to assess the correspondence between distinct basal ganglia pathways and hypothesized cognitive processes , we applied the high spatio - temporal resolution of single - unit electrophysiology to a stop - signal task based around our prior studies of rodent decision - making ( fig . 1 , table s1 ) . each rat was trained to place its nose in a central port until the onset of a go cue ( 1khz or 4khz tone ) that directed a brief lateral head movement ( to left or right ; see supplemental movie ) . on 30% of trials the go tone was followed by a stop cue ( white noise ) , instructing that the rat should stay in the central port ( fig . both go trials and stop trials correct performance was rewarded by delivery of a sugar pellet . as typically observed for stop - signal tasks , reaction times for failed stop trials corresponded to the faster portion of the go trial reaction time distribution ( fig . this is consistent with race models : when the go process happens more quickly , a stop process is less likely to successfully suppress behavior . for our first set of recordings ( experiment 1 ) , well - trained subjects ( n=4 ) received tetrode implants that simultaneously targeted striatum , stn , globus pallidus ( gp ) , and snr ( for further details see ref . we isolated spikes from individual neurons during task performance , from each brain region ( for anatomical locations see fig . s1 ) . a challenge when studying behavioral inhibition is to disentangle neural activity specifically linked to stopping , rather than going . to do this , we followed a latency matching procedure ( see methods ) , which exploits the similarity in reaction times -and thus presumably the go process - between failed stop trials and fast go trials . we compared the firing rate of each neuron between these trial types , and between correct stop trials and slow go trials . we then assessed the fraction of each neuronal population that showed significant differences at each moment in time ( fig . in contrast , both stn and snr contained a significant proportion of neurons with rapid responses to the stop signal ( fig . 2a , red and purple filled bars ) . for stn , this population response was the same for correct and failed stop trials ( for the two stn bins just after stop cue onset , p = 0.17 and 0.21 , shuffle test ) and thus resembled a fast  sensory-like response to the stop cue ( see fig . strikingly however , for snr this fast change in activity was only observed for correct , rather than failed stop trials ( for the two filled red snr bins , p = 0.008 and 0.005 , shuffle test ; see also fig . thus , while the activity in stn was consistent with a sensory response , activity in snr instead reflected the behavioral outcome on each trial . our gp recordings did not yield such unambiguous results . although the initial screen indicated that some neurons may selectively respond for correct stops ( fig . 2a ) , the direct comparison did not confirm a selective gp response in correct , rather than failed stop trials ( fig . we next examined the time course of activity in these stop - related stn and snr neurons . 3a ) , that in some cases took the form of just a single , precisely timed extra spike ( fig . these stn increases had consistently very low latencies ( peak response ~15 ms ; fig . 24 for similarly low stn latencies ) that were not different between correct and failed stop trials ( p = 0.41 , paired t - test ; fig . the magnitude of the peak stn response also showed no systematic population preference for correct versus failed stop trials ( fig . 3b ; p = 0.004 comparing stn to snr latencies , one - sided kolmogorov - smirnov test ) and preferentially on correct stop trials ( fig . this latency difference was seen even when the analysis was restricted to units recorded in the same session ( n = 15 pairs ; stn cells preceded snr cells by an average of 13.6 ms , p = 0.041 , shuffling statistic ) . all snr neurons that responded to the stop cue on correct trials did so before the stop - signal reaction time ( ssrt ; grey lines in fig . 3a ) , a standard , inferred behavioral measure for how quick a process must be to influence stopping performance . thus , snr activity not only distinguishes between correct and failed stop trials , it does so quickly enough to affect the trial outcome . most of the snr units with fast stop cue responses ( 10/18 ) also markedly decreased their activity beginning just before movement initiation ( fig . this suggests that the stop cue may not alter snr activity globally , but rather have a selective influence over cells and subregions involved in controlling the specific movement that needs to be inhibited . we therefore performed a second set of recordings ( experiment 2 ) using both high - density silicon probes ( n = 3 rats , fig . 4a ) and more tetrodes ( n = 2 rats ) to target a wide range of snr locations . combining all snr results together revealed a clear  hotspot  of snr cells that fired significantly more on correct than on failed stop trials , briefly after the stop cue ( figs . 