Growing evidence from primate neurophysiology and modeling indicates that in reaction time tasks, a perceptual choice is made when the firing rate of a selective cortical neural population reaches a threshold. This raises two questions: what is the neural substrate of the threshold and how can it be adaptively tuned according to behavioral demands? Using a biophysically based network model of spiking neurons, we show that local dynamics in the superior colliculus gives rise to an all-or-none burst response that signals threshold crossing in upstream cortical neurons. Furthermore, the threshold level depends only weakly on the efficacy of the cortico-collicular pathway. In contrast, the threshold and the rate of reward harvest are sensitive to, and hence can be optimally tuned by, the strength of cortico-striatal synapses, which are known to be modifiable by dopamine-dependent plasticity. Our model provides a framework to describe the main computational steps in a reaction time task and suggests that separate brain pathways are critical to the detection and adjustment of a decision threshold.

Although mammals of different species have different sleep patterns, brief sleep-wake transitions commonly are observed across species and appear to occur randomly throughout the sleeping period. The dynamical patterns and functions of these brief awakenings from sleep are not well understood, and they often are viewed as disruptions (random or pathologic) of the sleep process. In this article, we hypothesize that brief awakenings from sleep may reflect aspects of the endogenous sleep control mechanism and thus may exhibit certain robust dynamical patterns across species. We analyze sleep recordings from mice, rats, cats, and humans, and we compare the distributions of sleep and wake episode durations. For all four species, we find that durations of brief wake episodes during the sleep period exhibit a scale-free power-law behavior with an exponent ␣ that remains the same for all species (␣ Ϸ 2.2). In contrast, sleep episode durations for all four species follow exponential distributions with characteristic time scales, which change across species in relation to body mass and metabolic rate. Our findings suggest common dynamical features of brief awakenings and sleep durations across species and may provide insights into the dynamics of the neural circuits controlling sleep.power law ͉ sleep regulation ͉ sleep fragmentation S leep and wake are governed by complex interactions between neurons in many brain regions, including the hypothalamus and brainstem. Collectively, these neurons act as a sleep-wake ''latch'' that may help produce stable sleep and wakefulness (1, 2). Several mathematical and conceptual models have been proposed to account for the stability and control of sleep and wakefulness over time scales of hours and days (1-3). However, in addition to the regular sleep-wake pattern, humans and animals often exhibit brief awakenings from sleep. These brief awakenings seem to occur throughout the entire sleep period and are traditionally viewed as random disruptions of sleep associated with body motion or pathologic conditions such as sleep apnea. Because of that explanation, brief awakenings during sleep rarely are addressed in most current models of sleep regulation (4, 5).However, recent studies suggest that arousals and brief awakenings may have a more essential role in the process of sleep regulation, posing further questions to the origin and function of brief awakenings (5). A closer look at the temporal structure of the brief sleep-wake transitions reveals a complex picture (Fig. 1). In contrast to the circadian and ultradian cycles, which dominate the regulation of sleep and wakefulness at time scales of hours, brief awakenings from sleep exhibit distinct features: (i) they appear to be random, not periodic, and (ii) the duration of sleep and wake episodes during the sleep period ranges from seconds to several tens of minutes. In this article, we investigate whether a robust structure underlies the complex dynamics of the brief sleep-wake transitions across species. Some of us recently have repo...

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain.

Flexible behavior depends on the brain's ability to suppress a habitual response or to cancel a planned movement whenever needed. Such inhibitory control has been studied using the countermanding paradigm in which subjects are required to withhold an imminent movement when a stop signal appears infrequently in a fraction of trials. To elucidate the circuit mechanism of inhibitory control of action, we developed a recurrent network model consisting of spiking movement (GO) neurons and fixation (STOP) neurons, based on neurophysiological observations in the frontal eye field and superior colliculus of behaving monkeys. The model places a premium on the network dynamics before the onset of a stop signal, especially the experimentally observed high baseline activity of fixation neurons, which is assumed to be modulated by a persistent top-down control signal, and their synaptic interaction with movement neurons. The model simulated observed neural activity and fit behavioral performance quantitatively. In contrast to a race model in which the STOP process is initiated at the onset of a stop signal, in our model whether a movement will eventually be canceled is determined largely by the proactive top-down control and the stochastic network dynamics, even before the appearance of the stop signal. A prediction about the correlation between the fixation neural activity and the behavioral outcome was verified in the neurophysiological data recorded from behaving monkeys. The proposed mechanism for adjusting control through tonically active neurons that inhibit movement-producing neurons has significant implications for exploring the basis of impulsivity associated with psychiatric disorders.

Maintaining spatial orientation when carrying out goal-directed movements requires an animal to perform angular path integration. Such functionality has been recently demonstrated in the ellipsoid body (EB) of fruit flies, though the precise circuitry and underlying mechanisms remain unclear. We analyze recently published cellular-level connectomic data and identify the unique characteristics of the EB circuitry, which features coupled symmetric and asymmetric rings. By constructing a spiking neural circuit model based on the connectome, we reveal that the symmetric ring initiates a feedback circuit that sustains persistent neural activity to encode information regarding spatial orientation, while the asymmetric rings are capable of integrating the angular path when the body rotates in the dark. The present model reproduces several key features of EB activity and makes experimentally testable predictions, providing new insight into how spatial orientation is maintained and tracked at the cellular level.

We study dynamical aspects of sleep micro-architecture. We find that sleep dynamics exhibits a high degree of asymmetry, and that the entire class of sleep-stage transition pathways underlying the complexity of sleep dynamics throughout the night can be characterized by two independent asymmetric transition paths. These basic pathways remain stable under sleep disorders, even though the degree of asymmetry is significantly reduced. Our findings indicate an intriguing temporal organization in sleep dynamics at short time scales that is typical for physical systems exhibiting self-organized criticality (SOC).

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.