Increased firing irregularity as an emergent property of neural-state transition in monkey prefrontal cortex

Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Extension to Learning Task Events

Learning is a crucial basis for biological systems to adapt to environments. Environments include various states or episodes, and episode-dependent learning is essential in adaptation to such complex situations. Here, we developed a model for learning a two-target search task used in primate physiological experiments. In the task, the agent is required to gaze one of the four presented light spots. Two neighboring spots are served as the correct target alternately, and the correct target pair is switched after a certain number of consecutive successes. In order for the agent to obtain rewards with a high probability, it is necessary to make decisions based on the actions and results of the previous two trials. Our previous work achieved this by using a dynamic state space. However, to learn a task that includes events such as fixation to the initial central spot, the model framework should be extended. For this purpose, here we propose a "history-in-episode architecture." Specifically, we divide states into episodes and histories, and actions are selected based on the histories within each episode. When we compared the proposed model including the dynamic state space with the conventional SARSA method in the two-target search task, the former performed close to the theoretical optimum, while the latter never achieved target-pair switch because it had to re-learn each correct target each time. The reinforcement learning model including the proposed history-in-episode architecture and dynamic state scape enables episode-dependent learning and provides a basis for highly adaptable learning systems to complex environments.

Shape and Rule Information Is Reflected in Different Local Field Potential Frequencies and Different Areas of the Primate Lateral Prefrontal Cortex

The lateral prefrontal cortex (LFPC) plays a crucial role in executive function by adaptively storing behavior-relevant information as working memory. Neural mechanisms associated with local field potentials (LFPs) may underlie the adaptive properties of the LFPC. Here, we analyzed how LFPs recorded from the monkey LFPC are modulated by the crucial factors of a shape manipulation task. In this task, the test shape is transformed by manipulating a lever to match the size and orientation of the sample shape. The subject is required to temporarily memorize the rules such as the arm-movement-manipulation relationship and the sample shape to generate the sequential behavior of operations. In the present study, we focused on task variables about shape and rules, and examined among which aspects distinguish the ventral and dorsal sides of the LFPC. We found that the transformed shape in the sample period strongly affected the theta and delta waves in the delay period on the ventral side, while the arm-manipulation assignment influenced the gamma components on the dorsal side. These findings suggest that area- and frequency-selective LFP modulations are involved in dynamically recruiting different behavior-relevant information in the LFPC.

Chronic unpredictable stress induces depression-related behaviors by suppressing AgRP neuron activity

Previous studies have shown that AgRP neurons in the arcuate nucleus (ARC) respond to energy deficits and play a key role in the control of feeding behavior and metabolism. Here, we demonstrate that chronic unpredictable stress, an animal model of depression, decreases spontaneous firing rates, increases firing irregularity and alters the firing properties of AgRP neurons in both male and female mice. These changes are associated with enhanced inhibitory synaptic transmission and reduced intrinsic neuronal excitability. Chemogenetic inhibition of AgRP neurons increases susceptibility to subthreshold unpredictable stress. Conversely, chemogenetic activation of AgRP neurons completely reverses anhedonic and despair behaviors induced by chronic unpredictable stress. These results indicate that chronic stress induces maladaptive synaptic and intrinsic plasticity, leading to hypoactivity of AgRP neurons and subsequently causing behavioral changes. Our findings suggest that AgRP neurons in the ARC are a key component of neural circuitry involved in mediating depression-related behaviors and that increasing AgRP neuronal activity coule be a novel and effective treatment for depression.

Differences in task-phase-dependent time-frequency patterns of local field potentials in the dorsal and ventral regions of the monkey lateral prefrontal cortex

Although the ventral and dorsal regions of the lateral prefrontal cortex (lPFC) are anatomically distinct, their functional differentiation is still controversial. Local field potentials (LFPs) reflect synaptic input and are widely modulated by information from both the external world and the internal state of the brain. However, functional mapping using LFPs has not been fully tested and is expected to provide new insights into their differences. Thus, the present study analyzed the task-phase-dependent modulations of LFPs recorded from the lPFC of monkeys as they performed a shape manipulation task. Hierarchical cluster analyses of the LFP time-frequency spectra revealed characteristic patterns, especially in the theta and low gamma ranges. In particular, the theta range distinguished the ventral and dorsal parts of the lPFC well. The ventral part exhibited a block of similar LFP patterns whereas the dorsal part showed scattered patterns of small or single sites with different LFP patterns. These results suggest that functional segregation within the lPFC, especially between the ventral and dorsal regions, can be evaluated using task-phase-dependent time-frequency modulations in LFPs.

