Neural correlates of interval timing in rodent prefrontal cortex

Frontal Norepinephrine Represents a Threat Prediction Error Under Uncertainty

BACKGROUND

To adapt to threats in the environment, animals must predict them and engage in defensive behavior. While the representation of a prediction error signal for reward has been linked to dopamine, a neuromodulatory prediction error for aversive learning has not been identified.

METHODS

We measured and manipulated norepinephrine release during threat learning using optogenetics and a novel fluorescent norepinephrine sensor.

RESULTS

We found that norepinephrine response to conditioned stimuli reflects aversive memory strength. When delays between auditory stimuli and footshock are introduced, norepinephrine acts as a prediction error signal. However, temporal difference prediction errors do not fully explain norepinephrine dynamics. To explain noradrenergic signaling, we used an updated reinforcement learning model with uncertainty about time and found that it explained norepinephrine dynamics across learning and variations in temporal and auditory task structure.

CONCLUSIONS

Norepinephrine thus combines cognitive and affective information into a predictive signal and links time with the anticipation of danger.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Mental navigation in the primate entorhinal cortex

A cognitive map is a suitably structured representation that enables novel computations using previous experience; for example, planning a new route in a familiar space1. Work in mammals has found direct evidence for such representations in the presence of exogenous sensory inputs in both spatial2,3 and non-spatial domains4-10. Here we tested a foundational postulate of the original cognitive map theory1,11: that cognitive maps support endogenous computations without external input. We recorded from the entorhinal cortex of monkeys in a mental navigation task that required the monkeys to use a joystick to produce one-dimensional vectors between pairs of visual landmarks without seeing the intermediate landmarks. The ability of the monkeys to perform the task and generalize to new pairs indicated that they relied on a structured representation of the landmarks. Task-modulated neurons exhibited periodicity and ramping that matched the temporal structure of the landmarks and showed signatures of continuous attractor networks12,13. A continuous attractor network model of path integration14 augmented with a Hebbian-like learning mechanism provided an explanation of how the system could endogenously recall landmarks. The model also made an unexpected prediction that endogenous landmarks transiently slow path integration, reset the dynamics and thereby reduce variability. This prediction was borne out in a reanalysis of firing rate variability and behaviour. Our findings link the structured patterns of activity in the entorhinal cortex to the endogenous recruitment of a cognitive map during mental navigation.

Insular and prelimbic cortices control behavioral accuracy and precision in a temporal decision-making task in rats

The anterior insular cortex (AIC) comprises a region of sensory integration. It appears to detect salient events in order to guide goal-directed behavior, code tracking errors, and estimate the passage of time. Temporal processing in the AIC may be instantiated by the integration of representations of interoception. Projections between the AIC and the medial prefrontal cortex (mPFC) - found both in rats and humans - also suggest a possible role for these structures in the integration of autonomic responses during ongoing behavior. Few studies, however, have investigated the role of AIC and mPFC in decision-making and time estimation tasks. Moreover, their findings are not consistent, so the relationship between temporal decision-making and those areas remains unclear. The present study employed bilateral inactivations to explore the role of AIC and prelimbic cortex (PL) in rats during a temporal decision-making task. In this task, two levers are available simultaneously (but only one is active), one predicting reinforcement after a short, and the other after a long-fixed interval. Optimal performance requires a switch from the short to the long lever after the short-fixed interval elapsed and no reinforcement was delivered. Switch behavior from the short to the long lever was dependent on AIC and PL. During AIC inactivation, switch latencies became more variable, while during PL inactivation switch latencies became both more variable and less accurate. These findings point to a dissociation between AIC and PL in temporal decision-making, suggesting that the AIC is important for temporal precision, and PL is important for both temporal accuracy and precision.

Ramping cells in rodent mPFC encode time to past and future events via real Laplace transform

In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from rodent mPFC (Henke et al., 2021) during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior

Contextual cues and prior evidence guide human goal-directed behavior. The neurophysiological mechanisms that implement contextual priors to guide subsequent actions in the human brain remain unclear. Using intracranial electroencephalography (iEEG), we demonstrate that increasing uncertainty introduces a shift from a purely oscillatory to a mixed processing regime with an additional ramping component. Oscillatory and ramping dynamics reflect dissociable signatures, which likely differentially contribute to the encoding and transfer of different cognitive variables in a cue-guided motor task. The results support the idea that prefrontal activity encodes rules and ensuing actions in distinct coding subspaces, while theta oscillations synchronize the prefrontal-motor network, possibly to guide action execution. Collectively, our results reveal how two key features of large-scale neural population activity, namely continuous ramping dynamics and oscillatory synchrony, jointly support rule-guided human behavior.

