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OW MOVING VISUAL STIMULI MODULATE THE ACTIVITY OF THE

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. BERÉNYI,a P. GOMBKÖTO�,a Á. FARKAS,a

. PARÓCZY,a Z. MÁRKUS,a R. G. AVERKIN,a,b

. BENEDEKa AND A. NAGYa*

Department of Physiology, Faculty of Medicine, University of Szeged,óm tér 10, H-6720 Szeged, Hungary

Bay Zoltán Foundation for Applied Research, BAYGEN, Derkovitsasor 2, H-6726 Szeged, Hungary

bstract—The orientation of spatial attention via saccades isodulated by a pathway from the substantia nigra pars re-

icularis (SNr) to the superior colliculus, which enhances thebility to respond to novel stimuli. However, the algorithmhereby the SNr translates visual input to saccade-related

nformation is still unknown. We recorded extracellular sin-le-unit responses of 343 SNr cells to visual stimuli in anes-hetized cats. Depending on the size, velocity and direction ofhe visual stimulus, SNr neurons responded by either in-reasing or decreasing their firing rate. Using artificial neu-onal networks, visual SNr neurons could be classified intoistinct groups. Some of the units showed a clear preferenceor one specific combination of direction and velocity (simpleeurons), while other SNr neurons were sensitive to the di-ection (direction-tuned neurons) or the velocity (velocity-uned neurons) of the movement. Furthermore, a subset ofNr neurons exhibited a narrow inhibitory/excitatory domain

n the velocity/direction plane with an opposing surroundconcentric neurons). According to our results, spatiotempo-ally represented visual information may determine the dis-harge pattern of the SNr. We suggest that the SNr utilizespatiotemporal properties of the visual information to gener-te vector-based commands, which could modulate the ac-ivity of the superior colliculus and enhance or inhibit theeflexive initiation of complex and accurate saccades. © 2009BRO. Published by Elsevier Ltd. All rights reserved.

ey words: visual response map, saccade, artificial neuronaletwork, cat.

lementary sensorimotor reactions such as startle reac-ions, the orienting reflex or reflexive saccades are con-rolled by forebrain structures, including one of the mainutput structures of the basal ganglia, the substantia nigraSN) (Pollack, 2001; Afifi, 2003). Nigrotectal connectionsay serve as an important route for forebrain control overlementary sensorimotor reactions of the midbrain (Gray-iel, 1978; Behan et al., 1987). The role of the primate SN

n visually-guided saccades was extensively studied byikosaka and Wurtz (1983). They described the activity ofubstantia nigra pars reticularis (SNr) in behaving mon-

Corresponding author. Tel: �36-62-545-869; fax: �36-62-545-842.-mail address: nagya@phys.szote.u-szeged.hu (A. Nagy).bbreviations: AEV, anterior ectosylvian visual area; DAergic, dopa-

linergic; KS, Kolmogorov–Smirnov; SC, superior colliculus; SN, sub-

tantia nigra; SNr, substantia nigra pars reticularis.

306-4522/09 $ - see front matter © 2009 IBRO. Published by Elsevier Ltd. All rightoi:10.1016/j.neuroscience.2009.07.031

1316

eys, reporting that these units decreased firing in re-ponse to stationary visual stimuli used as saccade tar-ets. Their work established the saccade-initiation theoryWurtz and Hikosaka, 1986), which assumes that suspen-ion of tonic inhibitory GABAergic input by the SNr to theuperior colliculus (SC) is a permissive step toward a subse-uent saccadic eye movement. Their findings, with respect toesponse properties of SNr cells, were later extended by thebservation of increased activity of some SNr cells in re-ponse to visual sensory stimulation (Magariños-Ascone etl., 1994; Handel and Glimcher, 1999) and modulation of SNresponses by moving visual stimuli (Schwarz et al., 1984;agy et al., 2005a,b, 2006). The latter is not surprising, sinceovement is an important natural behavioral stimulus, andotion-dependent forecasting is needed to accurately tracknd target moving stimuli.

The present study examines the nature of the encodinglgorithm by which the SNr maintains its diversified func-ion, by testing the relationship between the responseharacteristics of single SNr cells and the physical proper-ies of moving stimuli. We hypothesize that the SNr mayupply specific information about moving stimuli to the SC,hich promotes the correct guidance of sight. Specifically,e wish to know whether these neurons have the ability toncode specific features of stimulus movement in theirction potential trains, information which may help to orientttention toward the forecasted position of the stimulus.e also tested whether excitatory and/or inhibitory re-

ponses are stimulus-dependent, and whether they areresent in separate neuronal populations. To answer theseuestions, we recorded and analyzed the responses of a

arge number of visually-active SNr neurons in anesthe-ized cats. Based on to the visual response characteristicsf these neurons, we offer an expanded hypothesis con-erning the role of the SNr in the control of saccade initi-tion and accompanying visuomotor processes.

