Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Synergistic anticancer effects of co-delivery of linc-RoR siRNA and curcumin using polyamidoamine dendrimers against breast cancer.

Breast cancer resistance to chemotherapy necessitates novel combination therapeutic approaches. Linc-RoR is a long intergenic noncoding RNA that regulates stem cell differentiation and promotes metastasis and invasion in breast cancer. Herein, we report a dual delivery system employing polyamidoamine dendrimers to co-administer the natural compound curcumin and linc-RoR siRNA for breast cancer treatment. Polyamidoamine dendrimers efficiently encapsulated curcumin and formed complexes with linc-RoR siRNA at an optimal N/P ratio. In MCF-7 breast cancer cells, the dendriplexes were effectively internalized and the combination treatment synergistically enhanced cytotoxicity, arresting the cell cycle at the G1 phase and inducing apoptosis. Linc-RoR gene expression was also significantly downregulated. Individual treatments showed lower efficacy, indicating synergism between components. Mechanistic studies are warranted to define the molecular underpinnings of this synergistic interaction. Our findings suggest dual delivery of linc-RoR siRNA and curcumin via dendrimers merits further exploration as a personalized therapeutic approach for overcoming breast cancer resistance.

Adaptive learning under expected and unexpected uncertainty.

The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

Multiple timescales of neural dynamics and integration of task-relevant signals across cortex.

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.

Separable Influences of Reward on Visual Processing and Choice.

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remain unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the end points of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that, in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, although the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options, whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for the integration of reward outcomes.

Salience-Driven Value Construction for Adaptive Choice under Risk.

Decisions we face in real life are inherently risky and can result in one of many possible outcomes. However, most of what we know about choice under risk is based on studies that use options with only two possible outcomes (simple gambles), so it remains unclear how the brain constructs reward values for more complex risky options faced in real life. To address this question, we combined experimental and modeling approaches to examine choice between pairs of simple gambles and pairs of three-outcome gambles in male and female human subjects. We found that subjects evaluated individual outcomes of three-outcome gambles by multiplying functions of reward magnitude and probability. To construct the overall value of each gamble, however, most subjects differentially weighted possible outcomes based on either reward magnitude or probability. These results reveal a novel dissociation between how reward information is processed when evaluating complex gambles: valuation of each outcome is based on a combination of reward information whereas weighting of possible outcomes mainly relies on a single piece of reward information. We show that differential weighting of possible outcomes could enable subjects to make decisions more easily and quickly. Together, our findings reveal a plausible mechanism for how salience, in terms of possible reward magnitude or probability, can influence the construction of subjective values for complex gambles. They also point to separable neural mechanisms for how reward value controls choice and attention to allow for more adaptive decision making under risk.SIGNIFICANCE STATEMENT Real-life decisions are inherently risky and can result in one of many possible outcomes, but how does the brain integrate information from all these outcomes to make decisions? To address this question, we examined choice between pairs of gambles with multiple outcomes using various computational models. We found that subjects evaluated individual outcomes by multiplying functions of reward magnitude and probability. To construct the overall value of each gamble, however, they differentially weighted possible outcomes based on either reward magnitude or probability. By doing so, they were able to make decisions more easily and quickly. Our findings illustrate how salience, in terms of possible reward magnitude or probability, can influence the construction of subjective values for more adaptive choice.

Influence of Expected Reward on Temporal Order Judgment.

Perceptual decision-making has been shown to be influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing, later stages of decision-making, or both. To address this question, we conducted two experiments in which human participants made saccades to what they perceived to be either the first or second of two visually identical but asynchronously presented targets while we manipulated expected reward from correct and incorrect responses on each trial. By comparing reward-induced bias in target selection (i.e., reward bias) during the two experiments, we determined whether reward caused changes in sensory or decision-making processes. We found similar reward biases in the two experiments indicating that reward information mainly influenced later stages of decision-making. Moreover, the observed reward biases were independent of the individual's sensitivity to sensory signals. This suggests that reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our experimental observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that, during a temporal judgment task, reward exerts its influence via changing later stages of decision-making (i.e., response bias) rather than early sensory processing (i.e., perceptual bias).

