The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.
Behavioral adaptation is the process by which animals extract the rules of their environment and learn to respond to cues to increase their chances of survival. The frontal areas of the brain, including the dorsal anterior cingulate cortex (dACC), are involved in driving this process, although the underlying neuronal mechanisms are not well understood [1]. Most studies have focused on the number of spikes discharged by single dACC units after informative events occur. Other potentially informative features of the neural response, such as reproducibility of spike timing across trials, have typically been ignored. The reason for that may be the apparent unreliability of spike timing when observing frontal activity, which is consistent with theoretical studies [2]. Accordingly, most models of cognitive processing [3–5] rely on stepwise firing rate inputs, therefore disregarding the potential impact of the temporal structure of the driving signals. In the specific case of dACC, a recent theory [1] suggests that this area transmits a graded signal: the expected value of engaging cognitive resources to adapt the behavior. This signal has to be remembered from the moment when the current behavioral policy appears to be improper until the moment when a more appropriate strategy can be implemented. Hence, a simple neural integrator [6–9], which by construction is insensitive to spike timing, would be well suited to decode and memorize this signal. This neural integrator could be implemented by the lateral prefrontal cortex [10], which is a plausible dACC target during behavioral adaptation [11]. Nevertheless, some brain regions are known to be sensitive to both the timing [12] and the spatial distribution [13] of spikes within their inputs. These features may improve information transfer between neurons through, for instance, coincidence detection [14]. Some studies reported the presence of a temporal structure in frontal activity, including in dACC [15–23]. However, these observations are not sufficient to make conclusions about the relevance of this temporal structure for the downstream network’s dynamics and for the decision about future behavior. Indeed, to the best of our knowledge, there exists no study comparing the reliability and correlation with behavior of spike count and spike timing in individual frontal neurons during a cognitive task. Comparing spike count versus spike timing sensitive decoders is central to the general view of temporal coding [24]. In this framework, temporal coding can be defined as the improvement of information transmission based on sensitivity to spike timing within an encoding time window [24]. In fact, the temporal structure can be present but still not improve decoding, because spike timing and spike count can carry redundant information [25,26]. Furthermore, the temporal structure can be informative but still fail to correlate with behavior, suggesting that downstream processes disregard it and rely solely on neural integration [27,28]. Here, we address the issue of temporal coding in dACC. We use recordings from monkeys engaged in a trial-and-error learning task [29], in which performance relied on reward-based decision making and behavioral adaptation (Fig 1a and Materials and Methods). The task consisted of finding by trial and error which one of four targets was rewarded. Each trial led to the touch of a target and feedback: a reward if the touch was correct, nothing otherwise. In each block of trials (i. e. , a problem), monkeys first explored the different targets in successive trials. The first reward indicated discovery of the correct response. This was followed a period in which they could repeatedly touch the correct target in three successive trials to exploit and receive additional rewards. The firing rate of single dACC units was previously shown to increase at feedback time during either exploration, repetition, or when switching between those two states [29]. Hence, dACC neurons may signal whether and/or how behavior should be adapted. In this context, we probe the putative structure and function of a downstream neuronal network decoding dACC feedback-driven signals. To do so, we investigate to what extent the temporal structure of dACC spike trains, during post-feedback firing, could improve information transmission and predict behavior (Fig 1b). Assuming a neural integrator decoding scheme, the downstream network would compute and maintain the memory of the need for behavioral adaptation on the basis of the number of spikes emitted by dACC (Fig 1c, middle). Alternatively, the downstream network could be sensitive to the spatiotemporal structure of dACC activity (Fig 1c, bottom). For instance, temporal coincidences in the afferent dACC signals could favor the switch to, and maintenance of, a high-activity state in the downstream network to encode behavioral adaptation ([32], see also [33,34]). We bring forth evidence for a spatiotemporal decoding of dACC activity. First, we find that there are informative temporal patterns in single units leading to a decoding more efficient than with spike integration. The optimal decoding time scale is in the range of 70–200 ms. Second, some spike coincidences across jointly recorded neurons are advantageous for decoding. Furthermore, the data suggest that downstream neurons could benefit from a non-linear spatiotemporal integration of inputs. Finally, we develop a new method to evaluate to what extent dACC temporal patterns can predict the behavior of monkeys comparatively to spike count. Importantly, we find that temporal patterns are significantly and sizably predictive of the upcoming response time of monkeys.