EXPERIMENTAL PROCEDURES

nimal preparation and surgery

he experiments were carried out on 10 adult cats of both sexes,eighing from 2.5 to 4.0 kg. All procedures were carried out toinimize the number and the suffering of the animals. They followed

he European Communities Council Directive of November 24, 198686/609 ECC) and the National Institutes of Health Guidelines for theare and Use of Animals for Experimental Procedures. The experi-ental protocol had been accepted by the Ethical Committee fornimal Research at the Albert Szent-Györgyi Medical and Pharma-eutical Center of the University of Szeged.

Anesthesia was initiated with ketamine hydrochloride (Calyp-ol, 30 mg kg�1, i.m., Richter, Budapest, Hungary). After cannu-

ation of the femoral vein and the trachea, the animals were placed

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A. Berényi et al. / Neuroscience 163 (2009) 1316–1326 1317

n a stereotaxic head holder. The wound edges and pressureoints were treated generously with procaine hydrochloride (Pro-ain, 1%, TEVA, Debrecen, Hungary). Anesthesia was continuedith halothane (Narcotan, 1.6% during surgery and 1.0% during

ecordings, Zentiva, Praha, Czech Republic; the minimum alveo-ar concentration (MAC) level of halothane being held at 1 and 0.5,espectively), and was maintained for 3 to 5 days. The depth ofnesthesia was monitored by regular inspection of the pupil sizen the non-treated side, and the examination of electrocortico-ram and electrocardiogram recordings. The animals were then

mmobilized with gallamine triethiodide (Flaxedyl, 20 mg kg–1, i.v.,igma, St. Louis, MO, USA). During the experiment, a solutionontaining gallamine (8 mg (kg h)–1), glucose (Glucosum 40%, 10g (kg h)–1, Pannonpharma, Pécs, Hungary) and dextran (Rheo-acrodex 10%, 50 mg (kg h)–1, Baxter, Platting, Germany) ininger’s solution (B. Braun, Melsungen, Germany) was infusedontinuously at a rate of 3 ml (kg h)–1. Atropine (atropinum sulfu-icum, 0.1%, 0.2 ml, s.c., Egis, Budapest, Hungary) and ceftriax-ne (Rocephin, 40 mg (kg day)–1 i.m., Roche, Budapest, Hungary)ere administered. The end-tidal CO2 level and the rectal tem-erature were monitored continuously and kept constant at 3.8–.2% and 37–38 °C, respectively.

ecording and stimulation

lectrophysiological single-cell recordings were performed extra-ellularly via parylene-insulated tungsten microelectrodes (AMystem Inc., Carlsborg, WA, USA, 2 M�). Vertical penetrationsere made into the SN between Horsley–Clark coordinates ante-

ior 4–7 mm, lateral 4–6 mm, at a stereotaxic depth between 4nd 7 mm. Individual action potentials were selected with the helpf a data acquisition system (SciWorks Datawave, Datawaveechnologies, Berthoud, CO, USA). Putative GABAergic SNr neu-ons were selected via the duration of their action potentials andheir spontaneous firing rate (Ungless et al., 2004).

At the end of each experiment, the animal was deeply anesthe-ized with pentobarbital (Euthanyl, 200 mg kg–1, i.v., Bimeda-MTC,ambridge, Canada) and transcardially perfused with 4% parafor-aldehyde solution. Brains were removed, sliced into coronal sec-

ions of 50 �m, and stained with Neutral Red. The positions of theecorded neurons were localized on the basis of the tracks ofhe electrode penetrations, and their depths were related to theurface of the SN. The position of the uppermost neuron that coulde distinguished as belonging to the SN based on firing rate andecording depths was used to define the dorsal nigral surface.

Visual receptive fields of the neurons were estimated subjec-ively by listening to the neuronal responses to the movements of

light spot generated by a handheld lamp. To quantitativelyharacterize the motion sensitivity of SNr neurons, we chose aery simple stimulus, which provides a high contrast ratio, andhose parameters are easy to quantify. Specifically, moving spotsere projected onto a tangent screen (LCD projector, refresh rate00 Hz; resolution 1280�720 pixels; contrast ratio: 1300:1; re-ponse time: 8 ms) centered on the area centralis and positionedt a distance of 57.3 cm from the eye of the animal. In agreementith previous reports, tested visual fields covered almost thehole visual field of the contralateral eye. Spots of two differentiameters were used: 1° or 5°. A standard stimulus set of 80timulus parameter combinations was used to test the visual respon-iveness of each neuron. Specifically, we moved each contrast spot

n eight different directions within the projected space (0–315° in 45°ncrements, where 0° corresponded to straight up direction), with fiveifferent movement velocities (5, 20, 40, 80 and 160 °/s). For eachecorded neuron, each stimulus combination was presented at least0 times. For each trial control records were made for 1000 msefore presenting the stimulus (the prestimulus time), and then theoving stimulus was presented for an additional 1000 ms (the peri-

timulus time). The number and temporal distribution of the action

otentials recorded during visual stimulations were stored for off-line a

nalysis (sampling rate: 20 kHz) and were visualized as peristimulusime histograms.

