthermore, any “mixing” of the two tasks would reduce themagnitude of any task-related effects, hence the differencesreported in our study could be only lower estimates of thereal differences.

Wherever the modulation originates from, the un-changed sparseness indices show that it does not changethe selectivity of the neurons. The selectivity of the ITarises through V4, TEO and special local inhibitions in TE(Wang et al., 2000). A modulatory influence could act onthe early visual pathways, or it could be a diffuse input tothe abovementioned areas, acting uniformly on mostneurons of the IT and thus not changing the selectivity ofthis area. The lack of altered selectivity could be compen-sated for by a temporal advantage, which might be morebeneficial for the organism. Attention to a particular objectpromotes a quicker reaction in a situation requiring action,

and it can therefore be expected to shorten the latencytimes in the areas responsible for the processing ofinformation important for the given action. We believethat this is the first documentation of the behavioralmodulation of response latencies in the IT. A 10-msadvantage was provided on the sensory side of the see–compute–react loop, and the individual might thereforereact faster and have more time to decide concerning onthe appropriate action to take.

4. Experimental procedure

4.1. Behavioral tasks

Details of the surgical procedures are to be found in recentpublications (Kovacs et al., 2003; Tompa et al., 2005). Twoanimals (monkeys C and S) were trained to perform a fixationtask and a recognition task with a set of 20 color images(Tompa et al., 2005). Stimuli were presented on a uniform graybackground rectangle (side: 18°, luminance=8 cd/m2) posi-tioned in the center of the screen. The 20 stimuli were simplegeometrical images filled with a colored, textured pattern orphotos of complex, natural or artificial objects. The stimulioccupied the same bounding box of 6×5° and had a meanluminance of 7.9 cd/m2 (SD=5.6 cd/m2). Stimuli were pre-sented centrally.

For the recognition task, during a training period 10 stimuliwere associated with a left-side saccade, while the other 10stimuli were associated with a right-side saccade.

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The sequence of events in the two tasks was as follows (Fig.6). In the fixation task, a red fixation spot (arc diameter=6minorradius=3 pixels, and luminance=5.5 cd/m2) was followed by agray background (500 ms) and a stimulus (500 ms). The fixationspot remained on the screen for a further 100–300 ms. Theanimals were rewarded for keeping their gazes on the fixationspot. In the recognition task, the trials startedwith theonset of ablue fixation spot (same size, luminance: 3 cd/m2) followed by agray background (500 ms) and the same images (500 ms) as inthe fixation task. After the visual stimulus had been switchedoff, two target dots appeared on the sides of the screen. On thebasis of the previous training, the animals had to decidewhether the stimulus just seen belonged to the left or to theright target. The animalswere rewarded formaking a saccade tothe correct side. Accordingly, the only difference between thetasks was the color of the fixation spot and the behavioralresponse requirement. The differences between the stimuli inthe fixation task and the recognition task were so small thatbelow we refer to them as the same (see also Fig. 6 and theDiscussion). Eye movements were recorded with the scleralsearch coil method (Judge et al., 1980); the size of the fixationwindow was 0.5×0.5°. The tasks were presented in blocks, thesequence of which was randomized. The tasks were started inhalf of the caseswith the fixation task and in the other half withthe recognition task. If time permitted, the first task was

Fig. 6 – Tasks used in the study. In the fixation task, theanimals were rewarded for maintaining fixation. Duringrecognition, they were rewarded for making a saccade to theleft or the right after the stimulus offset, on the basis of theprevious training. The shape of the fixation spot merelyindicates the different colors (red in fixation and blue inrecognition) and is exaggerated in size in the Figure. The realsize of the fixation spot is 0.2° at the center of a stimulus,occupying a bounding box of 6×5° (see also the Experimentalprocedure).

repeated to ensure that the same neuron was recorded (seealso theDiscussion). For eachneuron ineach task, the 20 stimuliwere presented at least 10 times (i.e., a minimum of 200successful trials per task). If the monkey broke fixation or res-ponded incorrectly, the trial was aborted. Only data from fullyand correctly completed trials were included in the analysis.

4.2. Recording and data analysis

Neuronal activity was recorded with standard electrophysio-logical methods, using tungsten electrodes (1–3 MΩ, FHC).Signals were amplified, bandpass filtered (FHC) and collectedby using custom-made software. Analysis was performed off-line. An effective stimulus in the fixation task was selected foreach cell, and the corresponding response was taken from therecognition task. Thus, the neuronal activities of the samecells in response to the same images in the two different taskswere compared and analyzed. The neuronal responses wereanalyzed in two time windows, one for baseline activity, from−300 ms to 0 ms (0 meaning the time of the stimulus onset),and the other for the response (100–400ms). For the responsesto the visual stimuli, net firing rates were taken: the baselineactivity was subtracted from the spike count recorded duringstimulus presentation. Net neuronal responses were com-pared by means of the t-test for dependent samples. Fordetermination and comparison of the selectivity, the sparse-ness index (SP) was used (Rolls and Tovee, 1995). Latencytimes were calculated by a Poisson spike train analysis (Haneset al., 1995) and were compared by using t-tests for dependentsamples.