We first tested how single-trial, single-unit dACC activity could send signals that could drive behavioral adaptation after feedback. Behavioral adaptation occurred either after the first reward (thus switching from exploration to repetition) or after any error during exploration (Fig 1a). Signaling the need for adaptation requires that spike trains emitted during either first reward or errors can be discriminated from those emitted during repetitions (referred to as first reward and error discrimination analyses, respectively). Neurons in dACC could show early post-feedback responses specific to behavioral adaptation [29]. Therefore, we analyzed spike trains starting at the onset of the feedback delivered 600 ms after target touch. We will refer to any post-feedback time interval (i. e. , following either an error, first reward, or repetition) as a “task epoch. ” We quantified to what extent spike trains emitted during different task epochs were discriminable by a downstream decoder by classifying them based on a spike train dissimilarity measure [35]. This dissimilarity measure computed the minimal cost to transform the first spike train into the second one through two possible recursive operations: (i) adding or removing a spike, for a cost of 1; and (ii) changing the timing of a spike by dt, for a cost of q dt ≤ 2. Note that the maximum cost allowing two spikes to be temporally matched (coincidence detection) is 2 because it corresponds to the cost of removing and adding one spike (Fig 2a and Materials and Methods). This measure allows different temporal sensitivities of a downstream decoder to be evaluated by varying the parameter q. A value of q = 0 s-1 describes a decoder sensitive to pure spike count. On the other hand, a larger q value corresponds to a decoder sensitive to precise spike times. The larger the q value, the smaller the maximum interspike interval leading to coincidence detection, and the more the decoder disregards spike count. We stress that even when the neural activity is temporally structured, sensitivity to spike timing does not necessarily improve decoding. For instance, spike timing and spike count might provide redundant information, and then a neural integrator could be more robust (S1 Text, Sec. 4). We quantified the classification performance (i. e. , how well, on average, a spike train was correctly associated to the task epoch with the most similar activity) by computing the mutual information between the predicted distribution of spike trains across task epochs and the true distribution (Materials and Methods). Throughout this article, mutual information values are expressed as percentage of the maximum value corresponding to perfect discrimination. Information values were computed for different analysis windows, all starting 1 ms after feedback time and with increasing duration. In this way, the state of a putative decoder of dACC feedback-related discharges could be evaluated at different delays after the start of the decoding process. We investigated the nature of dACC firing statistics determining the advantage of temporal decoding. Spike-timing reliability might mainly reflect differences in the temporal variations of firing rates between task epochs. Alternatively, beyond this time-dependent firing rate, temporal correlations between spikes within one trial may impact the spike time reproducibility. Indeed, cellular processes (such as spike-triggered hyperpolarizing currents) may affect future spiking probability depending on past spike times [4,36], especially when the synaptic current received by the neuron is not very variable. Similarly, recurrent neural network dynamics—within dACC or upstream—may create correlations in spike times [37,38]. Here, we tested whether or not, beyond their existence, spike-timing correlations sizably and consistently (over neurons) impacted information transmission. Multiple neurons were often simultaneously recorded (median = 2). Thus, we also decoded the activity of pairs of neurons (Monkey M, n = 122 pairs; Monkey P, n = 271) while varying both the temporal sensitivity q and the degree of distinction k (Fig 2b, Materials and Methods). For the computation of the dissimilarity measure, the parameter k represents the cost of transforming a spike from neuron 1 into a spike from neuron 2. Therefore, during classification of spike trains from pairs of units, the dissimilarity between spikes from different neurons increases with k. The parameter k permits testing of whether the informative spikes are neuron specific or if they tend to be emitted synchronously by two neurons. In the former case, the amount of information would increase if the decoder were accounting for neural identity (k > 0), as compared to a decoder blind to neural identity and sensitive to interferences between neurons (k = 0). In the latter case, k = 0 could be optimal for decoding because it makes the discharge of either one of the neurons sufficient to have reliable joint spiking. The presence of information in single-unit spike timing does not necessarily imply that the downstream networks do actually use it [27,28]. In particular, if dACC spike timing were not used, then different temporal patterns would be rather unlikely to reliably correlate with different behavioral outputs. Here we examined whether first reward single-unit activity could predict upcoming behavior. We focused on the behavioral response time, i. e. , the time between the “go” signal and touch on target (Materials and Methods). The response time was measured during the trial following the first reward. Thus, several seconds separated the analyzed neural activity and the behavioral measure. Interestingly, the response times of both monkeys consistently increased on the touch following the first reward compared to the touch leading to first reward (S11c Fig). This was in agreement with a behavioral switch from exploration to repetition. We separated trials into two groups: one group with response times higher than the median, and the other with response times below the median. The probability of switching to repetition was very high in both groups and statistically equivalent between them (S2 Table). We tested the hypothesis that longer response times may reflect a longer decision-making process, when monkeys might act more carefully to avoid mistakes.