4 , s4a  d ) . subregion described in anatomical studies , located dorsolaterally and extended along the rostral - caudal axis . this subregion projects to specific parts of the superior colliculus involved in orienting movements , so the stop signal influences activity in an snr subregion that is likely critical for exerting fast behavioral control . the distinct latencies of stn and snr cue responses are consistent with stop information being conveyed along the stn - snr pathway . yet , the selectivity of this transmission to correct stop trials suggests some form of gating mechanism . in other words : given that the glutamatergic stn cells spike on failed stop trials , why are snr neurons not responsive to this input ? the answer may lie in the movement - related firing rate decreases of snr neurons ( figs . s3 and s4e , f ) . such snr firing pauses are well - known from studies of eye and limb movements , and are thought to facilitate action through disinhibition of superior colliculus and other structures more directly linked to motor output . snr pauses are driven by increased firing of the gabaergic striatal direct pathway neurons , plausible participants in a go process . to assess how striatal neurons may contribute to movement preparation and initiation , we looked for units that distinguish movement direction before movement onset . we found an abrupt increase in contralateral coding starting ~140ms before movements ( arrow in fig . when we compared the activity of these direction - selective striatal cells ( n = 74 ) between different trial types , we observed a rapid acceleration of firing rate just before movement onset ( fig . 5b , right ) , that followed the same trajectory for fast go , slow go and failed stop trials . aligned on the earlier go cue , this striatal activity remained very similar between fast go and failed stop trials , but distinct to slow go and correct stop trials ( fig . these results fit well with a simple race model , in which variability in the timing of a striatal - based go process determines the outcome on stop trials . on failed stop trials , movement - related striatal activity has already begun to increase by the time of stop cue onset ( figs . , the lack of snr responses to the stop cue on failed stop trials may be due to the early arrival of striatal gabaergic input , shunting away the effects of glutamatergic inputs from stn . to confirm the viability of this idea we studied gating of the stop cue responses in a simple integrate - and - fire model of an snr neuron . this neuron received excitatory pulses , mimicking stn sensory responses to stop cues , and ( as in prior basal ganglia models ) this excitatory input was influenced by gabaergic inhibition ( see methods ) . for gabaergic input we adjusted synaptic strengths of inhibitory and excitatory inputs to provide a good qualitative match with the cue - evoked increases and movement - related decreases in snr firing . we define  as the interval between stop cue onset and the point in the striatal output at which movements begin on go trials . if the stop cue begins long before movement onset ( fig . 6a ;  = 200ms ) , striatal inhibition is low at that time and the stop cue evokes a full response in the snr cell . by contrast , if the stop cue occurs only briefly before movement onset ( fig . 6a ;  = 50ms ) , a high level of striatal inhibition suppresses the snr cue response . a systematic variation of  in the behaviorally relevant range yielded a gating curve that quantified the model response to the stop cue ( fig . the gating phenomenon required strong divisive inhibition , e.g. through shunting inhibition , rather than simple summation of inhibitory and excitatory conductances ( fig . we then used the behavioral data of each rat to estimate the actual distribution of  for both correct and failed stop trials ( fig . these  distributions could then be used to calculate model firing rates for these trial types ( fig . importantly , just as in real rat snr cells , the model snr cell selectively responded to the stop cue in correct but not failed stop trials . we conclude that the integration of distinct excitatory and inhibitory synaptic inputs by individual snr neurons provides a straightforward , mechanistic account of how go and stop processes can  race  within the brain . race models have been central to theories of action suppression for decades , yet clear evidence that they actually describe neural processes has been elusive . here we have demonstrated that activity in two key basal ganglia pathways for action control closely resembles a race between go and stop processes . individual snr neurons show both movement - related pauses in firing ( driven by striatum ) and rapid firing rate increases following stop cues ( driven by stn ) , and the relative timing of these influences corresponds to whether stopping was successful . these snr cells are located in a specific dorsolateral subregion , that in turn projects to collicular intermediate layers important for the control of orienting movements . furthermore , the evidence we found for shunting inhibition of stn inputs by striatal inputs begins to reveal how mechanisms operating within single cells can contribute to sensorimotor gating . it is important to note that neurons in stn and snr with fast stop cue responses also increased spiking with the go cue that instructed contralateral movement ( figs . thus the stn - snr pathway does not solely convey signals that instruct stopping , but also other task - relevant cues . the effect of both go and stop cues was to transiently increase firing of a population of snr neurons that decrease firing with movement onset ( fig . s3 ) . we found that trials in which stn and snr responded more strongly to the go cue had longer reaction times ( fig . s9 ) , consistent with a role for stn - snr transmission in delaying behavioral output , rather than causing outright stopping ( see below ) . a rapid ,  automatic  inhibition of behavioral responses by task - relevant cues may help prevent responses that are impulsive or premature ( i.e. when movement preparation is incomplete ) , and also explain why even cues that instruct subjects not to stop , but instead continue as planned , result in longer reaction times . further , our results contribute to the ongoing debate about whether certain brain areas contribute to action inhibition , versus cue - evoked reorienting of