The Poisson spike train analysis described by Hanes et al.(1995) and Thompson et al. (1996) is a two-step process. Instage one, the intensity parameter of the Poisson process isestimated from the baseline activity and the starting times,and probabilities (“surprise indices”) of significant deviations(bursts) are detected. Stage two calculates a single latency timefrom these values. Since we had 10 repetitions per stimulus,stage two of the original method reduced to the degeneratecase: this gave back the average of the first and the thirdearliest burst starting times. Hencewemodified the algorithm:for each trial, we took the beginning of the first activation afterthe stimulus onset and corrected it with aweighted sum of thetimes of occurrence of the spikes preceding it (but still afterstimulus presentation). The weights used were the reciprocalsof the numbers of spikes after the given spike.

tlat ¼ tburst1 �Xn

i¼1

tiN� i

where tlat is the latency, tburst1 is the beginning of the firstactivation, ti are the arrival times of spikes between thestimulus onset and tburst1, n is the number of spikes betweenthe stimulus onset and tburst1, and N is the number of spikesafter the stimulus onset.

The latency values for each stimulus were the trial-wiseaverages of these values. This heuristics was remarkablystable and provided latencies consistent with the PSTHs forthe widest range of cells (Fig. 5).

SP is a measure of the proportion of effective stimuli basedon the response to each of the 20 stimuli. It indicates thelength of the tail of the distribution of the net firing rates for

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the different stimuli. Low values indicate a long tail of thedistribution with only a few stimuli with high response rates.SP for n stimuli is computed by using the following formula:

SP ¼ ½Ri¼1;nðRi=nÞ�2=½Ri¼1;nðR2i =nÞ�

where Ri is the response to the ith stimulus of a stimulus setcontaining n stimuli. SP may range up to 1.0, indicating thecase when a neuron responds similarly to all of the stimuli inthe stimulus set. Only firing rates during the stimuluspresentation interval were used in the calculation of SP, andthe negative net responses were clipped to zero.

All statistical comparisons were considered significant ifthe corresponding p value was less than 0.05. Both animals arestill participating in experiments and histological verificationof the recording sites is therefore not available at this time;however, on the basis of CT images, the neuronal responses,the alternation of white and graymatter during themovementof the electrode, the selectivity for complex color stimuli, theresponse latency values and previous histology made on ourmonkeys with similar parameters, we are confident that therecordings were made in the anterior part of the IT, in area TE,both from the lower bank of STS and the lateral part of TE (justbefore the electrode tip reached the bone). All proceduresconformed to the guidelines of the NIH and of the AnimalWelfare Committee of the University of Szeged.

Acknowledgments

This work was supported by the following grants: OTKA T-042610, OTKA-F048396 and ETT 429/2003. The authors thankL. Gehér for his comments on the Poisson analysis, G. Dósaiand P. Liszli for their technical assistance, K. Hermann formaintaining the laboratory equipment, J. Kóródi for takingcare of the laboratory animals, M. Janáky and T. Gyetvai fortheir help in the eye surgery, and E. Vörös for the NMR and CTimages.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.brainres.2006.08.106.

R E F E R E N C E S

Azouz, R., Gray, C.M., 1999. Cellular mechanisms contributing toresponse variability of cortical neurons in vivo. J. Neurosci. 19,2209–2223.

Baylis, G.C., Rolls, E.T., Leonard, C.M., 1987. Functionalsubdivisions of the temporal lobe neocortex. J. Neurosci. 7,330–342.

Commenges, D., Seal, J., 1985. The analysis of neuronal dischargesequences: change-point estimation and comparison ofvariances. Stat. Med. 4, 91–104.

Dean, P., 1976. Effects of inferotemporal lesions on the behavior ofmonkeys. Psychol. Bull. 83, 41–71.

Desimone, R., Ungerleider, L.G., 1986. Multiple visual areas in thecaudal superior temporal sulcus of the macaque. J. Comp.Neurol. 248, 164–189.

Desimone, R., Albright, T.D., Gross, C.G., Bruce, C., 1984.Stimulus-selective properties of inferior temporal neurons inthe macaque. J. Neurosci. 4, 2051–2062.

Edwards, R., Xiao, D., Keysers, C., Foldiak, P., Perrett, D., 2003. Colorsensitivity of cells responsive to complex stimuli in thetemporal cortex. J. Neurophysiol. 90, 1245–1256.

Friedman, H.S., Priebe, C.E., 1999. Smoothing bandwidth selectionfor response latency estimation. J. Neurosci. Methods 87,13–16.

Fuster, J.M., Jervey, J.P., 1981. Inferotemporal neurons distinguishand retain behaviorally relevant features of visual stimuli.Science 212, 952–955.