Post-feedback spike counts in dACC neurons were shown to depend on whether behavioral adaptation was required [29]. Given the absence of stimulus-driven temporal fluctuations in input and high noise in spike timing, a plausible hypothesis would be that only spike count is relevant to the transmission of the need to adapt behavior by dACC firing [2]. By contrast, we provide evidence for an efficient spatiotemporal spike coding of behavioral adaptation signals. Our analysis accounts for the temporal sensitivity of a biologically plausible neural decoder that would receive post-feedback dACC discharges. Adjusting the temporal sensitivity of the decoder (within the interval of one second after the feedback) can enhance the readout of single-unit spike trains relevant to behavioral adaptation. Beyond the existence of a temporal patterning of dACC activity, these results indicate that spike-timing reliability supplements spike-count reliability. Interestingly, in frontal areas single-unit spike generation mechanisms or network dynamics, rather than external stimuli or motor feedback, are probably responsible for spike timing reliability and spike-count variability [37,40,43,44]. We found strong temporal correlations, stronger-than-Poisson spike count variability, and heterogeneous spike times across the dACC population. This feedback-type-specific dynamics is thus unlikely to arise through neuronal assemblies connected by balanced excitatory and inhibitory inputs with uniform wiring probability and with stationary weak-to-moderate strengths [37,40], as these features tend to create Poisson-like spike trains. Spike-triggered hyperpolarizing currents or short-term plasticity could also plausibly favor the presence of informative temporal correlations in dACC activity and could participate to shaping the lower-than-Poisson spike count variability occurring shortly after the feedback [36,39,44]. Note that the optimal range of decoding time scale that we found (τ ≈ 70–200 ms) is larger than those found when decoding responses to stimuli with relevant temporal patterning or contrast at onset time (e. g. , auditory stimuli, τ ≈ 5 ms [45]; visual stimuli, τ ≈ 10–100 ms [13,35]). This is consistent with the idea of a hierarchy of increasing time scales from sensory to higher-order areas [46]. However, there are also exceptions to this rule, for instance in the gustatory modality (for which the timing of the stimulus is less relevant). Indeed, the optimal time scales were found to be close to the one we found in dACC (τ ≈ 50–500 ms [47]). Given that during a gustatory stimulation, the motor behavior of the animals and/or some sensorial input transients were probably participating in shaping the temporal code [47], it is quite remarkable that we found equivalent time scales in our data for which internal neuronal dynamics was probably the major contributor to spike timing reliability. Also, the optimal spike coincidence timescale we observed loosely matches the period of local field potential (LFP) oscillations in the delta and theta range, on which frontal neurons can phase lock during cognitive tasks [16,19,48]. LFPs partially reflect the synaptic input of the local population [49], which could both shape and be influenced by the temporal spiking patterns of dACC. The optimal temporal sensitivity range for decoding identified in this study remains an approximation. First, different methods (S1 and S4 Texts), or different analysis windows (Fig 4), might give slightly different optimal values. Yet, although it is not feasible to extensively test all possible decoders, our analysis accounts for biophysically reasonable assumptions on the downstream decoder. In this framework, we provide strong evidence for the plausibility of decoding through spike coincidences (up to a few hundred ms), compared to a neural integrator decoder. Second, spike trains were referenced to feedback time, but the internal reference of the brain could be different and more or less accurate [50] (e. g. , coincidence detection during a population onset [24], or precise spike timing relations in a neuronal population [51]). Aligning to feedback times was very relevant for behavioral-adaptation task epochs where monkeys could not predict the outcome and were thus reacting to feedback. However, anticipation of rewards during repetition periods may have promoted internal references dissociated or jittered from actual juice delivery, decreasing the apparent temporal reliability (as suggested by the data, see S4 Fig). The spike-time sensitive decoder can be understood as a downstream network that, through synaptic plasticity [52], becomes differentially selective to coincident spiking patterns that are specific to task epochs. The optimal temporal sensitivity range is compatible with the time constant of NMDA-mediated currents. Indeed, the efficiency of the spike coincidence mechanism decreased with interspike intervals