attention , by indicating that these functions are not necessarily distinct . the very low , fixed latency of cue - evoked activity in stn is informative in several ways . first , race models often incorporate variable timing of both go and stop processes , yet we found that the stn stop cue response occurred at the same time in correct and failed stop trials . thus , if these responses are part of a stop process , performance variability arises directly from the variable timing of the go process ( i.e. variable reaction times ) , at least in this version of the stop - signal task . this result is consistent with recent simulations of basal ganglia networks during inhibitory control : stn provides the same fast signal to pause action , whether stopping is actually successful or not . second , increased stn spiking within just 15 ms of cue onset constrains the sophistication of prior information processing , and which afferents can be driving this response . recent human studies have emphasized the role of frontal cortical inputs to stn in action suppression , but it is not clear that cue information can be passed quickly enough through cortex to cause this fast stn spiking . our implementation of the stop - signal task encouraged very quick responses , and may have increased the importance of subcortical pathways that support sensory processing and fast orienting - like movements . in particular , many neurons in the thalamic intralaminar complex ( centromedian / parafascicular nuclei , cm / pf ) and pedunculopontine nucleus ( ppn ) have short - latency responses to salient auditory stimuli and project to a range of basal ganglia targets , including stn . cm / pf projections to striatum are important for behavioral switching and learning following unexpected cues . we hypothesize that the stn responses we observed are one component of a broader  interrupt  system , mediated by cm / pf and/or ppn , that coordinates a response to salient cues across multiple timescales using multiple pathways ( fig . very fast yet transient excitation of stn and snr serves to delay actions that are close to execution , similar to previous descriptions of stn  buying time  during decision - making . however , the stn - driven increase in snr firing is highly transient ; in our simulation it would delay , but not fully cancel , the striatum - driven decrease in firing that releases movements ( fig . we also found evidence that a second , slower mechanism may act within striatum to help shut down the go process . movementrelated striatal activity abruptly decreases in correct stop trials , compared to slow go trials ( fig . 5b , c and s6 ) , and a similar suppression of contralateral - coding striatal activity was observed in an antisaccade task . such suppression of a go process is a key feature of  interactive race striatum - based processing by itself is unlikely to account for stop - signal performance , as the reduction in striatal output was not consistently before the ssrt ( fig . it thus appears that complementary mechanisms allow action suppression to be both fast ( via stn ) and selective ( via striatum ) . future studies will investigate how the striatal go process is suppressed in correct stop trials . direct pathway neurons can be inhibited in many ways , and some ( non - exclusive ) possibilities include the influences of indirect pathway cells and cholinergic interneurons . we used a basic stop - signal task , designed to investigate  reactive  aspects of behavioral inhibition ( responding to a stop cue ) . this task does not assess all the complexities of behavioral inhibition , such as  proactive  components ( i.e. preparedness to stop ) . it has been proposed that proactive inhibition involves yet another basal ganglia circuit , the indirect pathway from striatum through gp . in follow - up studies we plan to investigate whether systematically varying preparedness to stop reveals a clear role for gp that was not apparent here . finally , we previously reported ( using the experiment 1 data ) that salient task cues cause a rapid reset of beta oscillatory phase throughout the basal ganglia , whether or not the cues actually direct behavior on a given trial . by contrast , cue - induced increases in beta power only occur for cues that are  used  - for example , after the stop cue on correct but not failed stop trials . this distinction corresponds closely to the difference between stn and snr described here : events that caused abrupt increases in stn firing also produced beta phase reset , while those events that additionally increased snr firing subsequently produced beta power increases . furthermore , there is evidence that other oscillatory frequencies such as delta / theta can influence the parameters of behavioral control , such as decision thresholds . an important direction for future investigation will be to determine the mechanistic relationships between rapid firing rate changes and altered dynamic states within basal ganglia circuitry . all animal experiments were approved by the university of michigan committee for the use and care of animals . subjects were adult male long - evans rats , housed on a 12:12 reverse light : dark cycle and tested during the dark phase . rats were housed in groups of 34 with moderate environmental enrichment ( toys , variety of bedding , 593920 cm cages ) during pre - surgical training , then singly - housed after surgery . the operant chamber had 5 nose - poke holes on one wall and a food dispenser on the opposite wall . at the start of each trial , one of the three more - central holes ( chosen randomly ) was illuminated , indicating that the rat should poke and hold its nose in that port . after a variable hold delay ( 5001,200 ms ) , a tone ( go cue ; 65 db , 50 ms ) instructed the rat to move promptly into the adjacent hole either to the left ( 1 khz tone ) or right ( 4 khz tone ) ; correct choices triggered immediate delivery of a sugar pellet reward ( signaled by an audible click of the food dispenser ) . to encourage rats to respond quickly , on go trials rats had to leave the initial port within a  limited hold period , and then poke the adjacent hole within a  movement hold  ( see table s1 ) . on stop trials ( 30 % ) , the go cue was followed after a short delay ( the stop - signal delay , ssd ) by a stop cue ( white noise burst , 65 db , 125 ms ) . if the rat moved before the ssd , the stop cue was not played and the trial was treated as a go trial . to successfully complete a stop trial , the rat had to maintain its nose in the initial port until the limited hold period would have expired on a go trial . at that point , the audible click of the food dispenser signaled reward delivery . errors of any type produced a time - out ( houselight on , 8 s ) . the computer - controlled sequence of trials was randomized and experimenters were blind to the trial sequence . however , to discourage the animal from adopting a holding - strategy without go responses , the animal had to perform a correct go trial before a stop trial could occur . other randomization or blinding procedures were not performed in this study . after achieving stable task performance ( typically ~23 months of training , > 70 % correct choices on go trials ) rats in experiment 1 received implants containing 21 individually drivable tetrodes targeting basal ganglia structures ( str , gp , stn , and snr ) , and recording sessions began approximately one week later . ssd was held constant within each recording session to facilitate electrophysiological analyses , but was adjusted between stop - signal sessions so that correct and failed stop trials were approximately equal in number . on alternate days rats performed the stop - signal task and a go / nogo task , which was identical in most respects to the stop - signal task but with an ssd of zero ( on nogo trials the white noise was played instead of a go cue ) . during task performance wide - band ( 19,000 hz ) tetrodes were usually moved by at least 80 m between two stop - signal sessions . in some cases , ( e.g. if the number of trials was low in one recording session ) , tetrodes were not moved between sessions , and we only included the better session in the analysis . individual neurons were isolated offline using wavelet - based filtering followed by standard manual spike - sorting , and classification into different presumed cell types . experiment 2 examined whether stop responses are localized to a specific subregion of snr . to facilitate a systematic functional mapping , three rats received 8-shank , 64-channel silicon probes ( neuronexus inc . ) into snr . silicon probe shanks were coated in the lipophilic dye dio before implantation and not moved after initial surgery . for these fixed - location silicon probes we only included data from a single session per animal , to avoid including duplicate cells . data distribution was not formally tested for normality , but we instead mostly used statistical methods that are robust for non - normal - distributed data ( kolmogoroy - smirnov and shuffling tests ) . first , we determined the fraction of failed stop trials f. then we calculated the stopping time as the f - th percentile of the distribution of go trial reaction times . the ssrt is then the stopping time minus the ssd ( for further details see ref . ) . this ssrt value was also used to separate go trials in the same session into  fast go  and  slow go  . to examine the activity of each neuron we used 40 ms time bins ( sliding in steps of 20 ms ) to obtain spike count distributions for different trial types , near key task events . a neuron had to exceed a 3 hz firing rate in at least one time bin to be included in subsequent analyses . to best isolate activity associated with stop cues , we compared trial types for which the activity associated with movement preparation is most similar . that is , we compared failed stop trials with fast go trials , and correct stop trials with slow go trials ( latency - matching ) . for stop trials neural activity was aligned to the onset of the stop cue , and for go trials we used the time at which the stop signal would have occurred , i.e. go cue onset + ssd . we interpreted these as trials for the initial stopping was actually successful , but subsequent holding on for reward was not ( both spike and lfp measures were consistent with this interpretation ; data not shown ) . we therefore excluded failed stop trials with reaction times > 500 ms from all analyses . to compare whether spike rates were different between two trial types , we used a shuffle test for each time bin . we shuffled the trial type labels 10,000 times , and for each shuffle we compared the means of the two resulting spike count distributions . to yield a p - value we determined the fraction of shuffles in which the difference between the shuffled means was larger ( or smaller ) than the difference between the two actually observed means . we used a p - value of 0.05 to determine significant coding of trial types . it follows that 5 % of a population of randomly active units should , on average , be classified as  coding a binomial test was then used to determine whether the empirically measured fraction of coding units was significantly higher than that expected by chance . we corrected for multiple testing with respect to the overall time - window around task events ( e.g. for fig . 