A Second Introduction to the Neurobiology of Interval Timing

Time is a critical variable that organisms must be able to measure in order to survive in a constantly changing environment. Initially, this paper describes the myriad of contexts where time is estimated or predicted and suggests that timing is not a single process and probably depends on a set of different neural mechanisms. Consistent with this hypothesis, the explosion of neurophysiological and imaging studies in the last 10 years suggests that different brain circuits and neural mechanisms are involved in the ability to tell and use time to control behavior across contexts. Then, we develop a conceptual framework that defines time as a family of different phenomena and propose a taxonomy with sensory, perceptual, motor, and sensorimotor timing as the pillars of temporal processing in the range of hundreds of milliseconds.

The Interactions of Temporal and Sensory Representations in the Basal Ganglia

In rodents and primates, interval estimation has been associated with a complex network of cortical and subcortical structures where the dorsal striatum plays a paramount role. Diverse evidence ranging from individual neurons to population activity has demonstrated that this area hosts temporal-related neural representations that may be instrumental for the perception and production of time intervals. However, little is known about how temporal representations interact with other well-known striatal representations, such as kinematic parameters of movements or somatosensory representations. An attractive hypothesis suggests that somatosensory representations may serve as the scaffold for complex representations such as elapsed time. Alternatively, these representations may coexist as independent streams of information that could be integrated into downstream nuclei, such as the substantia nigra or the globus pallidus. In this review, we will revise the available information suggesting an instrumental role of sensory representations in the construction of temporal representations at population and single-neuron levels throughout the basal ganglia.

Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees

Extracting temporal regularities and relations from experience/observation is critical for organisms' adaptiveness (communication, foraging, predation, prediction) in their ecological niches. Therefore, it is not surprising that the internal clock that enables the perception of seconds-to-minutes-long intervals (interval timing) is evolutionarily well-preserved across many species of animals. This comparative claim is primarily supported by the fact that the timing behavior of many vertebrates exhibits common statistical signatures (e.g., on-average accuracy, scalar variability, positive skew). These ubiquitous statistical features of timing behaviors serve as empirical benchmarks for modelers in their efforts to unravel the processing dynamics of the internal clock (namely answering how internal clock "ticks"). In this chapter, we introduce prominent (neuro)computational approaches to modeling interval timing at a level that can be understood by general audience. These models include Treisman's pacemaker accumulator model, the information processing variant of scalar expectancy theory, the striatal beat frequency model, behavioral expectancy theory, the learning to time model, the time-adaptive opponent Poisson drift-diffusion model, time cell models, and neural trajectory models. Crucially, we discuss these models within an overarching conceptual framework that categorizes different models as threshold vs. clock-adaptive models and as dedicated clock/ramping vs. emergent time/population code models.

Diverse Time Encoding Strategies Within the Medial Premotor Areas of the Primate

The measurement of time in the subsecond scale is critical for many sophisticated behaviors, yet its neural underpinnings are largely unknown. Recent neurophysiological experiments from our laboratory have shown that the neural activity in the medial premotor areas (MPC) of macaques can represent different aspects of temporal processing. During single interval categorization, we found that preSMA encodes a subjective category limit by reaching a peak of activity at a time that divides the set of test intervals into short and long. We also observed neural signals associated with the category selected by the subjects and the reward outcomes of the perceptual decision. On the other hand, we have studied the behavioral and neurophysiological basis of rhythmic timing. First, we have shown in different tapping tasks that macaques are able to produce predictively and accurately intervals that are cued by auditory or visual metronomes or when intervals are produced internally without sensory guidance. In addition, we found that the rhythmic timing mechanism in MPC is governed by different layers of neural clocks. Next, the instantaneous activity of single cells shows ramping activity that encodes the elapsed or remaining time for a tapping movement. In addition, we found MPC neurons that build neural sequences, forming dynamic patterns of activation that flexibly cover all the produced interval depending on the tapping tempo. This rhythmic neural clock resets on every interval providing an internal representation of pulse. Furthermore, the MPC cells show mixed selectivity, encoding not only elapsed time, but also the tempo of the tapping and the serial order element in the rhythmic sequence. Hence, MPC can map different task parameters, including the passage of time, using different cell populations. Finally, the projection of the time varying activity of MPC hundreds of cells into a low dimensional state space showed circular neural trajectories whose geometry represented the internal pulse and the tapping tempo. Overall, these findings support the notion that MPC is part of the core timing mechanism for both single interval and rhythmic timing, using neural clocks with different encoding principles, probably to flexibly encode and mix the timing representation with other task parameters.