Dynamic Axis-Tuned Cells in the Monkey Lateral Prefrontal Cortex during a Path-Planning Task

The lateral prefrontal cortex (lPFC) plays a crucial role in the cognitive processes known as executive functions, which are necessary for the planning of goal-directed behavior in complex and constantly changing environments. To adapt to such environments, the lPFC must use its neuronal resources in a flexible manner. To investigate the mechanism by which lPFC neurons code directional information flexibly, the present study explored the tuning properties and time development of lPFC neurons in male Japanese monkeys during a path-planning task, which required them to move a cursor to a final goal in a stepwise manner within a checkerboard-like maze. We identified "axis-tuned" cells that preferred two opposing directions of immediate goals (i.e., vertical and horizontal directions). Among them, a considerable number of these axis-tuned cells dynamically transformed from vector tuning to a single final-goal direction. We also found that the activities of axis-tuned cells, especially pyramidal neurons, were also modulated by the abstract sequence patterns that the animals were to execute. These findings suggest that the axis-tuned cells change what they code (the type of behavioral goal) as well as how they code (their tuning shapes) so that the lPFC can represent a large number of possible actions or sequences with limited neuronal resources. The dynamic axis-tuned cells must reflect the flexible coding of behaviorally relevant information at the single neuron level by the lPFC to adapt to uncertain environments.SIGNIFICANCE STATEMENT The lateral PFC (lPFC) plays a crucial role in the planning of goal-directed behavior in uncertain environments. To adapt to such situations, the lPFC must flexibly encode behaviorally relevant information. Here, we investigated the goal-tuning properties of neuronal firing in the monkey lPFC during a path-planning task. We identified axis-tuned cells that preferred "up-down" or "left-right" immediate goals, and found that many were dynamically transformed from vector tuning to a final-goal direction. The activities of neurons, especially pyramidal neurons, were also modulated by the abstract sequence patterns. Our findings suggest that PFC neurons can alter not only what they code (behavioral goal) but also how they code (tuning shape) when coping with unpredictable environments with limited neuronal resources.

Why context matters? Divisive normalization and canonical microcircuits in psychiatric disorders

Neural activity on cellular, regional, and behavioral levels shows context-dependence. Here we suggest the processing of input-output relationships in terms divisive normalization (DN), including (i) summing/averaging inputs and (ii) normalizing output against input stages, as a computational mechanism to underlie context-dependence. Input summation and output normalization are mediated by input-output relationships in canonical microcircuits (CM). DN/CM are altered in psychiatric disorders like schizophrenia or depression whose various symptoms can be characterized by abnormal context-dependence.

Temporal Patterns of Individual Neuronal Firing in Rat Dorsal Column Nuclei Provide Information Required for Somatosensory Discrimination

A wealth of mechanical information from the body generates various forms of sensory experience during touch or kinesthesia. Dorsal column nuclei (DCN) in the medulla are the first relay station for somatosensory inputs from peripheral receptors. These nuclei integrate somatosensory information and send the output to higher-order centers; therefore, investigating the firing patterns of DCN neurons should elucidate coding principles within the somatosensory system. In this study, we quantified the firing patterns of DCN neurons and examined whether the firing patterns of particular neurons are altered when moving tactile stimuli are applied in different directions. The activities of 17 neurons in the DCN of anesthetized rats were selected and their firing patterns were analyzed using LvR, which refers to the local variation of intervals of action potentials (i.e., the cross-correlation between consecutive intervals of action potentials) compensated by the refractoriness constant, R. The LvR of the 17 neurons ranged widely from 0.35 to 2.28. Of the 17 neurons, 12 responded to hair deflection (hair neurons), whereas five responded specifically to movement of forelimb joints. In 11 of 12 hair neurons, moving stimuli were applied in two to four different directions, which yielded 25 pairs of comparisons. Of these, 14 pairs (56%) showed significant differences in LvR. Among these 14 pairs, the range of LvR fluctuation was 0.13 ± 0.06 (mean ± standard deviation) and its effect size (Cohen's d) was 0.6 ± 0.2. These results suggest that the firing pattern of individual DCN neurons may contribute to somatosensory discrimination.