up to approximately 200 ms, which relates to an exponential decay time-constant of 100 ms. Within the decoding by coincidence framework, decoding relies on the convergence of excitatory neurons that transmit similar temporal patterns to a post-synaptic compartment (triggering summation of depolarizations). Yet, informative neurons with distinct and potentially antagonistic temporal patterns may improve information transfer, for instance, if they were decoded by different specialized post-synaptic neurons. We showed that paired decoding generally enhanced information transmission relative to the pair’s most discriminative unit. This suggests that highly informative activity can be advantageously combined with less informative inputs that do not act as contaminating noise. The information increase was achieved by varying the degree of distinction between the two units (parameter k). This mechanism may be implemented by different spatial organizations of synapses, which could modulate, through non-linear summation, the temporal precision of spike coincidence detection. Other mechanisms, such as different synaptic weights or synaptic timescales (i. e. , two weak/shorter depolarizations that require more precise coincidence to efficiently sum), or targeted inhibition, may also induce a similar effect. In addition, we showed that in a smaller proportion of pairs the activity of both units did not need to be distinguished to achieve optimal discrimination. Thus, if these two units were excitatory, direct summation of their post-synaptic potentials would be advantageous. The partial spatial specificity of reliable spikes may be advantageous during realistic decision-making when quick choices should be made between many strategies. Indeed, the combination of spatial and temporal information can increase the number of possible specific activity patterns compared to simultaneous firing of all neurons. Artifacts of spike sorting were unlikely to affect our conclusions (S2 Text). The removal of (rare) coincident spikes during sorting decreased the reliability of both spike count and temporal patterns. Noisy spikes erroneously assigned to a given neuron can make spike count more reliable whenever two mixed single units have the same firing preference (with respect to task epoch). On the other hand, temporal coding may only become more reliable if the two mixed units are temporally coherent. Our results showed that different dACC units did not often share coherent spike times, which suggests that possible erroneous spike assignments would not favor temporal decoding. Furthermore, pairs recorded on the same electrode did not always differ from pairs recorded on different electrodes (S3 Table). Indeed, while no differences occurred when discriminating first reward versus repetition task epochs, differences could exist for error versus repetition decoding. When present, such differences were more likely to reflect a topological organization of some inputs. Indeed, a by-product of a cross-talk between sorting templates would rather have a constant impact across all task epochs. We further probed dACC function by testing how it could affect future behavior. We found a significant correlation between neural activity at feedback time and the monkeys’ response time during the following trial. This finding is functionally different from the correlation previously reported between pre-movement dACC activity (which often resembles an integration to threshold [53,54], in contrast to feedback-driven dACC responses) and immediate motor response [53–55]. This motor correlation could become apparent through the comparison between trials with high versus low firing rates (or, equivalently, spike-counts in a given window). In particular, Michelet et al. showed that the quicker the increase of firing rate to threshold, the quicker the movement [53]. This implied high versus low spike-count correlation when aligning spike trains with respect to movement. In contrast, we observed a correlation between dACC activity and behavior in terms of deviation from prototypical activity patterns, while we did not observe a robust link between large versus small number of spikes emitted during first-reward–triggered discharges and different behaviors. This result can be well understood when considering that dACC can signal a given behavioral strategy when its activity lies close to a given prototypical state. Hence, this interpretation can be consistent with a report of increased spike count variability (and hence, of absence of defined state of activity) in dACC during periods of behavioral uncertainty [56]. It can also be related to the sudden reorganization of dACC activity in a new “rule-encoding network state” when animals switch to a new rule [57]. Within this framework, first reward feedback triggers specific dACC activity patterns [58] that shape the response of downstream areas such that the appropriate decision (here, switching to repetition) is made. Deviation from these “prototypical patterns” would lead to a slower behavioral response. In addition, if the deviation of dACC discharges from their usual pattern were triggered by increased uncertainty or difficulty, slowing the behavioral response may prevent incorrect choices (as suggested by the similar error rates between trials with fast versus slow responses). Interestingly, these results also suggest that the information transmitted to downstream areas cannot be mapped onto a mere intensity value (i. e. , a single dimension), such as the magnitude of the required cognitive control, as in the case of the integrator model. Rather, the deviation from a prototypical pattern, which