Combinations of low-level and high-level neural processes account for distinct patterns of context-dependent choice.

Context effects have been explained by either low-level neural adjustments or high-level cognitive processes but not their combination. It is currently unclear how these processes interact to shape individuals' responses to context. Here, we used a large cohort of human subjects in experiments involving choice between two or three gambles in order to study the dependence of context effects on neural adaptation and individuals' risk attitudes. Our experiments did not provide any evidence that neural adaptation on long timescales (~100 trials) contributes to context effects. Using post-hoc analyses we identified two groups of subjects with distinct patterns of responses to decoys, both of which depended on individuals' risk aversion. Subjects in the first group exhibited strong, consistent decoy effects and became more risk averse due to decoy presentation. In contrast, subjects in the second group did not show consistent decoy effects and became more risk seeking. The degree of change in risk aversion due to decoy presentation was positively correlated with the original degrees of risk aversion. To explain these results and reveal underlying neural mechanisms, we developed new models incorporating both low- and high-level processes and used these models to fit individuals' choice behavior. We found that observed distinct patterns of decoy effects can be explained by a combination of adjustments in neural representations and competitive weighting of reward attributes, both of which depend on risk aversion but in opposite directions. Altogether, our results demonstrate how a combination of low- and high-level processes shapes choice behavior in more naturalistic settings, modulates overall risk preference, and explains distinct behavioral phenotypes.

On the Flexibility of Basic Risk Attitudes in Monkeys.

Monkeys and other animals appear to share with humans two risk attitudes predicted by prospect theory: an inverse-S-shaped probability-weighting (PW) function and a steeper utility curve for losses than for gains. These findings suggest that such preferences are stable traits with common neural substrates. We hypothesized instead that animals tailor their preferences to subtle changes in task contexts, making risk attitudes flexible. Previous studies used a limited number of outcomes, trial types, and contexts. To gain a broader perspective, we examined two large datasets of male macaques' risky choices: one from a task with real (juice) gains and another from a token task with gains and losses. In contrast to previous findings, monkeys were risk seeking for both gains and losses (i.e., lacked a reflection effect) and showed steeper gain than loss curves (loss seeking). Utility curves for gains were substantially different in the two tasks. Monkeys showed nearly linear PWs in one task and S-shaped ones in the other; neither task produced a consistent inverse-S-shaped curve. To account for these observations, we developed and tested various computational models of the processes involved in the construction of reward value. We found that adaptive differential weighting of prospective gamble outcomes could partially account for the observed differences in the utility functions across the two experiments and thus provide a plausible mechanism underlying flexible risk attitudes. Together, our results support the idea that risky choices are constructed flexibly at the time of elicitation and place important constraints on neural models of economic choice.SIGNIFICANCE STATEMENT We respond in reliable ways to risk, but are our risk preferences stable traits or ephemeral states? Using various computational models, we examined two large datasets of macaque risky choices in two different tasks. We observed several deviations from "classic" risk preferences seen in humans and monkeys: no reflection effect, loss seeking as opposed to loss aversion, and linear and S-shaped, as opposed to inverse-S-shaped, probability distortion. These results challenge the idea that our risk attitudes are evolved traits shared with the last common ancestor of macaques and humans, suggesting instead that behavioral flexibility is the hallmark of risky choice in primates. We show how this flexibility can emerge partly as a result of interactions between attentional and reward systems.

Dynamic combination of sensory and reward information under time pressure.