Hanes, D.P., Thompson, K.G., Schall, J.D., 1995. Relationship ofpresaccadic activity in frontal eye field and supplementary eyefield to saccade initiation in macaque: Poisson spike trainanalysis. Exp. Brain Res. 103, 85–96.

Judge, S.J., Richmond, B.J., Chu, F.C., 1980. Implantation ofmagnetic search coils for measurement of eye position: animproved method. Vision Res. 20, 535–538.

Kiani, R., Esteky, H., Tanaka, K., 2005. Differences in onset latencyof macaque inferotemporal neural responses to primate andnon-primate faces. J. Neurophysiol. 94, 1587–1596.

Kovacs, G., Sary, G., Koteles, K., Chadaide, Z., Tompa, T., Vogels,R., Benedek, G., 2003. Effects of surface cues on macaqueinferior temporal cortical responses. Cereb. Cortex 13,178–188.

Liu, Z., Richmond, B.J., 2000. Response differences in monkey TEand perirhinal cortex: stimulus association related to rewardschedules. J. Neurophysiol. 83, 1677–1692.

Logothetis, N.K., Sheinberg, D.L., 1996. Visual object recognition.Annu. Rev. Neurosci. 19, 577–621.

Martinez-Trujillo, J.C., Treue, S., 2004. Feature-based attentionincreases the selectivity of population responses in primatevisual cortex. Curr. Biol. 14, 744–751.

Moran, J., Desimone, R., 1985. Selective attention gatesvisual processing in the extrastriate cortex. Science 229,782–784.

Reynolds, J.H., Chelazzi, L., 2004. Attentional modulation of visualprocessing. Annu. Rev. Neurosci. 27, 611–647.

Richmond, B.J., Sato, T., 1987. Enhancement of inferior temporalneurons during visual discrimination. J. Neurophysiol. 58,1292–1306.

Roelfsema, P.R., Lamme, V.A., Spekreijse, H., 1998. Object-basedattention in the primary visual cortex of the macaque monkey.Nature 395, 376–381.

Rolls, E.T., Tovee, M.J., 1995. Sparseness of the neuronalrepresentation of stimuli in the primate temporal visual cortex.J. Neurophysiol. 73, 713–726.

Rolls, E.T., Cahusac, P.M., Feigenbaum, J.D., Miyashita, Y., 1993.Responses of single neurons in the hippocampus of themacaque related to recognition memory. Exp. Brain Res. 93,299–306.

Sary, G., Chadaide, Z., Tompa, T., Kovacs, G., Koteles, K., Boda, K.,Raduly, L., Benedek, G., 2004. Relationship between stimuluscomplexity and neuronal activity in the inferotemporal cortexof the macaque monkey. Brain Res. Cogn. Brain Res. 22,1–12.

Sato, T., 1988. Effects of attention and stimulus interaction onvisual responses of inferior temporal neurons in macaque.J. Neurophysiol. 60, 344–364.

Tamura, H., Tanaka, K., 2001. Visual response properties of cells inthe ventral and dorsal parts of the macaque inferotemporalcortex. Cereb. Cortex 11, 384–399.

Tanaka, K., 1996. Inferotemporal cortex and object vision. Annu.Rev. Neurosci. 19, 109–139.

Tanaka, H., Onoe, H., Tsukada, H., Fujita, I., 2001a. Attentionalmodulation of neural activity in themacaque inferior temporalcortex during global and local processing. Neurosci. Res. 39,469–472.

82 B R A I N R E S E A R C H 1 1 2 1 ( 2 0 0 6 ) 7 6 – 8 2

Tanaka, H., Uka, T., Yoshiyama, K., Kato, M., Fujita, I., 2001b.Processing of shape defined by disparity in monkey inferiortemporal cortex. J. Neurophysiol. 85, 735–744.

Thompson, K.G., Hanes, D.P., Bichot, N.P., Schall, J.D., 1996.Perceptual and motor processing stages identified in theactivity of macaque frontal eye field neurons during visualsearch. J. Neurophysiol. 76, 4040–4055.

Tompa, T., Sary, G., Chadaide, Z., Koteles, K., Kovacs, G., Benedek,G., 2005. Achromatic shape processing in the inferotemporalcortex of the macaque. NeuroReport 16, 57–61.

Ungerleider, L.G., Mishkin, M., 1982. Two cortical visual systems.

In: Ingle, D.J., Goodale, M.A., Mansfield, R.J.W. (Eds.), Analysis ofVisual Behaviour. The MIT Press, pp. 549–585.

Vogels, R., Sary, G., Orban, G.A., 1995. How task-related are theresponses of inferior temporal neurons? Vis. Neurosci. 12,207–214.

Wang, Y., Fujita, I., Murayama, Y., 2000. Neuronal mechanisms ofselectivity for object features revealed by blocking inhibition ininferotemporal cortex. Nat. Neurosci. 3, 807–813.

Womelsdorf, T., Fries, P., Mitra, P.P., Desimone, R., 2006.Gamma-band synchronization in visual cortex predicts speedof change detection. Nature 439, 733–736.