nalysis of the visual responsiveness of the SNreurons

he data were analyzed with MATLAB® software (The Mathworks,nc., Natick, MA, USA). The duration of the recording from eachell is proportional to the chance of losing it during the recordingession (and also with the instability of the recording). Conse-uently, the high number of different stimulus parameters testedorced us to test only a limited number of repetitions for eachifferent stimulus. The responses of the SNr neurons to visualtimuli in anesthetized cats were usually weaker than those ofther visually responsive structures in the visual system. More-ver, the low number of cases made t-test comparisons betweenhe pre- and peristimulus periods vulnerable to sudden bursts. Forhese reasons, this statistical procedure was poorly suited for thisork. To obtain a parameter that reliably estimates the effect of

he applied stimulus on the activity of a neuron based on aelatively small number of recorded trials, we tested the responsesn overlapping narrow time windows with the help of the Kolmog-rov–Smirnov (KS) test. While all of the prestimulus periods dur-

ng the whole recording of a neuron contained spontaneous ac-ivity, we handled them as a common dataset for spontaneity. Thisataset was tested for stability and homogeneity. Those record-

ngs in which more than 5% of trials exceeded the �1 SD range ofean spontaneous activity were considered to have inhomoge-eous spontaneous activity (bursting, fluctuations, etc.), or to be aesult of unacceptable isolation during recording, and were ex-luded from the analysis. With the help of this strict rule, we wereble to minimize false detection of response fluctuations. Twoundred sequences of 200 ms were randomly selected from theataset, and the firing rates of each 200-ms-long segment werealculated (Fig. 1A). In a second step, the firing rates for eachtimulus combination were estimated separately. The firing rate ofach 100-ms-long sequence (with 50% overlap) within the peris-imulus period was compared with the previously created datasetwhich represented the spontaneous activity) by using the KS testFig. 1B–D). We defined those 100 ms-long sequences of theeristimulus time as responses for which the KS test demon-trated a significant difference from the spontaneous activity.hese “significant” segments are marked with colored bars in Fig., which denotes the responses of an SNr neuron to various visualtimuli. To quantify the strength of a response, we summed thebsolute values of the net firing rates (the difference of the stim-lated firing rate and the spontaneous firing rate) during the sig-ificant sequences of the peristimulus intervals. We tested ourstimation method on numerous negative control recordings (onecordings without stimulation), and also on previous well-charac-erized neuronal data. The KS test, combined with the calculationf net firing rates, proved to be an effective tool for estimationf the strength of a response, because it eliminates the distur-ances caused by non-consequent burst-like activity amongrials, and also gives a reliable quantification of the strength.

To visualize the stimulus preference of each neuron, two-dimen-ional color-coded maps were constructed, where the axes repre-ented the independent stimulus parameters, and the color scaleenoted the responses to the different parameter combinations.

lassification of response properties

ased on visual inspection of the response characteristics, welassified the SNr neurons into 10 different tuning types (for detailsoncerning the preference types, see the Results section). Toave an objective, reproducible method with which to sort theesponse maps into hypothesized classes, we used an artificialeural network containing 40 joints as an input layer and 10 joints

s an output layer (representing the 10 different hypothesized

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A. Berényi et al. / Neuroscience 163 (2009) 1316–13261318

ig. 1. Analysis of the visual responsivity of SNr neurons. (A) The left panel depicts the raster plot of the spontaneous activity of a SNr neuron across80 recorded trials, each 1000 ms long. The 200 randomly-selected, 200-ms-long segments which served as a representative dataset for thepontaneous firing rate of the investigated cell are indicated by red lines. The distribution function of this dataset can be seen in the right panel, wherehe abscissa defines the firing frequency ranges, and the ordinate the frequencies each range. (B, C) Two 100-ms-long segments of the neuronalctivity during visual stimulation. The left panel shows the peristimulus time-histogram (PSTH) of a neuron. Each PSTH includes a prestimulus andperistimulus period; the time scales are presented on the abscissas (ms). The duration of visual stimulation is denoted by the thick horizontal black

ine above the PSTHs, and ordinates demonstrate the cumulated spike counts in each 25-ms-wide bin. Below the PSTHs, the raster plots of thectivities of the 10 trials are delineated. The unshaded part denotes the 100-ms-long segment of the response from which the distribution function inhe right panel is calculated. The KS test indicates that these distributions in parts b and c are not significantly different from the distribution ofpontaneous activity (part a, right panel). (D) A part of the PSTH which contains a visually-induced neuronal firing frequency increase; in this segment,he distribution of the firing rates differs significantly from that of the spontaneous activity. For interpretation of the references to color in this figure

egend, the reader is referred to the Web version of this article.

