Neural Representation Of Time Research Articles

Time, memory, and the legacy of Howard Eichenbaum.

Over the past 15 years, there has been an explosion of new research on the role of the hippocampus in the representation of information about time in memory. Much of this work was inspired by the ideas and research of Howard Eichenbaum, who made major contributions to our understanding of the neurobiology of episodic memory and the neural representation of time. In this article, I will review evidence regarding the role of time in understanding hippocampal function. This review will cover a broad range of evidence from studies of humans and nonhuman animals with a narrative arc that follows Howard's major discoveries. These studies demonstrate that the hippocampus encodes information in relation to an episodic context, and that time, as well as space, serves to define these contexts. Moreover, the research has shown that the hippocampus can encode temporal, spatial, and situational information in parallel. Building on this work, I present a new framework for understanding temporal structure in human episodic memory. I conclude by outlining current controversies and new questions that must be addressed by the field in the years to come.

Differential Encoding of Time by Prefrontal and Striatal Network Dynamics.

The neural representation of time is thought to be distributed across multiple functionally specialized brain structures, including the striatum and cortex. However, until now, the neural code for time has not been compared quantitatively between these areas. Here, we performed large-scale recordings in the striatum and orbitofrontal cortex of mice trained on a stimulus-reward association task involving a delay period and used a machine-learning algorithm to quantify how well populations of simultaneously recorded neurons encoded elapsed time from stimulus onset. We found that, although both areas encoded time, the striatum consistently outperformed the orbitofrontal cortex. These results suggest that the striatum may refine the code for time by integrating information from multiple inputs.

The Mechanisms of Movement Control and Time Estimation in Cervical Dystonia Patients

Traditionally, the pathophysiology of cervical dystonia has been regarded mainly in relation to neurochemical abnormities in the basal ganglia. Recently, however, substantial evidence has emerged for cerebellar involvement. While the absence of neurological “cerebellar signs” in most dystonia patients may be considered at least provoking, there are more subtle indications of cerebellar dysfunction in complex, demanding tasks. Specifically, given the role of the cerebellum in the neural representation of time, in the millisecond range, dysfunction to this structure is considered to be of greater importance than dysfunction of the basal ganglia. In the current study, we investigated the performance of cervical dystonia patients on a computer task known to engage the cerebellum, namely, the interception of a moving target with changing parameters (speed, acceleration, and angle) with a simple response (pushing a button). The cervical dystonia patients achieved significantly worse results than a sample of healthy controls. Our results suggest that the cervical dystonia patients are impaired at integrating incoming visual information with motor responses during the prediction of upcoming actions, an impairment we interpret as evidence of cerebellar dysfunction.

The Neural Representation of Time: An Information-Theoretic Perspective

A prominent finding in psychophysical experiments on time perception is Weber's law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Weber's law remains unknown, and currently only a few models generically reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Weber's law in time perception and also its frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Weber's law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance- and correlation-based processes and allows predictions about the scaling of the resulting timing errors.

Sex Differences in Counting and Timing

Sex Differences in Counting and Timing

Learned Timing of Motor Behavior in the Smooth Eye Movement Region of the Frontal Eye Fields

Proper timing is a critical aspect of motor learning. We report a relationship between a representation of time and an expression of learned timing in neurons in the smooth eye movement region of the frontal eye fields (FEF(SEM)). During prelearning pursuit of target motion at a constant velocity, each FEF(SEM) neuron is most active at a distinct time relative to the onset of pursuit tracking. In response to an instructive change in target direction, a neuron expresses the most learning when the instruction occurs near the time of its maximal participation in prelearning pursuit. Different neurons are most active, and undergo the most learning, at distinct times during pursuit. We suggest that the representation of time in the FEF(SEM) drives learning that is temporally linked to an instructive change in target motion, and that this may be a general function of motor areas of the cortex.

Neural Representation of Temporal Duration: Coherent Findings Obtained with the “Lossy Integration” Model

Neural Representation of Temporal Duration: Coherent Findings Obtained with the “Lossy Integration” Model

Neural representation of time in cortico-basal ganglia circuits

Encoding time is universally required for learning and structuring motor and cognitive actions, but how the brain keeps track of time is still not understood. We searched for time representations in cortico-basal ganglia circuits by recording from thousands of neurons in the prefrontal cortex and striatum of macaque monkeys performing a routine visuomotor task. We found that a subset of neurons exhibited time-stamp encoding strikingly similar to that required by models of reinforcement-based learning: They responded with spike activity peaks that were distributed at different time delays after single task events. Moreover, the temporal evolution of the population activity allowed robust decoding of task time by perceptron models. We suggest that time information can emerge as a byproduct of event coding in cortico-basal ganglia circuits and can serve as a critical infrastructure for behavioral learning and performance.

Predictive Motor Timing Performance Dissociates Between Early Diseases of the Cerebellum and Parkinson's Disease

There is evidence that both the basal ganglia and the cerebellum play a role in the neural representation of time in a variety of behaviours, but whether one of them is more important is not yet clear. To address this question in the context of predictive motor timing, we tested patients with various movement disorders implicating these two structures in a motor-timing task. Specifically, we investigated four different groups: (1) patients with early Parkinson's disease (PD); (2) patients with sporadic spinocerebellar ataxia (SCA); (3) patients with familial essential tremor (ET); and (4) matched healthy controls. We used a predictive motor-timing task that involved mediated interception of a moving target, and we assessed the effect of movement type (acceleration, deceleration and constant), speed (slow, medium and fast) and angle (0 degrees , 15 degrees and 30 degrees) on performance (hit, early error and late error). The main results showed that PD group and arm ET subgroup did not significantly differ from the control group. SCA and head ET subjects (severe and mild cerebellar damage, respectively) were significantly worse at interception than the other two groups. Our findings support the idea that the basal ganglia play a less significant role in predictive motor timing than the cerebellum. The fact that SCA and ET subjects seemed to have a fundamental problem with predictive motor timing suggests that the cerebellum plays an essential role in integrating incoming visual information with the motor output in a timely manner, and that ET is a heterogeneous entity that deserves increased attention from clinicians.