Temporal encoding in deep reinforcement learning agents

Neuroscientists have observed both cells in the brain that fire at specific points in time, known as "time cells", and cells whose activity steadily increases or decreases over time, known as "ramping cells". It is speculated that time and ramping cells support temporal computations in the brain and carry mnemonic information. However, due to the limitations in animal experiments, it is difficult to determine how these cells really contribute to behavior. Here, we show that time cells and ramping cells naturally emerge in the recurrent neural networks of deep reinforcement learning models performing simulated interval timing and working memory tasks, which have learned to estimate expected rewards in the future. We show that these cells do indeed carry information about time and items stored in working memory, but they contribute to behavior in large part by providing a dynamic representation on which policy can be computed. Moreover, the information that they do carry depends on both the task demands and the variables provided to the models. Our results suggest that time cells and ramping cells could contribute to temporal and mnemonic calculations, but the way in which they do so may be complex and unintuitive to human observers.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

Using temperature to analyze the neural basis of a time-based decision

The basal ganglia are thought to contribute to decision-making and motor control. These functions are critically dependent on timing information, which can be extracted from the evolving state of neural populations in their main input structure, the striatum. However, it is debated whether striatal activity underlies latent, dynamic decision processes or kinematics of overt movement. Here, we measured the impact of temperature on striatal population activity and the behavior of rats, and compared the observed effects with neural activity and behavior collected in multiple versions of a temporal categorization task. Cooling caused dilation, and warming contraction, of both neural activity and patterns of judgment in time, mimicking endogenous decision-related variability in striatal activity. However, temperature did not similarly affect movement kinematics. These data provide compelling evidence that the timecourse of evolving striatal activity dictates the speed of a latent process that is used to guide choices, but not continuous motor control. More broadly, they establish temporal scaling of population activity as a likely neural basis for variability in timing behavior.

Multiplexing working memory and time in the trajectories of neural networks

Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

Influence of Recent Trial History on Interval Timing

Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making. While it has been shown that time estimation is adaptive to the temporal context, it remains unclear how interval timing behavior is influenced by recent trial history. Here we found that, in mice trained to perform a licking-based interval timing task, a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier. Optogenetic inactivation of the anterior lateral motor cortex (ALM), but not the medial prefrontal cortex, for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial. Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling. Thus, interval timing is adaptive to recent experience of the temporal interval, and ALM activity during time estimation reflects recent experience of interval.

The neural representation of time distributed across multiple brain regions differs between implicit and explicit time demands

Animals appear to possess an internal timer during action, based on the passage of time. However, the neural underpinnings of the perception of time, ranging from seconds to minutes, remain unclear. Herein, we considered the neural representation of time based on mounting evidence on the neural correlates of time perception. The passage of time in the brain is represented by two types of neural encoding as follows: (i) the modulation of firing rates in single neurons and (ii) the sequential activity in neural ensembles. Time-dependent neural activity reflects the relative time rather than the absolute time, similar to a clock. They emerge in multiple regions, including the hippocampus, medial and lateral entorhinal cortices, medial prefrontal cortex, and dorsal striatum. Moreover, they involve different brain regions, depending on an implicit or explicit event duration. Thus, the two types of internal timers distributed across multiple brain regions simultaneously engage in time perception, in response to implicit or explicit time demands.

Time Perception in Adult ADHD: Findings from a Decade-A Review

Time perception is impaired in adult ADHD. Since the term time perception subsumes different constructs, including time estimation, time reproduction, time production, and duration discrimination, it remains open whether certain domains are more affected than other domains in adult ADHD. The aim of this explorative review is to present the current state of research on time perception in adult ADHD by analysing studies from the past 10 years. A review of the literature addressing adult ADHD time perception, time estimation, and time reproduction was performed. The search strategy was conducted by using the databases "PubMed", "Medline", and "PSYNDEX". The results of the present review indicate that the number of studies on time perception in adult ADHD is very scarce. Moreover, the main investigated domains of time perception in the past decade were time estimation, time reproduction and time management. Whereas some of the found studies were able to demonstrate a distinct deficit in time estimation, time reproduction and time management other studies were unable to demonstrate a clear association between ADHD and time estimation and time reproduction deficits. However, the diagnostic protocols, study design, and methodology varied between studies. Further studies on time estimation and time reproduction need to be carried out.

A Recurrent Neural Network Model Accounts for Both Timing and Working Memory Components of an Interval Discrimination Task

Interval discrimination is of fundamental importance to many forms of sensory processing, including speech and music. Standard interval discrimination tasks require comparing two intervals separated in time, and thus include both working memory (WM) and timing components. Models of interval discrimination invoke separate circuits for the timing and WM components. Here we examine if, in principle, the same recurrent neural network can implement both. Using human psychophysics, we first explored the role of the WM component by varying the interstimulus delay. Consistent with previous studies, discrimination was significantly worse for a 250 ms delay, compared to 750 and 1500 ms delays, suggesting that the first interval is stably stored in WM for longer delays. We next successfully trained a recurrent neural network (RNN) on the task, demonstrating that the same network can implement both the timing and WM components. Many units in the RNN were tuned to specific intervals during the sensory epoch, and others encoded the first interval during the delay period. Overall, the encoding strategy was consistent with the notion of mixed selectivity. Units generally encoded more interval information during the sensory epoch than in the delay period, reflecting categorical encoding of short versus long in WM, rather than encoding of the specific interval. Our results demonstrate that, in contrast to standard models of interval discrimination that invoke a separate memory module, the same network can, in principle, solve the timing, WM, and comparison components of an interval discrimination task.

