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Interval Tuning in the Primate Medial Premotor Cortex as a General Timing Mechanism

The precise quantification of time during motor performance is critical for many complex behaviors, including musical execution, speech articulation, and sports; however, its neural mechanisms are primarily unknown. We found that neurons in the medial premotor cortex (MPC) of behaving monkeys are tuned to the duration of produced intervals during rhythmic tapping tasks. Interval-tuned neurons showed similar preferred intervals across tapping behaviors that varied in the number of produced intervals and the modality used to drive temporal processing. In addition, we found that the same population of neurons is able to multiplex the ordinal structure of a sequence of rhythmic movements and a wide range of durations in the range of hundreds of milliseconds. Our results also revealed a possible gain mechanism for encoding the total number of intervals in a sequence of temporalized movements, where interval-tuned cells show a multiplicative effect of their activity for longer sequences of intervals. These data suggest that MPC is part of a core timing network that uses interval tuning as a signal to represent temporal processing in a variety of behavioral contexts where time is explicitly quantified.

Measuring time with different neural chronometers during a synchronization-continuation task

Temporal information processing is critical for many complex behaviors including speech and music cognition, yet its neural substrate remains elusive. We examined the neurophysiological properties of medial premotor cortex (MPC) of two Rhesus monkeys during the execution of a synchronization-continuation tapping task that includes the basic sensorimotor components of a variety of rhythmic behaviors. We show that time-keeping in the MPC is governed by separate cell populations. One group encoded the time remaining for an action, showing activity whose duration changed as a function of interval duration, reaching a peak at similar magnitudes and times with respect to the movement. The other cell group showed a response that increased in duration or magnitude as a function of the elapsed time from the last movement. Hence, the sensorimotor loops engaged during the task may depend on the cyclic interplay between different neuronal chronometers that quantify the time passed and the remaining time for an action.

Sensorimotor neural dynamics during isochronous tapping in the medial premotor cortex of the macaque.

We determined the response properties of neurons in the primate medial premotor cortex that were classified as sensory or motor during isochronous tapping to a visual or auditory metronome, using different target intervals and three sequential elements in the task. The cell classification was based on a warping transformation, which determined whether the cell activity was statistically aligned to sensory or motor events, finding a large proportion of cells classified as sensory or motor. Two distinctive clusters of sensory cells were observed, i.e. one cell population with short response‐onset latencies to the previous stimulus, and another that was probably predicting the occurrence of the next stimuli. These cells were called sensory‐driven and stimulus‐predicting neurons, respectively. Sensory‐driven neurons showed a clear bias towards the visual modality and were more responsive to the first stimulus, with a decrease in activity for the following sequential elements of the metronome. In contrast, stimulus‐predicting neurons were bimodal and showed similar response profiles across serial‐order elements. Motor cells showed a consecutive activity onset across discrete neural ensembles, generating a rapid succession of activation patterns between the two taps defining a produced interval. The cyclical configuration in activation profiles engaged more motor cells as the serial‐order elements progressed across the task, and the rate of cell recruitment over time decreased as a function of the target interval. Our findings support the idea that motor cells were responsible for the rhythmic progression of taps in the task, gaining more importance as the trial advanced, while, simultaneously, the sensory‐driven cells lost their functional impact.

When Are New Hippocampal Neurons, Born in the Adult Brain, Integrated into the Network That Processes Spatial Information?

Adult-born neurons in the dentate gyrus (DG) functionally integrate into the behaviorally relevant hippocampal networks, showing a specific Arc-expression response to spatial exploration when mature. However, it is not clear when, during the 4- to 6-week interval that is critical for survival and maturation of these neurons, this specific response develops. Therefore, we characterized Arc expression after spatial exploration or cage control conditions in adult-born neurons from rats that were injected with BrdU on one day and were sacrificed 1, 7, 15, 30, and 45 days post-BrdU injection (PBI). Triple immunostaining for NeuN, Arc, and BrdU was analyzed through the different DG layers. Arc protein expression in BrdU-positive cells was observed from day 1 to day 15 PBI but was not related to behavioral stimulation. The specific Arc-expression response to spatial exploration was observed from day 30 and 45 in about 5% of the BrdU-positive cell population. Most of the BrdU-positive neurons expressing Arc in response to spatial exploration (∼90%) were located in DG layer 1, and no Arc expression was observed in cells located in the subgranular zone (SGZ). Using the current data and that obtained previously, we propose a mathematical model suggesting that new neurons are unlikely to respond to exploration by expressing Arc after they are 301 days old, and also that in a 7-month-old rat the majority (60%) of the neurons that respond to exploration must have been born during adulthood; thus, suggesting that adult neurogenesis in the DG is highly relevant for spatial information processing.

Behavioral and Neurophysiological Aspects of Target Interception

This chapter focuses on the behavioral and neurophysiological aspects of manual interception. We review the most important elements of an interceptive action from the sensory and cognitive stage to the motor side of this behavior. We describe different spatial and temporal target parameters that can be used to control the interception movement, as well as the different strategies used by the subject to intercept a moving target. We review the neurophysiological properties of the parietofrontal system during target motion processing and during a particular experiment of target interception. Finally, we describe the neural responses associated with the temporal and spatial parameters of a moving target and the possible neurophysiological mechanisms used to integrate this information in order to trigger an interception movement.

Trial time warping to discriminate stimulus-related from movement-related neural activity

In tasks where different sensory, cognitive, and motor events are mixed in a sequence it is difficult to determine whether neural activity is related to any behavioral parameter. Here, we consider the case in which two alternative trial-alignment schemes correspond to two different neural representations, one stimulus-related and the other movement-related, using both simulations of neural activity and real recordings in the medial premotor areas during a multiple-interval tapping task called synchronization-continuation task (SCT). To discover whether neural responses are better aligned to sensory or motor events we introduce a family of trial-alignment time-warping functions indexed by a single parameter such that when the parameter takes the value 0 the trials are aligned to the stimulus and when the parameter takes the value 1 they are aligned to the movement. We then characterize neurons by the best-fitting alignment scheme (in the sense of maximum likelihood) under the assumption that the correct alignment would produce homogeneous trials without excess trial-to-trial variation. We use Bayes factors to determine the evidence in favor of sensory or motor neural alignments. The simulations revealed that the variability in neural responses and sequential motor outputs are key parameters to obtain appropriate warping results. In addition, the analysis on the activity of 500 neurons in the medial premotor areas of monkeys executing the SCT showed that most of the neural responses (54.2%) were aligned to the tapping movements instead of the stimuli used to drive the temporal behavior.

Linking Perception, Cognition, and Action: Psychophysical Observations and Neural Network Modelling

It has been argued that perception, decision making, and movement planning are in reality tightly interwoven brain processes. However, how they are implemented in neural circuits is still a matter of debate. We tested human subjects in a temporal categorization task in which intervals had to be categorized as short or long. Subjects communicated their decision by moving a cursor into one of two possible targets, which appeared separated by different angles from trial to trial. Even though there was a 1 second-long delay between interval presentation and decision communication, categorization difficulty affected subjects’ performance, reaction (RT) and movement time (MT). In addition, reaction and movement times were also influenced by the distance between the targets. This implies that not only perceptual, but also movement-related considerations were incorporated into the decision process. Therefore, we searched for a model that could use categorization difficulty and target separation to describe subjects’ performance, RT, and MT. We developed a network consisting of two mutually inhibiting neural populations, each tuned to one of the possible categories and composed of an accumulation and a memory node. This network sequentially acquired interval information, maintained it in working memory and was then attracted to one of two possible states, corresponding to a categorical decision. It faithfully replicated subjects’ RT and MT as a function of categorization difficulty and target distance; it also replicated performance as a function of categorization difficulty. Furthermore, this model was used to make new predictions about the effect of untested durations, target distances and delay durations. To our knowledge, this is the first biologically plausible model that has been proposed to account for decision making and communication by integrating both sensory and motor planning information.

Neurophysiology of Timing in the Hundreds of Milliseconds: Multiple Layers of Neuronal Clocks in the Medial Premotor Areas

The precise quantification of time in the subsecond scale is critical for many complex behaviors including music and dance appreciation/execution, speech comprehension/articulation, and the performance of many sports. Nevertheless, its neural underpinnings are largely unknown. Recent neurophysiological experiments from our laboratory have shown that the cell activity in the medial premotor areas (MPC) of macaques can represent different aspects of temporal processing during a synchronization-continuation tapping task (SCT). In this task the rhythmic behavior of monkeys was synchronized to a metronome of isochronous stimuli in the hundreds of milliseconds range (synchronization phase), followed by a period where animals internally temporalized their movements (continuation phase). Overall, we found that the time-keeping mechanism in MPC is governed by different layers of neural clocks. Close to the temporal control of movements are two separate populations of ramping cells that code for elapsed or remaining time for a tapping movement during the SCT. Thus, the sensorimotor loops engaged during the task may depend on the cyclic interplay between two neuronal chronometers that quantify in their instantaneous discharge rate the time passed and the remaining time for an action. In addition, we found MPC neurons that are tuned to the duration of produced intervals during the rhythmic task, showing an orderly variation in the average discharge rate as a function of duration. All the tested durations in the subsecond scale were represented in the preferred intervals of the cell population. Most of the interval-tuned cells were also tuned to the ordinal structure of the six intervals produced sequentially in the SCT. Hence, this next level of temporal processing may work as the notes of a musical score, providing information to the timing network about what duration and ordinal element of the sequence are being executed. Finally, we describe how the timing circuit can use a dynamic neural representation of the passage of time and the context in which the intervals are executed by integrating the time-varying activity of populations of cells. These neural population clocks can be defined as distinct trajectories in the multidimensional cell response-space. We provide a hypothesis of how these different levels of neural clocks can interact to constitute a coherent timing machine that controls the rhythmic behavior during the SCT.

Neural basis for categorical boundaries in the primate pre-SMA during relative categorization of time intervals

Perceptual categorization depends on the assignment of different stimuli to specific groups based, in principle, on the notion of flexible categorical boundaries. To determine the neural basis of categorical boundaries, we record the activity of pre-SMA neurons of monkeys executing an interval categorization task in which the limit between short and long categories changes between blocks of trials within a session. A large population of cells encodes this boundary by reaching a constant peak of activity close to the corresponding subjective limit. Notably, the time at which this peak is reached changes according to the categorical boundary of the current block, predicting the monkeys’ categorical decision on a trial-by-trial basis. In addition, pre-SMA cells also represent the category selected by the monkeys and the outcome of the decision. These results suggest that the pre-SMA adaptively encodes subjective duration boundaries between short and long durations and contains crucial neural information to categorize intervals and evaluate the outcome of such perceptual decisions.Grouping stimuli into categories often depends on a subjective determination of category boundaries. Here the authors report a neuronal population in pre-supplementary motor area whose peak activity predicts the categorical decision boundary between long and short time intervals on a trial-by-trial basis.

The Synaptic Properties of Cells Define the Hallmarks of Interval Timing in a Recurrent Neural Network

Extensive research has described two key features of interval timing. The bias property is associated with accuracy and implies that time is overestimated for short intervals and underestimated for long intervals. The scalar property is linked to precision and states that the variability of interval estimates increases as a function of interval duration. The neural mechanisms behind these properties are not well understood. Here we implemented a recurrent neural network that mimics a cortical ensemble and includes cells that show paired-pulse facilitation and slow inhibitory synaptic currents. The network produces interval selective responses and reproduces both bias and scalar properties when a Bayesian decoder reads its activity. Notably, the interval-selectivity, timing accuracy, and precision of the network showed complex changes as a function of the decay time constants of the modeled synaptic properties and the level of background activity of the cells. These findings suggest that physiological values of the time constants for paired-pulse facilitation and GABAb, as well as the internal state of the network, determine the bias and scalar properties of interval timing. SIGNIFICANCE STATEMENT Timing is a fundamental element of complex behavior, including music and language. Temporal processing in a wide variety of contexts shows two primary features: time estimates exhibit a shift toward the mean (the bias property) and are more variable for longer intervals (the scalar property). We implemented a recurrent neural network that includes long-lasting synaptic currents, which cannot only produce interval-selective responses but also follow the bias and scalar properties. Interestingly, only physiological values of the time constants for paired-pulse facilitation and GABAb, as well as intermediate background activity within the network can reproduce the two key features of interval timing.