2 , each single test was done for a 40 ms time - window , yielding 500/40 independent tests around the task events ) . this correction is overly conservative for some key time points of interest , since we hypothesized a priori that firing - rates would change shortly after cue onset . 2a ) indicate times when the p - value was below 0.05 without adjusting for multiple comparisons . for stn we included units that contributed to the significant responses in either correct or failed stop trials ( i.e. the filled red and magenta bars for stn in fig . did not reach significance in failed stop trials , we included those individual snr units that only contributed to the significant stop cue response in correct stop trials ( i.e. the filled red bars for snr in fig . the firing rate time course of each unit was then estimated by averaging spike counts over trials of the same type ( e.g. failed stop - right trials ) with a sliding 20 ms window in steps of 5 ms around important task events ( e.g. the stop cue ; fig . firing rate time courses between trial types , activity of each unit was first transformed to z - scores using the mean and standard deviation of session - wide firing rate estimates ( obtained from 1 s wide windows ) . to compare the magnitude of the stop response between correct and failed stop trials ( fig . 3c ) , we used peak firing rates in the range 1070 ms after stop cue onset . to more precisely identify the times at which stn and snr neurons responded to the stop cue ( fig . 3b ) , we used non - overlapping 3 ms time windows smoothed with a 3-point average for the firing rate estimation . the latency was taken as the time of peak firing within the range 1070 ms after stop cue onset . 4 and s4a  d ) if the corresponding trial types were significantly different in at least two out three bins ( each bin 40 ms wide , centered at 40 , 60 and 80 ms after the stop cue ) . for fig . 4 we compared trial types separately for ipsi- and contralateral conditions and marked units red if in one or both cases the activity differences were significant . similarly , in fig . s2b , a unit is counted in the histogram if it exhibits a significant difference in activity between failed and correct stop trials for ipsi- or contralateral trials . to describe the gating of the stop cue response , we implemented an integrate - and - fire model of a single snr cell . changes in the membrane potential v at time t were given by : 
 mdvdt=v(t)+emrmi(t ) , where m is the membrane time constant , em the membrane resting potential , and rm the membrane input resistance . i is a sum of currents : i(t ) = ib(t ) + istr(t ) + istn(t ) . the current ib was used to mimic the high spontaneous activity observed in snr cells : ib(t ) = gb ( v(t )  eb ) ( see below for parameter settings ) . synaptic input from the striatum was summarized by istr(t ) = gstr(t)(v(t )  estr ) . the time - dependent synaptic conductance gstr(t ) was modeled using the activity a(t ) of go - related striatal cells during movement initiation shown in fig . 5b ( right panel ) , averaged over slow and fast go trials , as 
 gstr(t)=a(t)i=0ta(i ) . the scaling factor controls the movement - related decrease in our model snr cell and was fitted to reproduce the average time course in fig . although further increases in striatal input strength could block stn inputs without the need for shunting inhibition , this change also produced too early a pause in snr firing ( i.e. a mismatch with the timing of striatal and snr changes observed in vivo ) . the sum 
 i=0ta(i ) is over previous activity in the current trial ( starting 500ms before movement onset ) and is used as a simplified description of facilitation found at striatonigral synapses . in our model this facilitation effectively lead to a broader peak of the striatal activity ramp and thereby to a broader pause in snr activity , as we observed in the experimental data . although real snr neurons pause at a range of times relative to movement onset ( fig . s3 and ref . ) , for our simple model we used a fixed striatal input time course . we further assumed that stn input to snr consisted of a single spike at time ts evoked by the go or stop cue ( see stn example cell in fig . each spike gave rise to a modulated alpha function yielding a synaptic current istn ( t ) = h(t ) ( t , ts)(v(t )  estn ) with a scaling factor  and a shunting factor h(t ) . the scaling factor  controls the amplitude of the snr response to the stn input . we choose  to fit the amplitude of snr responses to the go cue . due to the high baseline activity in snr mediated by ib , in some trials stn inputs can shift the spike timing rather than increase the snr firing rate . when the snr baseline activity is diminished by inhibitory input , this effect is reduced so that stn input becomes more likely to evoke an snr spike ( see grey line in fig . the alpha function was zero for t < 0 and otherwise defined as ( t , ts ) = exp((t  ts ) /q ) exp((t  ts ) /0.25q ) with a time constant q. the shunting factor h(t ) takes values between zero and one depending on the amount of inhibitory current : h(t)=(1istr(t)j)(1istr(t)j ) , where  denotes the heaviside step function . the reference current j controls the efficacy of the shunting inhibition ( fig . a spike was generated by the model snr cell if v(t ) reached the threshold voltage vthr = 50mv . after one millisecond , v(t ) was then reset to the equilibrium membrane potential em = 80 mv . other parameter values were m=20 ms ; rm = 10m ; gb = 0.095s ; eb = 0 mv ; estr = 100mv ; estn = 0 mv ;  = 1/1200 ;  = 0.1 ; q = 10ms ; and j = 1 , 1.5 , and 2 na for  strong  ,  medium  and  weak  shunting inhibition , respectively . further , complex effects on synaptic integration of gabaa were neglected in our model as for simplicity we set estr < em . however , we performed