Functional architecture of executive control and associated event-related potentials in macaques

The medial frontal cortex (MFC) enables executive control by monitoring relevant information and using it to adapt behavior. In macaques performing a saccade countermanding (stop-signal) task, we simultaneously recorded electrical potentials over MFC and neural spiking across all layers of the supplementary eye field (SEF). We report the laminar organization of neurons enabling executive control by monitoring the conflict between incompatible responses, the timing of events, and sustaining goal maintenance. These neurons were a mix of narrow-spiking and broad-spiking found in all layers, but those predicting the duration of control and sustaining the task goal until the release of operant control were more commonly narrow-spiking neurons confined to layers 2 and 3 (L2/3). We complement these results with evidence for a monkey homolog of the N2/P3 event-related potential (ERP) complex associated with response inhibition. N2 polarization varied with error-likelihood and P3 polarization varied with the duration of expected control. The amplitude of the N2 and P3 were predicted by the spike rate of different classes of neurons located in L2/3 but not L5/6. These findings reveal features of the cortical microcircuitry supporting executive control and producing associated ERPs.

Neural population clocks: Encoding time in dynamic patterns of neural activity

The ability to predict and prepare for near- and far-future events is among the most fundamental computations the brain performs. Because of the importance of time for prediction and sensorimotor processing, the brain has evolved multiple mechanisms to tell and encode time across scales ranging from microseconds to days and beyond. Converging experimental and computational data indicate that, on the scale of seconds, timing relies on diverse neural mechanisms distributed across different brain areas. Among the different encoding mechanisms on the scale of seconds, we distinguish between neural population clocks and ramping activity as distinct strategies to encode time. One instance of neural population clocks, neural sequences, represents in some ways an optimal and flexible dynamic regime for the encoding of time. Specifically, neural sequences comprise a high-dimensional representation that can be used by downstream areas to flexibly generate arbitrarily simple and complex output patterns using biologically plausible learning rules. We propose that high-level integration areas may use high-dimensional dynamics such as neural sequences to encode time, providing downstream areas information to build low-dimensional ramp-like activity that can drive movements and temporal expectation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Time encoding migrates from prefrontal cortex to dorsal striatum during learning of a self-timed response duration task

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.

The neural bases for timing of durations

Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future ('prospective timing') and durations that start in the past and end either in the past or the present ('retrospective timing'). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration 'estimation' are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.

Perceptual confidence of visual stimulus features is associated with duration perception

It has been shown that the perceived duration of an object in the subsecond range is closely associated with its nontemporal perceptual properties, the mechanism under which remains unclear. Previous studies have revealed a modulatory effect of early visual feature processing on the apparent duration. Here, we further examined the relationship between perceptual confidence and subjective time by asking participants to simultaneously perform temporal and nontemporal perceptual judgments. The results revealed a significant effect on confidence levels. When participants' confidence in judging the coherent motion direction or relative dot numerosity increases, their perceived duration of the stimulus also appears longer. These results are discussed in the context of perceptual evidence accumulation and evaluation for the decision-making of perceptual properties. They suggest a profound contribution of object processing to the computation of subjective time and provide further insights into the mechanism of event timing.

Dissociable Roles of Theta and Alpha in Sub-Second and Supra-Second Time Reproduction: An Investigation of their Links to Depression and Anxiety

A growing collection of observations has demonstrated the presence of multiple neural oscillations participating in human temporal cognition and psychiatric pathologies such as depression and anxiety. However, there remains a gap in the literature regarding the specific roles of these neural oscillations during interval timing, and how these oscillatory activities might vary with the different levels of mental health. The current study examined the participation of the frontal midline theta and occipital alpha oscillations, both of which are prevalent cortical oscillatory markers frequently reported in working memory and time perception paradigms. Participants performed a time reproduction task in the sub- (400, 600, 800 ms) and supra-second timescales (1600, 1800, 2000 ms) while undergoing scalp EEG recordings. Anxiety and depression levels were measured via self-report mental health inventories. Time–frequency analysis of scalp EEG revealed that both frontal midline and occipital alpha oscillations were engaged during the encoding of the durations. Furthermore, we observed that the correlational relationship between frontal midline theta power and the reproduction performance in the sub-second range was modulated by state anxiety. In contrast, the correlational relationship between occipital alpha and the reproduction performance of supra-second intervals was modulated by depression and trait anxiety. The results offer insights on how alpha and theta oscillations differentially play a role in interval timing and how mental health further differentially relates these neural oscillations to sub- and supra-second timescales.