relates to behavioral modulation, appeared to occur in many different ways (through either an increase or a decrease of spike count, or through spike timing deviations within the heterogeneous temporal patterns of dACC neurons). This hints to the transmission of a high-dimensional representation by dACC, possibly linked to the embedding of the cognitive control signal into a specific context, or behavioral strategy [1,29,30]. One limitation of our study is that we only characterized the dimensionality of the representation transmitted by dACC through the large differentiation, at the population level, between measures based on firing rate and measures based on (absolute) deviation from prototype. A full evaluation of this dimensionality will need future studies to evaluate the space of neuronal variability and its relation to behavioral variability in each single neuron. Importantly, beyond the deviations of dACC spike trains from prototypical spike count, our findings indicate that deviations from prototypical temporal patterns were predictive of the monkeys’ upcoming response time. This was consistent and significant in both monkeys. Furthermore, compared to the prediction based on spike count deviations, the prediction power of adapted temporal sensitivity was either equivalent (monkey P) or significantly stronger (for monkey M, which showed the most reliable relation between neural activity and behavior). This strongly suggests that the temporal patterning of single unit activity is not an epiphenomenon irrelevant to downstream network dynamics. Interestingly, dACC differs from other decision-making related areas such as middle temporal (MT) or orbitofrontal cortex (OFC) regarding the nature of the relation between neuronal variability and future response time variability. Indeed, in MT and OFC, the firing rate of specific neuronal populations predicts behavioral modulation [59,60]. In addition, evidence suggests that neurons in MT are decoded through integration, a process that could be reflected in LIP (lateral intraparietal cortex) activity [61] and that appears to have one-dimensional dynamics [62]. Altogether, our results appear hard to reconcile with the hypothesis of a decoding of post-feedback dACC activity by a neural integrator. Other types of decoders could be compatible with both an increase in information through spatiotemporal coincidences and a correlation of deviation from prototypical temporal patterns to behavior. For instance, as we illustrate in Fig 1c, a recurrently connected neuronal population, which maintains memory through a high-activity state, can be modulated by the temporal structure of its input [32]. Alternatively, a downstream network maintaining a memory through repetitions of sequential activations of NMDA-connected neurons would also be sensitive to spatiotemporal patterns [63]. Our findings, therefore, call for a better understanding of how models of short-term memory and decision-making could reliably be modulated by a temporal input at the timescale of hundreds of ms.
Two male rhesus monkeys were implanted with a head-restraining device, and neuronal activity was recorded by one to four epoxy-coated tungsten electrodes (horizontal separation: 150 μm) placed in guide tubes and independently advanced in the dorsal bank of the rostral region of the cingulate sulcus. Recording sites were confirmed through anatomical MRI and histology [11,29]. Extracellular activity was sampled at 13 kHz and unitary discharges were identified using online spike sorting based on template matching (MSD, AlphaOmega). All experimental procedures were in agreement with European, national, and local directives on animal research. For monkey P, all recorded units were used. For monkey M, only units showing a significant response to at least one event (either error, or first reward, or repetition reward, fixation breaks) were used (TEST 1 in [29]). The mean and standard deviation of the baseline firing rate (taken from -600 to -200 ms before feedback onset) were computed. Units with a change of firing rate of magnitude higher than 5 standard deviation of the baseline within more than six 10 ms bins between +60 and +800 ms of at least one event were selected. Note that this test cannot favor temporal coding in any way. Monkeys had to find, by trial-and-error, the rewarded target among 4 targets presented on a touch screen (Fig 1a). To begin a trial, the animal had to touch a central item (“lever”), which triggered the appearance of a fixation point. After 2 s of gaze fixation, the four targets appeared simultaneously. At fixation point offset, the animal had to select a target by making a saccade toward it, fixate it for 0. 5 s, and touch it following a “go” signal (i. e. , all targets bright). All targets dimed at the touch, and switched off after 0. 