additional simulations with more realistic values for shunting inhibition where em vetoing effect on excitatory stn input . for the simulation without shunting inhibition h(t ) was always set to one . in the model we kept the ssd constant at 300 ms , and assumed that stn input reached snr starting 30 ms after stop cue onset . the snr response to the stop cue was measured as the firing rate within the subsequent 50 ms time window . the interval  between movement onset and stop cue presentation was varied in 1 ms time steps , over the range 0  500 ms . for each  , 500 trials with and 500 trials without stop cue were simulated and the average firing rates were then used for the results . to obtain snr output for failed stop trials , we used the behavioral data of the rats . from the measured reaction times of s7 ) was used as the model parameter  for failed stop trials . for correct stop trials , however , employing the reaction time distribution on go trials , it is possible to estimate what the reaction time would have been if the stop cue had not been presented . more formally , for each rat we have an empirically measured reaction time distribution for go trials fgo(t ) = p(rt = t ) , with p denoting probability and rt being stop - aligned reaction times in 50 ms wide bins . with the corresponding probability distribution for failed stop trials ffs , the hypothetical distribution of correct stop reaction times was estimated as : 
 fcs(t)=fgo(t)pffs(t)1p , where p denotes the overall probability of failing to stop ( number of failed stop trials divided by the number of stop trials ) . due to noise in the behavioral data in a few cases the estimates of fcs were negative , so we applied a lower bound for fcs(t ) of zero and rescaled the whole probability distribution to maintain a total probability of one . the resulting estimates of fcs were then used as the distribution of  for correct stop trials . all animal experiments were approved by the university of michigan committee for the use and care of animals . subjects were adult male long - evans rats , housed on a 12:12 reverse light : dark cycle and tested during the dark phase . rats were housed in groups of 34 with moderate environmental enrichment ( toys , variety of bedding , 593920 cm cages ) during pre - surgical training , then singly - housed after surgery . the operant chamber had 5 nose - poke holes on one wall and a food dispenser on the opposite wall . at the start of each trial , one of the three more - central holes ( chosen randomly ) was illuminated , indicating that the rat should poke and hold its nose in that port . after a variable hold delay ( 5001,200 ms ) , a tone ( go cue ; 65 db , 50 ms ) instructed the rat to move promptly into the adjacent hole either to the left ( 1 khz tone ) or right ( 4 khz tone ) ; correct choices triggered immediate delivery of a sugar pellet reward ( signaled by an audible click of the food dispenser ) . to encourage rats to respond quickly , on go trials rats had to leave the initial port within a  limited hold period , and then poke the adjacent hole within a  movement hold  ( see table s1 ) . on stop trials ( 30 % ) , the go cue was followed after a short delay ( the stop - signal delay , ssd ) by a stop cue ( white noise burst , 65 db , 125 ms ) . if the rat moved before the ssd , the stop cue was not played and the trial was treated as a go trial . to successfully complete a stop trial , the rat had to maintain its nose in the initial port until the limited hold period would have expired on a go trial . at that point , the audible click of the food dispenser signaled reward delivery . errors of any type produced a time - out ( houselight on , 8 s ) . the computer - controlled sequence of trials was randomized and experimenters were blind to the trial sequence . however , to discourage the animal from adopting a holding - strategy without go responses , the animal had to perform a correct go trial before a stop trial could occur . other randomization or blinding procedures were not performed in this study . after achieving stable task performance ( typically ~23 months of training , > 70 % correct choices on go trials ) rats in experiment 1 received implants containing 21 individually drivable tetrodes targeting basal ganglia structures ( str , gp , stn , and snr ) , and recording sessions began approximately one week later . ssd was held constant within each recording session to facilitate electrophysiological analyses , but was adjusted between stop - signal sessions so that correct and failed stop trials were approximately equal in number . on alternate days rats performed the stop - signal task and a go / nogo task , which was identical in most respects to the stop - signal task but with an ssd of zero ( on nogo trials the white noise was played instead of a go cue ) . during task performance wide - band ( 19,000 hz ) tetrodes were usually moved by at least 80 m between two stop - signal sessions . in some cases , ( e.g. if the number of trials was low in one recording session ) , tetrodes were not moved between sessions , and we only included the better session in the analysis . individual neurons were isolated offline using wavelet - based filtering followed by standard manual spike - sorting , and classification into different presumed cell types . experiment 2 examined whether stop responses are localized to a specific subregion of snr . to facilitate a systematic functional mapping , three rats received 8-shank , 64-channel silicon probes ( neuronexus inc . ) into snr . silicon probe shanks were coated in the lipophilic dye dio before implantation and not moved after initial surgery . for these fixed - location silicon probes we only included data from a single session