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A. Berényi et al. / Neuroscience 163 (2009) 1316–1326 1319

lasses), together with three hidden layers, each with 400 neu-ons. This network was automatically generated by a standarduilt-in function of the MATLAB software. The number of neuronsithin the different layers was adjusted empirically, to achieve the

astest, learning performance. The output layer was adjusted toave a log-sigmoid transfer function, and thus the response valuef each output component ranged between 0 and 1. The systemas embedded with resilient back-propagation as a self-learninglgorithm. With this method, a properly trained network can cor-ectly recognize inputs it has not seen before. (Because of theigh number of possible variations within each group, a hypothet-

cal definition of the classes is unavoidable; more objective unsu-ervised learning algorithms, such as the self-organizing map,annot be used for this classification task.) This generalizationbility was used to classify the response characteristics. Theesilient back-propagation technique is widely used for automated,bjective classification tasks due to its high internal stability (Ser-en and Corra, 2002; Grip et al., 2003; review: Lotte et al., 2007).bove a certain complexity, neural networks of this kind show a

ather stable performance (Palaniappan, 2006). Our choice con-erning the number of neurons and hidden layers resulted inffective recognition performance, with acceptable computationalosts.

To train the network, we generated artificial response char-cteristics (40–100 of each tuning type) and presented them tohe network over several hundred epochs until the recognitionrror limit criterion (mean squared error, �10�5) was met. After

ig. 2. Visual responses of a SNr neuron. The 5�8 peristimulus time-hhe corresponding stimuli, with the same conventions as in Fig. 1. A sigondition is indicated by a red bar, and a significant decrease by a bluarked on the common abscissa and ordinate, respectively. In thehe axes denote the same direction and velocity values as the majorlue and red colors, respectively.

he training session, the real data for each recorded SNr neuron m

ere presented, and the network provided 10 values representinghe probability of fit into the 10 different types of tuning character-stics. The schematic buildup of this artificial neural network isutlined in Fig. 3. For each automated classification result, wealculated the confidence value as:

Vc � 1 �P2

P1,

here Vc is the calculated confidence value, while P1 and P2

enote the probability of the first and second most probable classype, respectively. We accepted a result as well-classified if therobability of the primary class was �0.5, and its confidence valueas �0.5. If a response map did not meet this characterizationriterion, then it was visually inspected. If the subjective catego-ization was in agreement with the automated result, the neuronas included within the analysis; otherwise we categorized it as

unclassified.”

RESULTS

isual responsivity of the SNr neurons

he visual responses of 312 well-categorized SNr neu-ons, with monophasic spikes with waveform lengths of �1

s (PSTHs) represent the responses of a SNr GABAergic neuron duringcrease in firing rate in a specific period in response to a given stimuluse direction and velocity of the movements of the applied stimuli are

t corner the corresponding response map can be seen as an inset.ain axes. Inhibition and excitation are marked with proportionally dark

istogramnificant ine bar. Th

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unney, 1983; Ungless et al., 2004), were analyzed. Thepontaneous activity of these neurons was high, with a meanf 27 spikes/s (SD�13 spikes/s, range: 17–76 spikes/s). The1 neurons in the unclassified group showed rather stochas-ic response maps, without any regular pattern. Interest-ngly many of these neurons had fluctuating responses,ontaining both excitatory and inhibitory segments. Theseingle units were excluded from the further analysis.

The visual receptive fields of the classified neuronsesembled those from our previous findings, since theyovered most of the contralateral visual hemifield includinghe area centralis (Nagy et al., 2005a). The majority of theisually active neurons responded optimally to high veloc-ties (�40 °/s), but poorly to stationary visual stimulation.o particular movement directions were preferred by aajority of the SNr neurons across the population. The

pontaneous neuronal activity and velocity preference dis-layed a weak negative correlation; single units with higherpontaneous firing rate preferred lower stimulus velocitiesR��0.12, P�0.025). We found no such correlation con-erning the direction sensitivity.

The stimulus conditions which evoked the greatesthange in neuronal activity were regarded as preferredonditions for the cells. There were 132 (42%) cells withominant excitatory responses (an increase in their firingate during stimulation). Their average firing rate in re-ponse to the preferred conditions was 34 spikes/sSD�17 spikes/s, range: 19 to 84 spikes/s), a 21% aver-ge increase relative to the spontaneous activity. For the80 (58%) neurons with dominant inhibitory responses (aecrease in their firing rate during stimulation), the meanring rate in response to the preferred conditions was 21pikes/s (SD�11 spikes/s, range: five to 68 spikes/s), 18%ess than the mean spontaneous activity. Comparison ofhe spontaneous activity of the cells with the visual re-ponses observed under the preferred conditions revealed

ig. 3. Scheme of the artificial neural network used for response presponse map of a recorded neuron, the layers of the artificial neuralolor coding and the conventions are the same as in Fig. 4. For a de

ignificant differences for both the excitatory (P�0.01) and d

he inhibitory (P�0.01) SNr neurons (Wilcoxon rank-sumest).

elocity and direction tuning of the SNr visualesponses

he major finding of this study is that a large proportion ofhe visual SNr neurons were not exclusively inhibitory orxcitatory in nature, but could display either excitatoryincreased activity) or inhibitory (decreased activity) visualesponses, depending on the stimulus parameters. TheseNr cells were uniform in the sense that they were able to

espond with a definite increase in activity, but only underertain stimulus conditions (Fig. 2). These response char-cteristics were utilized to generate a map for each cell,nd from these maps the neurons were classified into fiveategories (Figs. 4 and 5) with the help of an artificialeural network (see Experimental Procedures).

A majority of the neurons (61%, n�190), referred to asimple cells, exhibited a simple stimulus preference, dis-laying significant changes in activity in response to well-efined stimulus combinations. These cells revealed aoughly circular region with narrow directional and ve-ocity tuning in its best response zone (Fig. 4A, B),lthough minor regions with weaker responses wereresent. Eighty-one (26%) of these simple neurons re-ponded predominantly with excitation (increased activity),hile 109 (35%) underwent a definite inhibition (i.e. aecreased activity during visual stimulation; Fig. 5).

Fifty-seven (18%) of the recorded SNr cells, referred tos direction-sensitive neurons, responded to stimuli mov-

ng in a particular direction (Fig. 4C, D). In this case, theegion of best response displayed a linear organizationligned along a specific direction. Seventeen (5.5%) ofhese cells exhibited an increase in activity in response tohe preferred stimulus, and 40 cells (13%) showed a de-rease (Fig. 5). No correlation was found between the

sification. The figure demonstrates the input dataset containing theand the response characteristic profile produced by the network. Thelanation of the tuning of artificial neural layers, see the text.

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A. Berényi et al. / Neuroscience 163 (2009) 1316–1326 1321

ithin the limits of the small sample, all directions occurredith the same frequency. No neurons were found in theNr that preferred two opposing directions of movements.

Twenty (6.4%) of the recorded cells were observed toe velocity-sensitive neurons (Fig. 4E, F). These cellsesponded to stimuli moving at a certain velocity, irrespec-ive of their direction, which mapped as a linear region ofest response aligned with a specific velocity. Seven2.2%) of the responses were excitatory and 13 (4.2%)

ig. 4. Examples of visual response tuning maps. Each stimulus prefeype of preference. The abscissa denotes the eight different directionsf the response to each specific stimulus parameter combination is core marked in blue, and excitatory responses (increase in firing rate) ietter visibility, the color codes of the preference maps are normalizedpplied visual stimuli were classified into 4�2 distinct classes: (A, B) simE, F) velocity-sensitive excitatory and inhibitory and (G, H) concentric

nhibitory (Fig. 5). The distribution of the preferred veloci- 4

ies was not characteristic; all velocities occurred with sim-lar frequencies.

Both the velocity- and direction-tuned classes dis-layed maps that had additional areas of weaker modula-ion. Often the response areas adjacent to the main re-ponse were of the opposite sign. This organization wasven more striking in the remaining 45 neurons (14.4%),eferred to as concentric neurons. These cells exhibited apecific pattern of complex response characteristics (Fig.

p shows the responsiveness of an SNr GABAergic cell with a specificlus movement, and the ordinate the different velocities. The strength; inhibitory responses (firing frequency decreases during stimulation)e intensities of the colors are proportional to response strengths. Foroothed via a bicubic spline technique. The neuronal responses to theatory and inhibitory, (C, D) direction-sensitive excitatory and inhibitory,. For further details concerning the preference classes, see the text.

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G, H). Twenty-eight (8.9%) cells responded to a specific

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timulus condition with an increase in activity, and to evenlightly different neighboring conditions (both velocity andirection) with a definite decrease (Fig. 5). In other words,

hese neurons exhibited a simple response surrounded byn inhibitory domain in the velocity/direction plane. Con-ersely, 17 (5.5%) cells responded to a specific stimulusondition with a decrease in activity and to neighboringonditions with a definite increase (Fig. 5).

The neurons detailed above were distributed randomlyn the SNr. No spatial clustering of the neuronal stimulusreference types was observed. Similarly, we did not findny correlation between the location of the cells and theirelocity or direction preference.

timulus size modulation of the responses

o address the question of how the size of the stimulusffects the response characteristics of SNr neurons, lightpots of 1° or 5° in diameter were used to elicit visualesponses from 139 of the recorded neurons. Stimulationith the 5° light spot led to a mean decrease of 18% in thexcitatory response and a mean increase of 40% in the

nhibitory response. Thus, the excitatory response to the 1°pot was significantly stronger than that to the 5° spotMann–Whitney test, P�0.01). In contrast, the inhibitoryesponse was significantly stronger (P�0.01) to the 5° spothan to the 1° spot. It was noteworthy that the overallesponsivity remained the same for each cell, irrespectivef the stimulus size; only the ratio of the excitatory and

nhibitory responses changed. With the smaller spot asisual stimulus, 57% of the responses observed within theverall population were excitatory and 43% were inhibitory.

ig. 5. Distribution of visual response characteristics of 312 neurons.he abscissa denotes the hypothesized 10 stimulus preferencelasses, while the ordinate shows the frequency of occurrence of eachlass. The percentage distributions are indicated above each bar.bbreviations: SiE, simple excitatory; SiI, simple inhibitory; DiE, direc-

ion-sensitive excitatory; DiI, direction-sensitive inhibitory; OrE, orien-ation-sensitive excitatory; OrI, orientation-sensitive inhibitory; VelE,elocity-sensitive excitatory; VelI, velocity-sensitive inhibitory; EsI,oncentric neuron—excitation surrounded by inhibition; IsE, concen-ric neuron—inhibition surrounded by excitation.

he larger stimulus made the response character maps w

ore ambiguous, making the automated classification ofhese maps far less reliable than those of the small stim-lus. Further investigations with more stimulus diametersre needed to decide whether the response class of eacheuron is independent of stimulus size.

DISCUSSION

his study highlights the excitatory and inhibitory effects ofoving visual stimuli on SNr neurons in anesthetized cats.his is a suitable model for investigating visual information-rocessing in the SN, since it lacks the numerous directnd indirect non-sensory influencing factors (e.g. rewardrediction, unexpectedness and motor processes) presenturing behavioral paradigms (Sato and Hikosaka, 2002;ommett et al., 2005; Hikosaka, 2007). However, anes-

hesia has a direct influence both on the spontaneousxcitability and on the responsivity of the recorded neuronsVilleneuve and Casanova, 2003). In addition to the gen-ral depression, feedback information originating from theotor executor system is also absent from the investigated

ircuitry. Experiments carried out on awake animals usu-lly report a lower proportion of excitatory type responseshan those on anesthetized animals. This may be becausehe presence of higher spontaneous activity during awakeonditions decreases the ability of excitatory SNr inputs toenerate significant activity changes. By contrast, inhibi-ory inputs would have the opportunity for greater modu-ation of the SNr cell activity. The result would be a shift inalance toward inhibition under awake conditions. With the

ntroduction of the sliding KS test into our analysis, theetection of presumed weakened responses was possible.owever we have to emphasize the high computationalost of this analysis in contrast to the classic methods. Aurther disadvantage of this algorithm is the loss of theemporal distribution pattern, which may also carry impor-ant information. Below, we speculate on the role of theualistic behavior of the SNr neurons, and propose a se-uence of information coding with which the SNr mayontrol the visuomotor functions of the SC.

hysiological properties of the recorded SNreurons

n the basis of their neurotransmitters, most of the neu-ons of the SNr can be classified mainly into dopaminergicDAergic) and GABAergic classes, although a few non-Aergic–non-GABAergic cells remain, whose transmittersave not yet been clarified. Ficalora and Mize (1989) dem-nstrated that the nigrotectal tract consists of the axons ofABAergic neurons. We selected presumed GABAergic

ells from the neuronal population during recording byonsidering two properties: (1) these cells have narrowpike forms (1 ms) in extracellular recordings, and canherefore be reliably differentiated from DAergic cellshich produce broad spikes of over 2 ms that usuallyppear as spike doublets or triplets (Grace and Bunney,983; Ungless, 2004) and (2) GABAergic cells have beenhown indirectly to be fast-firing type II cells, in contrast

ith the slow-firing type I DAergic cells (Guyenet and

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ghajanian, 1978). Based on electrode track reconstruc-ions, the SNr neurons investigated in our study wereocated in the area of origin of the nigrotectal neuronsBeckstead et al., 1981; Jiang et al., 2003). Thus, theirhysiological properties and location indicate that the re-orded neurons were likely GABAergic nigrotectal cells.

In general, most of the SNr neurons we studied exhib-ted sensitivity to moving visual stimulation. Earlier studiesed to the categorization of GABAergic SNr neurons intowo classes. Most of the neurons responded to visualtimuli with strong inhibition, while a smaller proportionncreased their firing rate (Handel and Glimcher, 1999;asso and Wurtz, 2002; Comoli et al., 2003). A mainnding of the present study was that these neurons can beither excited or inhibited, depending on stimulus condi-ions. In our preliminary results larger stimuli elicit morentense inhibition in the SNr, while strongly decreasingxcitatory responses, though the amount of total re-ponses remained similar.

Based on our results, we propose a new classificationf SNr neurons. In addition to the classes with narrowlyuned neurons displaying velocity and direction sensitivity,e have found neuronal classes which showed broadand tuning to one of these stimulus properties. Theseeurons may serve as stand-alone direction or velocityetectors, and may reflect a higher level of organization.erhaps they receive multiple inputs from different simpleells and thus integrate their preference maps. This wouldlso explain the existence of concentric preference maps,here the simple response conditions are surrounded by

nverse responses. These neurons would disinhibit orlock their target SC neurons, only if the movement of thetimulus precisely fit their preference. As a result, theyould produce a vectorial saccade command for a specificirection and distance, consistent with previous evidenceor complex direction and velocity tuning of SNr GABAergiceurons (Nagy et al., 2005a).

imilarities of stimulus preference between the SNrnits and neurons of other visual structures

he source of the complex direction and velocity re-ponses observed in SNr is currently unknown. Previoustudies stimulating the anterior ectosylvian visual areaAEV) with simple geometric forms suggested that it alsoontains a high proportion of direction-selective unitsMucke et al., 1982; Benedek et al., 1988; Hicks et al.,988). Direction-selective cells are also common in areas7 (Hammond and Andrews, 1978), 18 (Rose and Blake-ore, 1974), and 21b (Tardif et al., 2000) and in theosteromedial lateral suprasylvian area (PMLS) (Blake-ore and Zumbroich, 1987), but are less common in areas9 (Bergeron et al., 1998) and 21a (Wimborne and Henry,992; Dreher et al., 1993). Orientation selectivity, in whicheurons are sensitive to movements along one axis,sually with inverse response to the opposing directions,

s a characteristic feature of AEV units, but was notound in this investigation of the SNr. Inhibitory re-ponses are a characteristic feature of cells in area 21b

Tardif et al., 2000) and also cells in the deep layers of o

C (Dreher and Hoffman, 1973). Detecting neurons withelocity and direction sensitivity (analogous to thoseiscovered earlier in the SC and other structures along

he extrageniculo– extrastriatal pathway (Benedek et al.,996; Nagy et al., 2003; Waleszczyk et al., 2007)) sug-ests that the SNr receives strong modulation from thisart of the visual system.

isual afferents supplying the SNr

e did not detect any clustering or correlation between theosition, direction or velocity preference of cells with dif-erent characters in the SNr. This supports the notion thathe SNr is not retinotopically arranged or systematicallyapped in any more complex fashion. Anatomical studies

ndicate two sets of neural circuits that may transmit visualnformation from the retina to the SN. A schematic repre-entation of these anatomical connections is provided byig. 6. Visual information presumably reaches the SNr

hrough the conventional corticostriatal route (Saint-Cyr etl., 1990; Norita et al., 1991). Visually active SNr neuronsay also receive visual input through direct and indirect

ectonigral pathways (Tokuno et al., 1994; Comoli et al.,003). The SC also projects to the pedunculopontine nu-leus (Redgrave et al., 1987) and the subthalamic nucleusTokuno et al., 1994; Coizet et al., 2009), and both of theseuclei make direct contact with neurons in the SN (Lokwant al., 1999). It has been suggested that basal gangliaircuits including the subthalamic nucleus could be aource of visual information for the SN cells with excitatoryesponses (Jiang et al., 2003). Further, the SC sendstrong visual efferents to the suprageniculate nucleus ofhe thalamus (Katoh et al., 1995), which in turn providesisual information to the caudate nucleus (Harting et al.,001), thereby forming a tecto-thalamo-striatal route. Al-hough the SN receives predominantly inhibitory inputsrom the striatum, an excitatory striatonigral pathway (Ro-ríguez et al., 2000) may also transmit visual information tohe nigral neurons.

unctional role of SNr GABAergic neurons

t is unclear whether the visually responsive SNr neuronsake part in sensory information processing, or are insteadomponents of the reverberating motor circuitry of theasal ganglia. The need for integration of sensory informa-ion and motor commands suggests the latter, but directvidence can only be gained from awake, behaving animalxperiments. The convergence of different sensory (Nagy etl., 2005b, 2006), oculomotor (Sato and Hikosaka, 2002;ikosaka and Wurtz, 1983) and somatomotor (Schultz,986) information onto SNr neurons is well supported, buthis does not mean that every sensory neuron has a role inotor functions. Based on previous studies, we conclude

hat saccade-related neurons that display visual activityre more likely to project to the SC than those withoutensory activity (Hikosaka and Wurtz, 1983).

A shift of attention toward novel stimuli in the surround-ng space requires an analysis of the movement parame-ers of the object by the saccade control system. Detection

f novelty may be provided by DAergic SN cells (Martin

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A. Berényi et al. / Neuroscience 163 (2009) 1316–13261324

nd Waszczak, 1994, 1996; Rice et al., 1997; Radnikownd Misgeld, 1998; Comoli et al., 2003; Dommett et al.,005; Redgrave and Gurney, 2006). Thus, pars compactaells may directly or indirectly confer the ability to appro-riately modulate saccadic movements onto SNr cells. The

act that we found an equally represented preference forvery stimulus direction and velocity combination suggests

hat, despite the absence of a topographic organization inhe SNr, some kind of functional organization may beresent. Wurtz and colleagues (Wurtz and Hikosaka,986; Basso and Wurtz, 2002) postulated a higher-order

opographic organization between the visual stimulus sitend the center of the receptive fields. We suggest thatach SNr neuron might be connected to corresponding SCells in such a way that they can activate or block specificaccades (McIlwain, 1990). The SC, on the other hand,eceives a retinotopic, highly-ordered input both from theetina (Graybiel, 1975; Ogawa and Takahashi, 1981; Beck-tead and Frankfurter, 1983) and from the cortical visualreas (McIlwain, 1977; Berson and McIlwain, 1983; Ber-on, 1988; Harting et al., 1992). These two projectionsonverge onto SC neurons, and furnish its retinotopic or-anization (McIlwain, 1990). The simple SNr cells re-orded here may activate or inhibit specific saccades thatould drive the eye toward a position where a movingtimulus is predicted to be when the saccade is carried outJiang et al., 2003). Moreover, Kaneda and colleagues2008) recently showed that the ipsilateral nigrotectal pro-ection also targets GABAergic interneurons in the SC. By

odifying the balance between the effect exerted directlyn the projection neurons of the SC and through this

ndirect connection, the SNr can adjust both the spatial andemporal aspects of the activity of SC motor neurons.

The functional relation between the SNr and SC inursuit eye-movements and saccade generation is known,

ig. 6. Visual and oculomotor connections of the SNr. Connections withe oculomotor circuitry is denoted with dashed arrows. Note that the abbreviations: SCs, Sci, SCd, superior colliculus superficial, intermedTN, subthalamic nucleus; Sg, suprageniculate nucleus; CN, caudate

ut the code behind the information transmission remains a

ontroversial. Two current theories about the meaning ofopulation activity in the SC (Van Gisbergen et al., 1987;ee et al., 1988; Van Opstal and Van Gisbergen, 1989)uggest that the activity of each ensemble determines aector of eye movement with a definite amplitude andirection. This means that the SC expresses an integratedignal that takes into account both the actual eye positionMcIlwain, 1990), and the expected future position. Theseight be integrated within the SC, or possibly within anxternal structure that serves as an integrative center. The

nhibitory nigrotectal neurons presumably help to sharpenhe collicular response map by disinhibiting some tectalnits when they decrease their own firing rate, while sup-ressing other tectal units when they increase their firingate. The capacity of the SNr signal to modify the saccadeirection was verified recently by Liu and Basso (2008).hey found that electrical activation of the GABAergic SNrells profoundly modified the amplitude and direction ofaccades in behaving paradigms, and the stimulation in-uenced both cell types in the SC. Li et al. (2006) con-luded that as a further step of integration, the mutual

nhibition among the populations of SC neurons will shapehe sum gross response profile of the SC, forming a dy-amic balance between inhibition and excitation caused byhe SNr. This output signal (a dynamic sum of the “mini-ectors” represented by the single units of the active pop-lation) is suitable for driving brainstem oculomotor cen-ers (Anderson et al., 1998). The vectorial response pro-les of both the buildup and burst cells of the SC suggesthat they are driven by two different sources: the incomingeedback signal reflecting the actual eye position (or theifference between the site of current attention) and infor-ation on the position (or expected position) of the point of

nterest. The input information is integrated in both the SNr

cending tecto-fugal visual system are marked with solid arrows, whiletecto-fugal pathway and the oculomotor pathway overlap each other.

deep layers respectively; PPT, pedunculopontine-tegmental nucleus;; FEF, frontal eye field; IVA, insular visual area.

hin the asscending

nd the SC, in multiple consecutive steps.

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A. Berényi et al. / Neuroscience 163 (2009) 1316–1326 1325

CONCLUSION

o summarize, this paper provides further understandingf how visual information may modulate the activity of theNr neurons. The spatiotemporally represented visual in-

ormation may determine the sensorimotor integrativeunction of the SNr. We suggest that the SNr could controlhe activity of the SC through direct nigrotectal connec-ions, and could enhance or inhibit the reflex initiation ofaccades to moving targets.

cknowledgments—The authors express their gratitude to G. D.olnár and K. Hermann for their valuable technical assistance, to. Liszli for his expert help, to A. Peto for the data collection, and

o G. Mochol, D. Wójcik and S. Łeski for the computational help.his work was supported by OTKA-NKTH/Hungary grant 68594nd OTKA/Hungary grant PD 75156. A.N. is a János Bolyai Re-earch Fellow of the Hungarian Academy.

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APPENDIX

upplementary data

upplementary data associated with this article can be found, in

he online version, at 10.1016/j.neuroscience.2009.07.031.

(Accepted 15 July 2009)(Available online 21 July 2009)