6 s. Reward (fruit juice) was delivered if the correct target was selected, otherwise no reward occurred. Throughout this article, we define a “trial” as the period of time between the touch of the lever and 1 s after the reception of a feedback (either error, or first reward, or repetition reward). In addition, we call “task epoch” the time interval between 1 ms and 1 s after the reception of a given feedback. After feedback, a time break of 2 s was imposed before starting a new trial. Any break in gaze fixation or touch within a trial led to resuming the sequence at the lever touch. Note that we did not consider that this started a new trial. In case of an incorrect choice, the animal could select another target in the following trial, and so on until the discovery of the rewarded target (i. e. , exploration). The correct target remained the same in the following trials, allowing the animal to exploit the rewarded action (i. e. , repetition). We define a “problem” as the block of trials that are associated with one rewarded target location and that terminate with a “signal to change”. This signal to change was a flashing signal indicated the end of repetition and the beginning of a new problem (the new rewarded target had a 90% probability to be different from the target rewarded in the preceding problem). In 90% of problems, the repetition period lasted three trials after the first reward, whereas in 10% of problems, 7–11 repetitions could occur. Repetition trials beyond the third one were excluded from analysis to avoid possible surprise effects. At the time of recordings, the task was well known: monkeys omitted to repeat the correct touch in one of the trials following the discovery of the rewarded target in only around 1% of problems. Then, both the incorrect touch and the following trials were discarded from analysis, but previous trials were kept. As previously reported [41], monkeys might be able to infer the rewarded target after three non-redundant errors, i. e. , the third error would systematically trigger a switch to repetition. Therefore, only first and second erroneous touches as well as first rewards preceded by less than three errors were included in the analysis. For the repetition period, we selected all correct trials that followed a search with up to three preceding search errors. We assessed to what extent single-trial spike trains encoded enough information to discriminate between the following types of post-feedback task epochs: (a) after first reward versus after second, third, and fourth rewards, i. e. , “first reward discrimination; ” (b) after no reward versus after second, third, and fourth rewards, i. e. , “error discrimination. ” To compare different decoding schemes (i. e. , spike-count and timing-sensitive decoders), the spike-train metrics framework [13,35] was considered. Neuronal decoding relied on the assessment of the Victor and Purpura distance dq, k (s, s’) between two spike trains (s, s’), both within and between task epochs. One spike train s contained the spikes of one (Fig 2a) or two (Fig 2b) neurons during one task-epoch of a given trial. The distance dq, k (also named “dissimilarity” throughout the article) depended on two parameters, namely, the spike timing sensitivity q and, in the case of multiunit activity, the degree of distinction k between different neurons (Fig 2). Algorithmically, the distance dq, k between two spike trains was computed as the minimal cost to transform the first spike train into the second one, using three possible operations: adding or removing a spike, for a cost (Dmax/2) = 1; changing the timing of a spike by an amount dt, for a cost (q dt) ≤ Dmax, where Dmax = 2 is the maximum cost corresponding to removing a spike and re-inserting it at the right time; changing the identity of the neuron which fired the spike, for a cost k. Each spike train s was classified as belonging to the task epoch E that contained the most similar neuronal responses to s. The similarity between s and all discharges produced during E was quantified by computing a global dissimilarity to the ensemble of spike trains in task epoch E. The results presented in the main body were obtained by taking this global dissimilarity as the median of the pairwise distances between s and any other spike train of E (i. e. , median (dq (s, s' ) ) s' ∊E, s' ≠ s). Comparable results were found by biasing the global dissimilarity measure towards small spike train distances, i. e. , by “ignoring” more the very dissimilar spike trains during classification (“nearest-neighbor” classification, S5 Fig). The accuracy of the classification process was measured as the mutual information between the actual probabilities of dACC spike trains to belong to each task epoch and the probabilities predicted by the clustering method. More precisely, if we define a confusion matrix N, whose entries N (i, j) are the number of spike trains belonging to task epoch i and classified in task epoch j, the raw information was 1∑I, JNI, J∑i, jNi, j ln (Ni, j ∑I, JNI, J (∑kNi, k) (∑lNl, j) ). This measure accounts for the differences in number of trials between task epochs, it tends toward zero for chance-like predictions (for large data samples), and its maximum (for perfect prediction) depends on the relative proportion of trials between task epochs. As the number of trials was neuron dependent, each information value was normalized with respect to its maximum value. Full details on the use of spike-train metrics are in S1 Text. To test whether classification was above chance, trials were randomly permuted between task epochs, and two groups were recreated (with the same number of trials as the original task epoch clusters). The information content associated to the shuffled groups was then computed. The process was repeated 1,000 times, leading for each q or [q, k], to 1,000 values of information under the null hypothesis that the discrimination between groups is due to random similarities between any two spike trains. The information analysis was done on increasing time windows, starting 1 ms after the onset of the feedback (to avoid pump-driven artifacts). The first window lasted until 50 ms post-feedback, and was incrementally increased to 600 ms by 50 ms steps, and then up to 1 s by 100 ms steps. The higher resolution for smaller windows allowed the time course of the fast initial transient to be evaluated. We computed the maximum (over q or [q, k]) number Nw of consecutive windows for which the information was