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Even in the absence of specific sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide-field voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head-fixed male and female mice expressing the voltage-sensitive fluorescent FRET sensor Butterfly 1.2. We find that global voltage and hemodynamic signals are both positively correlated with changes in arousal with amaximum correlation of 0.5 and 0.25, respectively, at a time lag of 0 s. We next show that arousal influences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensorymotor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in the prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies >1 Hz. We finally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity. [ABSTRACT FROM AUTHOR]

Author(s): Daniel A. Butts (corresponding author) [1, 2]; Chong Weng [3]; Jianzhong Jin [3]; Chun-I Yeh [3, 4]; Nicholas A. Lesica [1]; Jose-Manuel Alonso [3]; Garrett B. Stanley [1] The [...]

The electrolarynx (EL) voice prosthesis is widely used, but suffers from the inconvenience of requiring manual control. Therefore, a hands-free EL triggered by neck muscle electromyographic (EMG) activity was developed (EMG-EL). Signal processing circuitry in a belt-mounted control unit transforms EMG activity into control signals for initiation and termination of voicing. These control signals are then fed to an EL held against the neck by an inconspicuous brace. Performance of the EMG-EL was evaluated by comparison to normal voice, manual EL voice, and tracheo-esophageal (TE) voice in a series of reaction time experiments in seven normal subjects and one laryngectomy patient. The normal subjects produced voice initiation with the EMG-EL that was as fast as both normal voice and the manual EL. The laryngectomy subject produced voice initiation that was slower than with the manual EL, but faster than with TE voice. Voice termination with the EMG-EL was slower than normal voice for the normal subjects, but not significantly different than with the manual EL. The laryngectomy subject produced voice termination with the EMG-EL that was slower than with TE or manual EL. The EMG-EL threshold was set at 10% of the range of vocal-related EMG activity above baseline. Simulations of EMG-EL behavior showed that the 10% threshold was not significantly different from the optimum threshold produced through the process of error minimization. The EMG-EL voice reaction time appears to be adequate for use in a day-to-day conversation. Index Terms--Electromyography, prosthesis, voice.

As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in vivo extracellular single-unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that results in masking of synaptic relationships. Here, we provide a framework for establishing connectivity in multisite, multielectrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures. NEW & NOTEWORTHY Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior. [ABSTRACT FROM AUTHOR]

Behavioral adaptation is a prerequisite for survival in a constantly changing sensory environment, but the underlying strategies and relevant variables driving adaptive behavior are not well understood. Many learning models and neural theories consider probabilistic computations as an efficient way to solve a variety of tasks, especially if uncertainty is involved. Although this suggests a possible role for probabilistic inference and expectation in adaptive behaviors, there is little if any evidence of this relationship experimentally. Here, we investigated adaptive behavior in the rat model by using a well controlled behavioral paradigm within a psychophysical framework to predict and quantify changes in performance of animals trained on a simple whisker-based detection task. The sensory environment of the task was changed by transforming the probabilistic distribution ofwhisker deflection amplitudes systematically while measuring the animal's detection performance and corresponding rate of accumulated reward. We show that the psychometric function deviates significantly and reversibly depending on the probabilistic distribution of stimuli. This change in performance relates to accumulating a constant reward count across trials, yet it is exempt from changes in reward volume. Our simple model of reward accumulation captures the observed change in psychometric sensitivity and predicts a strategy seeking to maintain reward expectation across trials in the face of the changing stimulus distribution. We conclude that rats are able maintain a constant payoff under changing sensory conditions by flexibly adjusting their behavioral strategy. Our findings suggest the existence of an internal probabilistic model that facilitates behavioral adaptation when sensory demands change. [ABSTRACT FROM AUTHOR]

Even in the absence of external perturbation to the human cardiovascular system, measures of cardiac function, such as heart rate, vary with time in normal physiology. The primary source of the variation is constant regulation by a complex control system which modulates cardiac function through the autonomic nervous system. Here, we present methods of characterizing the statistical properties of the underlying processes that result in variations in ECG R-wave event times within the framework of an integrate-and-fire model. We first present techniques for characterizing the noise processes that result in heart rate variability even in the absence of autonomic input. A relationship is derived that relates the spectrum of R-R intervals to the spectrum of the underlying noise process. We then develop a technique for the characterization of the dynamic nature of autonomically related variability resulting from exogenous inputs, such as respiratory-related modulation. A method is presented for the estimation of the transfer function that relates the respiratory-related input to the variations in R-wave event times. The result is a very direct analysis of autonomic control of heart rate variability through noninvasive measures, which provides a method for assessing autonomic function in normal and pathological states. Index Terms--Autonomic nervous system, heart rate variability, stochastic processes, threshold models.

Little is known about whether information transfer at primary sensory thalamic nuclei is modified by behavioral context. Here we studied the influence of previous decisions/rewards on current choices and preceding spike responses of ventroposterior medial thalamus (VPm; the primary sensory thalamus in the rat whisker-related tactile system). We trained head-fixed rats to detect a ramp-like deflection of one whisker interspersed within ongoing white noise stimulation. Using generative modeling of behavior, we identify two task-related variables that are predictive of actual decisions. The first reflects task engagement on a local scale ("trial history": defined as the decisions and outcomes of a small number of past trials), whereas the other captures behavioral dynamics on a global scale ("satiation": slow dynamics of the response pattern along an entire session). Although satiation brought about a slow drift from Go to NoGo decisions during the session, trial history was related to local (trial-by-trial) patterning of Go and NoGo decisions. A second model that related the same predictors first to VPm spike responses, and from there to decisions, indicated that spiking, in contrast to behavior, is sensitive to trial history but relatively insensitive to satiation. Trial history influences VPm spike rates and regularity such that a history of Go decisions would predict fewer noise-driven spikes (but more regular ones), and more ramp-driven spikes. Neuronal activity in VPm, thus, is sensitive to local behavioral history, and may play an important role in higher-order cognitive signaling. [ABSTRACT FROM AUTHOR]

Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10 -20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene. [ABSTRACT FROM AUTHOR]