When making choices, collecting more information is beneficial but comes at the cost of sacrificing time that could be allocated to making other potentially rewarding decisions. To investigate how the brain balances these costs and benefits, we conducted a series of novel experiments in humans and simulated various computational models. Under six levels of time pressure, subjects made decisions either by integrating sensory information over time or by dynamically combining sensory and reward information over time. We found that during sensory integration, time pressure reduced performance as the deadline approached, and choice was more strongly influenced by the most recent sensory evidence. By fitting performance and reaction time with various models we found that our experimental results are more compatible with leaky integration of sensory information with an urgency signal or a decision process based on stochastic transitions between discrete states modulated by an urgency signal. When combining sensory and reward information, subjects spent less time on integration than optimally prescribed when reward decreased slowly over time, and the most recent evidence did not have the maximal influence on choice. The suboptimal pattern of reaction time was partially mitigated in an equivalent control experiment in which sensory integration over time was not required, indicating that the suboptimal response time was influenced by the perception of imperfect sensory integration. Meanwhile, during combination of sensory and reward information, performance did not drop as the deadline approached, and response time was not different between correct and incorrect trials. These results indicate a decision process different from what is involved in the integration of sensory information over time. Together, our results not only reveal limitations in sensory integration over time but also illustrate how these limitations influence dynamic combination of sensory and reward information.

Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles.

ABSTARCT: An ensemble of neurons can provide a dynamic representation of external stimuli, ongoing processes, or upcoming actions. This dynamic representation could be achieved by changes in the activity of individual neurons and/or their interactions. To investigate these possibilities, we simultaneously recorded from ensembles of prefrontal neurons in non-human primates during a memory-guided saccade task. Using both decoding and encoding methods, we examined changes in the information content of individual neurons and that of ensembles between visual encoding and saccadic target selection. We found that individual neurons maintained their limited spatial sensitivity between these cognitive states, whereas the ensemble selectively improved its encoding of spatial locations far from the neurons' preferred locations. This population-level "encoding expansion" was not due to the ceiling effect at the preferred locations and was accompanied by selective changes in noise correlations for non-preferred locations. Moreover, the encoding expansion was observed for ensembles of different types of neurons and could not be explained by shifts in the preferred location of individual neurons. Our results demonstrate that the representation of space by neuronal ensembles is dynamically enhanced prior to saccades, and this enhancement occurs alongside changes in noise correlations more than changes in the activity of individual neurons.

Optimal structure of metaplasticity for adaptive learning.

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified 'superior' metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning.

Belief about nicotine selectively modulates value and reward prediction error signals in smokers.

Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

Isolation, spectroscopic characterization, X-ray, theoretical studies as well as in vitro cytotoxicity of Samarcandin.

Samarcandin 1, a natural sesquiterpene-coumarin, was isolated as well as elucidated from F. assa-foetida which has significant effect in Iranian traditional medicine because of its medicinal attitudes. The crystal structure of samarcandin was determined by single-crystal X-ray structure analysis. It is orthorhombic, with unit cell parameters a=10.8204 (5)Å, b=12.9894 (7)Å, c=15.2467 (9)Å, V=2142.9 (2)Å(3), space group P212121 and four symmetry equivalent molecules in the unit cell. Samarcandin was isolated in order to study for its theoretical studies as well as its cellular toxicity as anti-cancer drug against two cancerous cells. In comparison with controls, our microscopic and MTT assay data showed that samarcandin suppresses cancer cell proliferation in a dose-dependent manner with IC50=11µM and 13 for AGS and WEHI-164 cell lines, respectively. Density functional theory (DFT) and time-dependent density functional theory (TD-DFT) of the structure was computed by three functional methods and 6-311++G(∗∗) standard basis set. The optimized molecular geometry and theoretical analysis agree closely to that obtained from the single crystal X-ray crystallography. To sum up, the good correlations between experimental and theoretical studies by UV, NMR, and IR spectra were found.

Dissociable dopaminergic control of saccadic target selection and its implications for reward modulation.

To investigate mechanisms by which reward modulates target selection, we studied the behavioral effects of perturbing dopaminergic activity within the frontal eye field (FEF) of monkeys performing a saccadic choice task and simulated the effects using a plausible cortical network. We found that manipulation of FEF activity either by blocking D1 receptors (D1Rs) or by stimulating D2 receptors (D2Rs) increased the tendency to choose targets in the response field of the affected site. However, the D1R manipulation decreased the tendency to repeat choices on subsequent trials, whereas the D2R manipulation increased that tendency. Moreover, the amount of shift in target selection resulting from the two manipulations correlated in opposite ways with the baseline stochasticity of choice behavior. Our network simulation results suggest that D1Rs influence target selection mainly through their effects on the strength of inputs to the FEF and on recurrent connectivity, whereas D2Rs influence the excitability of FEF output neurons. Altogether, these results reveal dissociable dopaminergic mechanisms influencing target selection and suggest how reward can influence adaptive choice behavior via prefrontal dopamine.

Biochemical and biophysical properties of a novel homoisoflavonoid extracted from Scilla persica HAUSSKN.

In this study isolation and structural elucidation of a homoisoflavonoid, 3-(3',4'-dihydroxybenzyl)-8-hydroxy-5,7-dimethoxychroman-4-one (Scillapersicone 1), is reported from Scilla persica HAUSSKN. The structure was solved by a single crystal X-ray analysis. The unit cell parameters are a=11.7676 (2)Å, b=20.1174 (4)Å, c=7.8645 (9)Å, ß=93.544 (2)°, V=1858.23 (7)Å(3), monoclinic space group P21/c and four symmetry equivalent molecules in an unit cell. The structure was consistent with the UV, IR, 1D and 2D NMR, HRFAB-MS data. The optimized molecular geometry agrees closely that obtained from the single crystal X-ray crystallography. Furthermore, cytotoxicity of this compound was evaluated by MTT assay on AGS and WEHI-164 cancerous cell lines.

A range-normalization model of context-dependent choice: a new model and evidence.

Most utility theories of choice assume that the introduction of an irrelevant option (called the decoy) to a choice set does not change the preference between existing options. On the contrary, a wealth of behavioral data demonstrates the dependence of preference on the decoy and on the context in which the options are presented. Nevertheless, neural mechanisms underlying context-dependent preference are poorly understood. In order to shed light on these mechanisms, we design and perform a novel experiment to measure within-subject decoy effects. We find within-subject decoy effects similar to what have been shown previously with between-subject designs. More importantly, we find that not only are the decoy effects correlated, pointing to similar underlying mechanisms, but also these effects increase with the distance of the decoy from the original options. To explain these observations, we construct a plausible neuronal model that can account for decoy effects based on the trial-by-trial adjustment of neural representations to the set of available options. This adjustment mechanism, which we call range normalization, occurs when the nervous system is required to represent different stimuli distinguishably, while being limited to using bounded neural activity. The proposed model captures our experimental observations and makes new predictions about the influence of the choice set size on the decoy effects, which are in contrast to previous models of context-dependent choice preference. Critically, unlike previous psychological models, the computational resource required by our range-normalization model does not increase exponentially as the set size increases. Our results show that context-dependent choice behavior, which is commonly perceived as an irrational response to the presence of irrelevant options, could be a natural consequence of the biophysical limits of neural representation in the brain.

Training-dependent gradients of timescales of neural dynamics in the primate prefrontal cortex and their contributions to working memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, e.g., from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales depend on training in a specific task and/or are an inherent property of neurons, and whether more fine-grained hierarchies of timescales exist within specific cortical regions. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition. Significance statement: Recent studies have demonstrated that neural responses exhibit dynamics with different timescales that follow a certain order or hierarchy across cortical areas. While the hierarchy of timescales is consistent across different tasks, it is unknown if these timescales emerge only after training or if they represent inherent properties of neurons. To answer this question, we estimated multiple timescales in neural response across five subregions of the monkeys' lateral prefrontal cortex before and after training on a working-memory task. Our results provide evidence for fine-grained gradients related to certain neural dynamics. Moreover, we show that these timescales depend on and can be modulated by training in a cognitive task, and contribute to encoding of task-relevant information at single-cell and population levels.