strictly larger than the 95th percentile of the 1,000 sets of permuted data. The same process was repeated for each set of permuted data, relative to the remaining 999 permuted sets. A neuron (or a pair) was considered as significant if Nw was strictly larger in the actual data than in 95% of permuted data. This process did not favor a given q or k, and could select neurons/pairs of neurons with different information time courses. Also, it allowed us to exclude neurons with very unreliable activity, which would act as “noise” during the subsequent analyses. The sample information bias was empirically estimated (for each q or [q, k] and each analysis window) as the mean information in the 1,000 permuted datasets [64], and was subtracted from the information in the original data (slightly negative corrected information were clipped at 0). Note that, throughout the paper, the term “information” refers to a bias-corrected and normalized-to-maximum value. After bias correction, for each q or [q, k] and for each significant neuron, the temporal evolution of information values was summarized by taking the mean information over 10 analysis windows of increasing lengths (ending from 100 ms to 1 s post-feedback onset, by steps of 100 ms, favoring neither early nor late information). We refer to this quantity as “time-averaged information” in the article, <I>t. Computing the time-averaged information is equivalent to averaging over delays before a decision is made by the animal. Finally, a non-parametric Friedman ANOVA was used to compare the time-averaged bias-corrected normalized information as a function of different q or [q, k], with Tukey' s honestly significant difference criterion correction for multiple comparisons. Note that our procedure is more powerful than simply counting the number of neurons that would reach a certain significance level separately for each q or [q, k]. Indeed, the latter method cannot be sensitive to differences in classification ability between two parameter sets that reach a certain significance level, and it is restricted to the use of large enough p-values (which can be evaluated with a reasonable number of permutations). We used spike-time shuffling to investigate to what extent random samples from a time-varying trial-averaged rate density (as in “Poisson” neurons with time-varying rate) could underlie the advantage of the temporal structure for decoding [35]. For each cell and each task epoch separately, we grouped all spikes emitted in the interval [0. 001,1] s post-feedback and randomly assigned each of them to a trial (repeated 1,000 times, Fig 5a). This procedure is equivalent to drawing the number of spikes in each trial from a Binomial distribution with parameters n = Nspikes and p=1Ntrials, which—following the common approximation—is close to a Poisson variable. Indeed, p was rather small (the trial number was usually big: 25th quantiles were 14. 25 and 51. 25 for first reward and repetition respectively); and the total number of spikes n was big (25th quantiles were 53. 75 and 175 for first reward and repetition, respectively). Under the Poisson approximation, spike counts restricted to sub-analysis windows are also Poisson (Raikov' s theorem). This allowed us to build the spike-shuffled data for smaller analysis windows by simple pruning of the 1,000 shuffled data of the largest window. We used a second shuffling procedure to test to what extent information transmission could be determined by time-varying firing rates and spike-count variability as in the original data (Fig 5d). In contrast to the previous shuffling method, this procedure considered that time-varying firing rate was modulated by a multiplicative factor. This factor constrained the spike-count variability to fit the original data and it was specific to each trial and time independent. This shuffling procedure not only conserved the PETH but also the number of spikes present in each trial. To do so, for each cell, each task epoch, each analysis window, and all 1,000 shufflings independently, we randomly permuted all available spikes before reassigning to each trial the exact same number of spikes as in original data (without replacement). Because both shuffling methods produced spike-shuffled data with the same number of trials as in the original data, the finite-sample information bias should be similar in both cases and should cancel when looking at the information difference, which was the relevant quantity. The bias was therefore not re-evaluated for this analysis. The response time was defined as the time between the “go” signal (for the hand movement) following the first reward, and the subsequent target touch. For this analysis, only neurons with significant first reward classification and with at least five available trials were used (or subgroups of this ensemble, see S12 Fig). For a given neuron, the dissimilarity between a recorded spike train s and the prototypical spike train sprototype emitted at first reward was estimated as the median of all pairwise distances d*q (s, s’), where s’ corresponds to all the recorded first reward spike trains that are different from s. We defined a new metrics d*q by dividing the Victor and Purpura distance dq by either the number of coincident spike pairs between s and s’, or 1 if no spikes were coincident. Note that we define two spikes to be coincident when their dissimilarity was smaller than Dmax = 2 (see Fig 2a). For q > 0, the distance d*q quantified the mean jitter of coincident spikes, plus a cost for unmatched spikes. For q = 0, d*q estimated the normalized absolute spike-count difference between the two spike trains. Importantly, d*q did not