per animal , to avoid including duplicate cells . data distribution was not formally tested for normality , but we instead mostly used statistical methods that are robust for non - normal - distributed data ( kolmogoroy - smirnov and shuffling tests ) . first , we determined the fraction of failed stop trials f. then we calculated the stopping time as the f - th percentile of the distribution of go trial reaction times . the ssrt is then the stopping time minus the ssd ( for further details see ref . ) . this ssrt value was also used to separate go trials in the same session into  fast go  and  slow go  . to examine the activity of each neuron we used 40 ms time bins ( sliding in steps of 20 ms ) to obtain spike count distributions for different trial types , near key task events . a neuron had to exceed a 3 hz firing rate in at least one time bin to be included in subsequent analyses . to best isolate activity associated with stop cues , we compared trial types for which the activity associated with movement preparation is most similar . that is , we compared failed stop trials with fast go trials , and correct stop trials with slow go trials ( latency - matching ) . for stop trials neural activity was aligned to the onset of the stop cue , and for go trials we used the time at which the stop signal would have occurred , i.e. go cue onset + ssd . for we interpreted these as trials for the initial stopping was actually successful , but subsequent holding on for reward was not ( both spike and lfp measures were consistent with this interpretation ; data not shown ) . we therefore excluded failed stop trials with reaction times > 500 ms from all analyses . to compare whether spike rates were different between two trial types , we used a shuffle test for each time bin . we shuffled the trial type labels 10,000 times , and for each shuffle we compared the means of the two resulting spike count distributions . to yield a p - value we determined the fraction of shuffles in which the difference between the shuffled means was larger ( or smaller ) than the difference between the two actually observed means . we used a p - value of 0.05 to determine significant coding of trial types . it follows that 5 % of a population of randomly active units should , on average , be classified as  coding a binomial test was then used to determine whether the empirically measured fraction of coding units was significantly higher than that expected by chance . we corrected for multiple testing with respect to the overall time - window around task events ( e.g. for fig . 2 , each single test was done for a 40 ms time - window , yielding 500/40 independent tests around the task events ) . this correction is overly conservative for some key time points of interest , since we hypothesized a priori that firing - rates would change shortly after cue onset . 2a ) indicate times when the p - value was below 0.05 without adjusting for multiple comparisons . for stn we included units that contributed to the significant responses in either correct or failed stop trials ( i.e. the filled red and magenta bars for stn in fig . as the overall population of snr units did not reach significance in failed stop trials , we included those individual snr units that only contributed to the significant stop cue response in correct stop trials ( i.e. the filled red bars for snr in fig . the firing rate time course of each unit was then estimated by averaging spike counts over trials of the same type ( e.g. failed stop - right trials ) with a sliding 20 ms window in steps of 5 ms around important task events ( e.g. the stop cue ; fig . firing rate time courses between trial types , activity of each unit was first transformed to z - scores using the mean and standard deviation of session - wide firing rate estimates ( obtained from 1 s wide windows ) . to compare the magnitude of the stop response between correct and failed stop trials ( fig . 3c ) , we used peak firing rates in the range 1070 ms after stop cue onset . to more precisely identify the times at which stn and snr neurons responded to the stop cue ( fig . 3b ) , we used non - overlapping 3 ms time windows smoothed with a 3-point average for the firing rate estimation . the latency was taken as the time of peak firing within the range 1070 ms after stop cue onset . 4 and s4a  d ) if the corresponding trial types were significantly different in at least two out three bins ( each bin 40 ms wide , centered at 40 , 60 and 80 ms after the stop cue ) . for fig . 4 we compared trial types separately for ipsi- and contralateral conditions and marked units red if in one or both cases the activity differences were significant . s2b , a unit is counted in the histogram if it exhibits a significant difference in activity between failed and correct stop trials for ipsi- or contralateral trials . to describe the gating of the stop cue response , we implemented an integrate - and - fire model of a single snr cell . changes in the membrane potential v at time t were given by : 
 mdvdt=v(t)+emrmi(t ) , where m is the membrane time constant , em the membrane resting potential , and rm the membrane input resistance . i is a sum of currents : i(t ) = ib(t ) + istr(t ) + istn(t ) . the current ib was used to mimic the high spontaneous activity observed in snr cells : ib(t ) = gb ( v(t )  eb ) ( see below for parameter settings ) . synaptic input from the striatum was summarized by istr(t ) = gstr(t)(v(t )  estr ) . the time - dependent synaptic conductance gstr(t ) was modeled using the activity a(t ) of go - related striatal cells during movement initiation shown in fig . 5b ( right panel ) , averaged over slow and fast go trials , as 
 gstr(t)=a(t)i=0ta(i ) . the scaling factor controls the movement - related decrease in our model snr cell and was fitted to reproduce the average time course in fig . although further increases in striatal input strength could block stn inputs without the need for shunting inhibition , this change also produced too early a pause in snr firing ( i.e. a mismatch with the timing of striatal and snr changes observed in vivo ) . the sum 
 i=0ta(i ) is over previous activity in the current trial ( starting 500ms before movement onset ) and is used as a simplified description of facilitation found at striatonigral synapses . in our model this facilitation effectively lead to a broader peak of the striatal activity ramp and thereby to a broader pause in snr activity , as we observed in the experimental data . although real snr neurons pause at a range of times relative to movement onset ( fig . s3 and ref . ) , for our simple model we used a fixed striatal input time course . we further assumed that stn input to snr consisted of a single spike at time ts evoked by the go or stop cue ( see stn example cell in fig . each spike gave rise to a modulated alpha function yielding a synaptic current istn ( t ) = h(t ) ( t , ts)(v(t )  estn ) with a scaling factor  and a shunting factor h(t ) . the scaling factor  controls the amplitude of the snr response to the stn input . due to the high baseline activity in snr mediated by ib , in some trials stn inputs can shift the spike timing rather than increase the snr firing rate . when the snr baseline activity is diminished by inhibitory input , this effect is reduced so that stn input becomes more likely to evoke an snr spike ( see grey line in fig . the alpha function was zero for t < 0 and otherwise defined as ( t , ts ) = exp((t  ts ) /q ) exp((t  ts ) /0.25q ) with a time constant q. the shunting factor h(t ) takes values between zero and one depending on the amount of inhibitory current : 
 h(t)=(1istr(t)j)(1istr(t)j ) , where  denotes the heaviside step function . the reference current j controls the efficacy of the shunting inhibition ( fig . a spike was generated by the model snr cell if v(t ) reached the threshold voltage vthr = 50mv . after one millisecond , v(t ) was then reset to the equilibrium membrane potential em = 80 mv . other parameter values were m=20 ms ; rm = 10m ; gb = 0.095s ; eb = 0 mv ; estr = 100mv ; estn = 0 mv ;  = 1/1200 ;  = 0.1 ; q = 10ms ; and j = 1 , 1.5 , and 2 na for  strong  ,  medium  and  weak  shunting inhibition , respectively . further , complex effects on synaptic integration of gabaa were neglected in our model as for simplicity we set estr < em . however , we performed additional simulations with more realistic values for shunting inhibition where em < estr < vthr . with appropriately rescaled values for  and j therefore , the key feature of inhibition was the divisive  vetoing effect on excitatory stn input . for the simulation without shunting inhibition h(t ) was always set to one . in the model we kept the ssd constant at 300 ms , and assumed that stn input reached snr starting 30 ms after stop cue onset . the snr response to the stop cue was measured as the firing rate within the subsequent 50 ms time window . the interval  between movement onset and stop cue presentation was varied in 1 ms time steps , over the range 0  500 ms . for each  , 500 trials with and 500 trials without stop cue were simulated and the average firing rates were then used for the results . to obtain snr output for failed stop trials , we used the behavioral data of the rats . from the measured reaction times of s7 ) was used as the model parameter  for failed stop trials . for correct stop trials , however , employing the reaction time distribution on go trials , it is possible to estimate what the reaction time would have been if the stop cue had not been presented . more formally , for each rat we have an empirically measured reaction time distribution for go trials fgo(t ) = p(rt = t ) , with p denoting probability and rt being stop - aligned reaction times in 50 ms wide bins . with the corresponding probability distribution for failed stop trials ffs , the hypothetical distribution of correct stop reaction times was estimated as : 
 fcs(t)=fgo(t)pffs(t)1p , where p denotes the overall probability of failing to stop ( number of failed stop trials divided by the number of stop trials ) . due to noise in the behavioral data in a few cases the estimates of fcs were negative , so we applied a lower bound for fcs(t ) of zero and rescaled the whole probability distribution to maintain a total probability of one . the resulting estimates of fcs were then used as the distribution of  for correct stop trials .
salient cues can prompt the rapid interruption of planned actions . 
 it has been proposed that fast , reactive behavioral inhibition involves specific basal ganglia pathways , and we tested this by comparing activity in multiple rat basal ganglia structures during performance of a stop - signal task . 
 subthalamic nucleus ( stn ) neurons showed low - latency responses to stop cues , irrespective of whether actions were successfully canceled or not . 
 by contrast , neurons downstream in the substantia nigra pars reticulata ( snr ) responded to stop cues only in trials with successful cancellation . 
 recordings and simulations together indicate that this sensorimotor gating arises from the relative timing of two distinct inputs to neurons in the snr dorsolateral 
 core 
 subregion : cue - related excitation from stn and movement - related inhibition from striatum . 
 our results support race models of action cancellation , with successful stopping requiring stop cue information to be transmitted from stn to snr before increased striatal input creates a point of no return .