Complementary representations of time in the prefrontal cortex and hippocampus

Episodic memory binds the spatial and temporal relationships between the elements of experience. The hippocampus encodes space through place cells that fire at specific spatial locations. Similarly, time cells fire sequentially at specific time points within a temporally organized experience. Recent studies in rodents, monkeys, and humans have identified time cells with discrete firing fields and cells with monotonically changing activity in supporting the temporal organization of events across multiple timescales. Using in vivo electrophysiological tetrode recordings, we simultaneously recorded neurons from the prefrontal cortex and dorsal CA1 of the hippocampus while rats performed a delayed match to sample task. During the treadmill mnemonic delay, hippocampal time cells exhibited sparser firing fields with decreasing resolution over time, consistent with previous results. In comparison, temporally modulated cells in the prefrontal cortex showed more monotonically changing firing rates, ramping up or decaying with the passage of time, and exhibited greater temporal precision for Bayesian decoding of time at long time lags. These time cells show exquisite temporal resolution both in their firing fields and in the fine timing of spikes relative to the phase of theta oscillations. Here, we report evidence of theta phase precession in both the prefrontal cortex and hippocampus during the temporal delay, however, hippocampal cells exhibited steeper phase precession slopes and more punctate time fields. To disentangle whether time cell activity reflects elapsed time or distance traveled, we varied the treadmill running speed on each trial. While many neurons contained multiplexed representations of time and distance, both regions were more strongly influenced by time than distance. Overall, these results demonstrate the flexible integration of spatiotemporal dimensions and reveal complementary representations of time in the prefrontal cortex and hippocampus in supporting memory-guided behavior.

Encoding time in neural dynamic regimes with distinct computational tradeoffs

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

The ability to tell time and anticipate when external events will occur are among the most fundamental computations the brain performs. Converging evidence suggests the brain encodes time through changing patterns of neural activity. Different temporal tasks, however, have distinct computational requirements, such as the need to flexibly scale temporal patterns or generalize to novel inputs. To understand how networks can encode time and satisfy different computational requirements we trained recurrent neural networks (RNNs) on two timing tasks that have previously been used in behavioral studies. Both tasks required producing identically timed output patterns. Using a novel framework to quantify how networks encode different intervals, we found that similar patterns of neural activity—neural sequences—were associated with fundamentally different underlying mechanisms, including the connectivity patterns of the RNNs. Critically, depending on the task the RNNs were trained on, they were better suited for generalization or robustness to noise. Our results predict that similar patterns of neural activity can be produced by distinct RNN configurations, which in turn have fundamentally different computational tradeoffs. Our results also predict that differences in task structure account for some of the experimentally observed variability in how networks encode time.

Distributed coding of duration in rodent prefrontal cortex during time reproduction

As we interact with the external world, we judge magnitudes from sensory information. The estimation of magnitudes has been characterized in primates, yet it is largely unexplored in nonprimate species. Here, we use time interval reproduction to study rodent behavior and its neural correlates in the context of magnitude estimation. We show that gerbils display primate-like magnitude estimation characteristics in time reproduction. Most prominently their behavioral responses show a systematic overestimation of small stimuli and an underestimation of large stimuli, often referred to as regression effect. We investigated the underlying neural mechanisms by recording from medial prefrontal cortex and show that the majority of neurons respond either during the measurement or the reproduction of a time interval. Cells that are active during both phases display distinct response patterns. We categorize the neural responses into multiple types and demonstrate that only populations with mixed responses can encode the bias of the regression effect. These results help unveil the organizing neural principles of time reproduction and perhaps magnitude estimation in general.

Cortical alpha-synuclein preformed fibrils do not affect interval timing in mice

One hallmark feature of Parkinson's disease (PD) is Lewy body pathology associated with misfolded alpha-synuclein. Previous studies have shown that striatal injection of alpha-synuclein preformed fibrils (PFF) can induce misfolding and aggregation of native alpha-synuclein in a prion-like manner, leading to cell death and motor dysfunction in mouse models. Here, we tested whether alpha-synuclein PFFs injected into the medial prefrontal cortex results in deficits in interval timing, a cognitive task which is disrupted in human PD patients and in rodent models of PD. We injected PFF or monomers of human alpha-synuclein into the medial prefrontal cortex of mice pre-injected with adeno-associated virus (AAV) coding for overexpression of human alpha-synuclein or control protein. Despite notable medial prefrontal cortical synucleinopathy, we did not observe consistent deficits in fixed-interval timing. These results suggest that cortical alpha-synuclein does not reliably disrupt fixed-interval timing.

Time coding in rat dorsolateral striatum

To assess the role of dorsolateral striatum (DLS) in time coding, we recorded neuronal activity in rats tasked with comparing the durations of two sequential vibrations. Bayesian decoding of population activity revealed a representation of the unfolding of the trial across time. However, further analyses demonstrated a distinction between the encoding of trial time and perceived time. First, DLS did not show a privileged representation of the stimulus durations compared with other time spans. Second, higher intensity vibrations were perceived as longer; however, time decoded from DLS was unaffected by vibration intensity. Third, DLS did not encode stimulus duration differently on correct versus incorrect trials. Finally, in rats trained to compare the intensities of two sequential vibrations, stimulus duration was encoded even though it was a perceptually irrelevant feature. These findings lead us to posit that temporal information is inherent to DLS activity irrespective of the rat's ongoing percept.

Dorsomedial Striatal Activity Tracks Completion of Behavioral Sequences in Rats

For proper execution of goal-directed behaviors, individuals require both a general representation of the goal and an ability to monitor their own progress toward that goal. Here, we examine how dorsomedial striatum (DMS), a region pivotal for forming associations among stimuli, actions, and outcomes, encodes the execution of goal-directed action sequences that require self-monitoring of behavior. We trained rats to complete a sequence of at least five consecutive lever presses (without visiting the reward port) to obtain a reward and recorded the activity of individual cells in DMS while rats performed the task. We found that the pattern of DMS activity gradually changed during the execution of the sequence, permitting accurate decoding of sequence progress from neural activity at a population level. Moreover, this sequence-related activity was blunted on trials where rats did not complete a sufficient number of presses. Overall, these data suggest a link between DMS activity and the execution of behavioral sequences that require monitoring of ongoing behavior.

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Neural Basis of Somatosensory Spatial and Temporal Discrimination in Humans: The Role of Sensory Detection

While detecting somatic stimuli from the external environment, an accurate determination of their spatial and temporal properties is essential for human behavior. Whether and how detection relates to human capacity for somatosensory spatial discrimination (SD) and temporal discrimination (TD) remains unclear. Here, participants underwent functional magnetic resonance imaging scanning when simply detecting vibrotactile stimuli of the leg, judging their location (SD), or deciding their number in time (TD). By conceptualizing tactile discrimination as consisting of detection and determination processes, we found that tactile detection elicited activation specifically involved in SD within the right inferior and superior parietal lobules, 2 regions previously implicated in the control of spatial attention. These 2 regions remained activated in the determination process, during which functional connectivity between these 2 regions predicted individual SD ability. In contrast, tactile detection produced little activation specifically related to TD. Participants' TD ability was implemented in brain regions implicated in coding temporal structures of somatic stimuli (primary somatosensory cortex) and time estimation (anterior cingulate, pre-supplementary motor area, and putamen). Together, our findings indicate a close link between somatosensory detection and SD (but not TD) at the neural level, which aids in explaining why we can promptly respond toward detected somatic stimuli.

Conceptually plausible Bayesian inference in interval timing

In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.

Parallel processing of working memory and temporal information by distinct types of cortical projection neurons

It is unclear how different types of cortical projection neurons work together to support diverse cortical functions. We examined the discharge characteristics and inactivation effects of intratelencephalic (IT) and pyramidal tract (PT) neurons-two major types of cortical excitatory neurons that project to cortical and subcortical structures, respectively-in the deep layer of the medial prefrontal cortex in mice performing a delayed response task. We found stronger target-dependent firing of IT than PT neurons during the delay period. We also found the inactivation of IT neurons, but not PT neurons, impairs behavioral performance. In contrast, PT neurons carry more temporal information than IT neurons during the delay period. Our results indicate a division of labor between IT and PT projection neurons in the prefrontal cortex for the maintenance of working memory and for tracking the passage of time, respectively.

Beyond the classic sensory systems: Characteristics of the sense of time of harbor seals (Phoca vitulina) assessed in a visual temporal discrimination and a bisection task

Beyond the classic sensory systems, the sense of time is most likely involved from foraging to navigation. As a prerequisite for assessing the role time is playing in different behavioral contexts, we further characterized the sense of time of a harbor seal in this study. Supra-second time intervals were presented to the seal in a temporal discrimination and a temporal bisection task. During temporal discrimination, the seal needed to discriminate between a standard time interval (STI) and a longer comparison interval. In the bisection task, the seal learnt to discriminate two STIs. Subsequently, it indicated its subjective perception of test time intervals as resembling either the short or long STI more. The seal, although unexperienced regarding timing experiments, learnt both tasks fast. Depending on task, time interval or duration ratio, it achieved a high temporal sensitivity with Weber fractions ranging from 0.11 to 0.26. In the bisection task, the prerequisites for the Scalar Expectancy Theory including a constant Weber fraction, the bisection point lying close to the geometric mean of the STIs, and no significant influence of the STI pair condition on the probability of a long response were met for STIs with a ratio of 1:2, but not with a ratio of 1:4. In conclusion, the harbor seal's sense of time allows precise and complex judgments of time intervals. Cross-species comparisons suggest that principles commonly found to govern timing performance can also be discerned in harbor seals.

Modality-Specific Impairment of Hippocampal CA1 Neurons of Alzheimer's Disease Model Mice

Impairment of episodic memory, a class of memory for spatiotemporal context of an event, is an early symptom of Alzheimer's disease. Both spatial and temporal information are encoded and represented in the hippocampal neurons, but how these representations are impaired under amyloid β (Aβ) pathology remains elusive. We performed chronic imaging of the hippocampus in awake male amyloid precursor protein (App) knock-in mice behaving in a virtual reality environment to simultaneously monitor spatiotemporal representations and the progression of Aβ depositions. We found that temporal representation is preserved, whereas spatial representation is significantly impaired in the App knock-in mice. This is because of the overall reduction of active place cells, but not time cells, and compensatory hyperactivation of remaining place cells near Aβ aggregates. These results indicate the differential impact of Aβ aggregates on two major modalities of episodic memory, suggesting different mechanisms for forming and maintaining these two representations in the hippocampus.

The orbitofrontal cortex in temporal cognition

One of the most important factors in decision-making is estimating the value of available options. Subregions of the prefrontal cortex, including the orbitofrontal cortex (OFC), have been deemed essential for this process. Value computations require a complex integration across numerous dimensions, including, reward magnitude, effort, internal state, and time. The importance of the temporal dimension is well illustrated by temporal discounting tasks, in which subjects select between smaller-sooner versus larger-later rewards. The specific role of OFC in telling time and integrating temporal information into decision-making remains unclear. Based on the current literature, in this review we reevaluate current theories of OFC function, accounting for the influence of time. Incorporating temporal information into value estimation and decision-making requires distinct, yet interrelated, forms of temporal information including the ability to tell time, represent time, create temporal expectations, and the ability to use this information for optimal decision-making in a wide range of tasks, including temporal discounting and wagering. We use the term "temporal cognition" to refer to the integrated use of these different aspects of temporal information. We suggest that the OFC may be a critical site for the integration of reward magnitude and delay, and thus important for temporal cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Medial prefrontal cortex and the temporal control of action

Across species, the medial prefrontal cortex guides actions in time. This process can be studied using behavioral paradigms such as simple reaction-time and interval-timing tasks. Temporal control of action can be influenced by prefrontal neurotransmitters such as dopamine and acetylcholine and is highly relevant to human diseases such as Parkinson's disease, schizophrenia, and attention-deficit hyperactivity disorder (ADHD). We review evidence that across species, medial prefrontal lesions impair the temporal control of action. We then consider neurophysiological correlates in humans, primates, and rodents that might encode temporal processing and relate to cognitive-control mechanisms. These data have informed brain-stimulation studies in rodents and humans that can compensate for timing deficits. This line of work illuminates basic mechanisms of temporal control of action in the medial prefrontal cortex, which underlies a range of high-level cognitive processing and could contribute to new biomarkers and therapies for human brain diseases.

The ecological significance of time sense in animals

Time is a fundamental dimension of all biological events and it is often assumed that animals have the capacity to track the duration of experienced events (known as interval timing). Animals can potentially use temporal information as a cue during foraging, communication, predator avoidance, or navigation. Interval timing has been traditionally investigated in controlled laboratory conditions but its ecological relevance in natural environments remains unclear. While animals may time events in artificial and highly controlled conditions, they may not necessarily use temporal information in natural environments where they have access to other cues that may have more relevance than temporal information. Herein we critically evaluate the ecological contexts where interval timing has been suggested to provide adaptive value for animals. We further discuss attributes of interval timing that are rarely considered in controlled laboratory studies. Finally, we encourage consideration of ecological relevance when designing future interval-timing studies and propose future directions for such experiments.

Serotonergic projections to the orbitofrontal and medial prefrontal cortices differentially modulate waiting for future rewards

Optogenetic activation of serotonergic neurons in the dorsal raphe nucleus (DRN) enhances patience when waiting for future rewards, and this effect is maximized by both high probability and high timing uncertainty of reward. Here, we explored which serotonin projection areas contribute to these effects using optogenetic axon terminal stimulation. We found that serotonin stimulation in the orbitofrontal cortex (OFC) is nearly as effective as that in the DRN for promoting waiting, while in the nucleus accumbens, it does not promote waiting. We also found that serotonin stimulation in the medial prefrontal cortex (mPFC) promotes waiting only when the timing of future rewards is uncertain. Our Bayesian decision model of waiting assumed that the OFC and mPFC calculate the posterior probability of reward delivery separately. These results suggest that serotonin in the mPFC affects evaluation of time committed, while serotonin in the OFC is responsible for overall valuation of delayed rewards.

Low-dimensional dynamics for working memory and time encoding

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Prefrontal D1 Dopamine-Receptor Neurons and Delta Resonance in Interval Timing

Considerable evidence has shown that prefrontal neurons expressing D1-type dopamine receptors (D1DRs) are critical for working memory, flexibility, and timing. This line of work predicts that frontal neurons expressing D1DRs mediate cognitive processing. During timing tasks, one form this cognitive processing might take is time-dependent ramping activity-monotonic changes in firing rate over time. Thus, we hypothesized the prefrontal D1DR+ neurons would strongly exhibit time-dependent ramping during interval timing. We tested this idea using an interval-timing task in which we used optogenetics to tag D1DR+ neurons in the mouse medial frontal cortex (MFC). While 23% of MFC D1DR+ neurons exhibited ramping, this was significantly less than untagged MFC neurons. By contrast, MFC D1DR+ neurons had strong delta-frequency (1-4 Hz) coherence with other MFC ramping neurons. This coherence was phase-locked to cue onset and was strongest early in the interval. To test the significance of these interactions, we optogenetically stimulated MFC D1DR+ neurons early versus late in the interval. We found that 2-Hz stimulation early in the interval was particularly effective in rescuing timing-related behavioral performance deficits in dopamine-depleted animals. These findings provide insight into MFC networks and have relevance for disorders such as Parkinson's disease and schizophrenia.

Mediodorsal Thalamus Contributes to the Timing of Instrumental Actions

The perception of time is critical to adaptive behavior. While prefrontal cortex and basal ganglia have been implicated in interval timing in the seconds to minutes range, little is known about the role of the mediodorsal thalamus (MD), which is a key component of the limbic cortico-basal ganglia-thalamocortical loop. In this study, we tested the role of the MD in timing, using an operant temporal production task in male mice. In this task, that the expected timing of available rewards is indicated by lever pressing. Inactivation of the MD with muscimol produced rightward shifts in peak pressing on probe trials as well as increases in peak spread, thus significantly altering both temporal accuracy and precision. Optogenetic inhibition of glutamatergic projection neurons in the MD also resulted in similar changes in timing. The observed effects were found to be independent of significant changes in movement. Our findings suggest that the MD is a critical component of the neural circuit for interval timing, without playing a direct role in regulating ongoing performance.SIGNIFICANCE STATEMENT The mediodorsal nucleus (MD) of the thalamus is strongly connected with the prefrontal cortex and basal ganglia, areas which have been implicated in interval timing. Previous work has shown that the MD contributes to working memory and learning of action-outcome contingencies, but its role in behavioral timing is poorly understood. Using an operant temporal production task, we showed that inactivation of the MD significantly impaired timing behavior.

CREB transcription in the medial prefrontal cortex regulates the formation of long-term associative recognition memory

The medial prefrontal cortex (mPFC) is known to be critical for specific forms of long-term recognition memory, however the cellular mechanisms in the mPFC that underpin memory maintenance have not been well characterized. This study examined the importance of phosphorylation of cAMP responsive element binding protein (CREB) in the mPFC for different forms of long-term recognition memory in the rat. Adenoviral transduction of the mPFC with a dominant-negative inhibitor of CREB impaired object-in-place memory following a 6 or 24 h retention delay, but no impairment was observed following delays of 5 min or 3 h. Long-term object temporal order memory and spatial temporal order memory was also impaired. In contrast, there were no impairments in novel object recognition or object location memory. These results establish, for the first time, the importance of CREB phosphorylation within the mPFC for memory of associative and temporal information crucial to recognition.

The differential role of the dorsal hippocampus in initiating and terminating timed responses: A lesion study using the switch-timing task

This study investigated the role of the dorsal hippocampus (dHPC) in the temporal entrainment of behavior, while addressing limitations of previous evidence from peak procedure experiments. Rats were first trained on a switch-timing task in which food was obtained from one of two concurrently available levers; one lever was effective after 8 s and the other after 16  s. After performance stabilized, rats underwent either bilateral NMDA lesions of the dHPC or sham lesions. After recovery, switch-timing training resumed. In a subsequent condition, the switch-timing task was modified such that food was available after either 8 or 32 s. Although dHPC lesions had subtle and complex effects on when rats stopped seeking for food at the 8-s lever (departures), it more systematically reduced the time when rats started seeking for food at the 16-s and 32-s lever (switches). No systematic effect of dHPC lesions were observed on the coefficient of quartile variation (normalized dispersion) of latencies to switch. Within the context of the pacemaker-accumulator framework of interval timing, these findings suggest that partially or wholly independent mechanisms control the initiation and termination of timed responses, and that the dHPC is primarily involved in encoding the time to start responding.

Dynamically changing neuronal activity supporting working memory for predictable and unpredictable durations

Diverse neural processes have been proposed as the neural basis of working memory. To investigate whether the medial prefrontal cortex (mPFC) relies on different neural processes to mediate working memory depending on the predictability of delay duration, we examined mPFC neural activity in mice performing a delayed response task with fixed (4 s) or random (between 1-7 s) delay durations. mPFC neural activity was strongly influenced by the predictability of delay duration. Nevertheless, mPFC neurons seldom showed persistent activity spanning the entire delay period and instead showed dynamically-changing delay-period activity under both the fixed-delay and random-delay conditions. mPFC neurons conveyed higher working memory information under the random-delay than fixed-delay conditions, possibly due to a higher demand for stable working memory maintenance. Our results suggest that the rodent mPFC may rely on dynamically-changing neuronal activity to maintain working memory regardless of the predictability of delay duration.