scale with the number of emitted spikes: a trial with too little spikes, as well as a trial with many spikes at inaccurate times, will have a large normalized distance to the prototype. We further discuss the rationale for designing d*q in S4 Text. Let r˜ denote the median value of observed response times, T+ be the set of first reward trials followed by a response time larger than r˜, and T- the set of trials followed by a response time lower than r˜. For each spike train s, we calculated the dissimilarity between s and prototypical first reward activity (i. e. median (dq* (s, s′) ) s′∈1st reward, s′≠s, similar to the spike train classification analysis). We then defined D¯T+ (D¯T−) as the mean over all s∊T+ (T-) of the dissimilarity between s and prototypical first reward activity. We finally computed the overall difference of deviation from the prototypical discharge at first reward as D¯=D¯T+−D¯T−. D¯ was computed for multiple time window lengths: from 100 ms to 1 s post-feedback time, by increments of 100 ms. Finally, a bias score b=∑positive biaswindows−log10 (psigned−rank (D¯) ) +∑negative biaswindowslog10 (psigned−rank (D¯) ) was computed, where the set of “positive bias windows” contained those analysis windows for which the sum of ranks for positive values was larger than the sum of ranks for negative values. Similarly, the “negative bias windows” were those with a sum of ranks for negative values larger than the sum of ranks for positive values. A positive (negative) bias in a given window would cause a corresponding increase (decrease) in b. To assess the significance of the bias score b, 1,000 surrogate datasets, in which the difference between high and low response time groups was eliminated, were compared to the real data. For each surrogate, and independently for each neuron, the sign of all D¯ values (for all analysis windows) had a 0. 5 probability to be changed. The p-value was computed as the proportion of surrogate datasets leading to higher or equal |b|as the real data. The temporal sensitivity q leading to best first reward discrimination in the population and q = 0 were compared (signed-rank test). To do so, we computed the mean values of D¯ over analysis windows ending from 100 ms to 1 s post-feedback time, by increments of 100 ms. Similar results were found when assessing the optimal q value by using either the original Victor and Purpura distance dq (main text), or the normalized distance d*q (S12 Fig and S4 Text). A similar analysis was done to test whether firing rates could also relate to response time. To do so, D¯ was replaced by the difference in mean firing rate between high and low response time trials, D¯rate. Table 1 summarizes an additional set of employed statistical measures. The latter were often non-normal; therefore, non-parametric tests were considered (p ≤ 0. 05 was considered as statistically significant): (1) correlations were assessed with Spearman coefficient with a permutation test (or big sample approximation); (2) distributions were compared with the 2-sided Kolmogorov-Smirnov test; (3) central tendencies were compared between 2 unpaired (resp. paired) distributions with the 2-sided ranked-sum (signed-rank) test; (4) deviation of distributions from 0-centered-symmetry was also tested with the 2-sided signed-rank test. When testing pairs of units, one limitation was that some pairs happened to share a neuron, and hence were correlated (in particular if non-shared neurons were discharging significantly less than the shared one). This was problematic for analyzing the optimal temporal sensitivity, which is not a parameter accounting for the interaction between neurons, and which can be impacted more by the neuron which fires the most. We therefore verified that the significance of the advantage of the temporal sensitivity during paired decoding could be reached without overlapping pairs (positivity of maxk (〈I (q=10s−1) 〉t) −maxk (〈I (q=0s−1) 〉t), signed-rank test, p ≤ 0. 05 in 1,000/1,000 random down-samplings to non-overlapping pairs). Note that, in contrast, interaction parameters such as the information gain or kopt are truly pair specific, implying that it was reasonable to keep overlapping pairs for the analysis. Note that although most statistical tests presented in the main results were carried out by pooling data from both monkeys, consistent trends were observed for both individuals.
In classical views of how information is processed in the brain, cognitive areas are often thought to encode incoming signals by a simple summation of spikes—action potentials fired by neurons and transmitted along the nerves—elicited by different cues. It is through this summation of spikes that cognitive areas are hypothesized to combine information from different cues and build memories. We investigated whether summation is relevant during the processing of signals emitted by the dorsal anterior cingulate cortex, a brain area which is thought to control behavioral adaptation in response to feedback cues that indicate the animal’s performance during a task. We found that a mere summation of spikes emitted in response to feedback cues actually extracts significantly less information compared to a code that also takes into account the timing of the spikes. Furthermore, we discovered that this temporal structure of the spike discharges predicts well the future behavior of monkeys. Overall, our findings suggest that the brain areas processing the signals emitted by the dorsal anterior cingulate cortex are sensitive to spike times and, thus, are unlikely to implement a mere approximate summation of inputs.
Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex