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Large cortical and hippocampal pyramidal neurons are elements of neuronal circuitry that have been implicated in cross-frequency coupling (CFC) during cognitive tasks. We investigate potential mechanisms for CFC within these neurons by examining the role that the hyperpolarization-activated mixed cation current (Ih) plays in modulating CFC characteristics in multicompartment neuronal models. We quantify CFC along the soma-apical dendrite axis and tuft of three models configured to have different spatial distributions of Ih conductance density: (1) exponential gradient along the soma-apical dendrite axis, (2) uniform distribution, and (3) no Ih conductance. We simulated two current injection scenarios: distal apical 4 Hz modulation and perisomatic 4 Hz modulation, each with perisomatic, mid-apical, and distal apical 40 Hz injections. We used two metrics to quantify CFC strength-modulation index and height ratio-and we analyzed CFC phase properties. For all models, CFC was strongest in distal apical regions when the 40 Hz injection occurred near the soma and the 4 Hz modulation occurred in distal apical dendrite. The strongest CFC values were observed in the model with uniformly distributed Ih conductance density, but when the exponential gradient in Ih conductance density was added, CFC strength decreased by almost 50%. When Ih was in the model, regions with much larger membrane potential fluctuations at 4 Hz than at 40 Hz had stronger CFC. Excluding the Ih conductance from the model resulted in CFC either reduced or comparable in strength relative to the model with the exponential gradient in Ih conductance. The Ih conductance also imposed order on the phase characteristics of CFC such that minimum (maximum) amplitude 40 Hz membrane potential oscillations occurred during Ih conductance deactivation (activation). On the other hand, when there was no Ih conductance, phase relationships between minimum and maximum 40 Hz oscillation often inverted and occurred much closer together. This analysis can help experimentalists discriminate between CFC that originates from different underlying physiological mechanisms and can help illuminate the reasons why there are differences between CFC strength observed in different regions of the brain and between different populations of neurons based on the configuration of the Ih conductance.

In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50-74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 106 neurons with 18 × 109 synapses and 2.2 × 106 neurons with 45 × 109 synapses. The modifications to GENESIS that enabled these large scale simulations have been incorporated into the May 2019 Official Release of PGENESIS 2.4 available for download from the GENESIS web site (genesis-sim.org).

Humans are increasingly interacting and collaborating with robotic and intelligent agents. How to make these interactions as effective as possible remains, however, an open question. Here, we argue that consistent understandings of the environment on the part of the human and agent are critical for their interaction and basing these understandings on only the objective features of sensory inputs may be inadequate. To that end, the current paper presents a novel approach to more integrated characterizations of the sensory environment that encompass objective and subjective features of sensory inputs. We propose that an approach to signal and behavioral estimation consistent with the control and communication theoretic perspective of Cybernetics could inform human robot interaction (HRI) applications. Specifically, we offer a potential path forward for quantifying similarity in stimulus events that can lead to consistent understandings of the environment, which when applied to HRI can enhance human-agent communication in HRI applications.

Decades of heavy investment in laboratory-based brain imaging and neuroscience have led to foundational insights into how humans sense, perceive, and interact with the external world. However, it is argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. Thus, it remains unclear how well the current knowledge of human brain function translates into the highly dynamic real world. While some demonstrated successes in real-world neurotechnologies are observed, particularly in the area of brain-computer interaction technologies, innovations and developments to date are limited to a small science and technology community. We posit that advancements in realworld neuroimaging tools for use by a broad-based workforce will dramatically enhance neurotechnology applications that have the potential to radically alter human-system interactions across all aspects of everyday life. We discuss the efforts of a joint government-academic-industry team to take an integrative, interdisciplinary, and multi-aspect approach to translate current technologies into devices that are truly fieldable across a range of environments. Results from initial work, described here, show promise for dramatic advances in the field that will rapidly enhance our ability to assess brain activity in real-world scenarios.

As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or locked in patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies., Comment: 41 pages, 3 figures

A closed-loop system that offers real-time assessment and manipulation of a user's affective and cognitive states is very useful in developing adaptive environments which respond in a rational and strategic fashion to real-time changes in user affect, cognition, and motivation. The goal is to progress the user from suboptimal cognitive and affective states toward an optimal state that enhances user performance. In order to achieve this, there is need for assessment of both 1) the optimal affective/cognitive state and 2) the observed user state. This paper presents approaches for assessing these two states. Arousal, an important dimension of affect, is focused upon because of its close relation to a user's cognitive performance, as indicated by the Yerkes-Dodson Law. Herein, we make use of a Virtual Reality Stroop Task (VRST) from the Virtual Reality Cognitive Performance Assessment Test (VRCPAT) to identify the optimal arousal level that can serve as the affective/cognitive state goal. Three stimuli presentations (with distinct arousal levels) in the VRST are selected. We demonstrate that when reaction time is used as the performance measure, one of the three stimuli presentations can elicit the optimal level of arousal for most subjects. Further, results suggest that high classification rates can be achieved when a support vector machine is used to classify the psychophysiological responses (skin conductance level, respiration, ECG, and EEG) in these three stimuli presentations into three arousal levels. This research reflects progress toward the implementation of a closed-loop affective computing system.

Human movement control is inherently stochastic, requiring continuous estimation of self-motion based upon noisy sensory inputs. The nervous system must determine which sensory signals are relevant on a time scale that enables successful behavior. In human stance control, failure to effectively adapt to changing sensory contexts could lead to injurious falls. Nonlinear changes in postural sway amplitude in response to changes in sensory environmental motion have indicated a dynamic changing of the weighting of the nervous system's multiple sensory inputs so that estimates are based upon the most relevant and accurate information available. However, the time scale of these changes is virtually unknown. Results here show systematic changes in postural gain when visual scene motion amplitude is increased or decreased abruptly, consistent with sensory re-weighting. However, this re-weighting displayed a temporal asymmetry. When visual motion increased, gain decreased within 5 s to a value near its asymptotic value. In contrast, when visual motion decreased, it took an additional 5 s for gain to increase by a similar absolute amount. Suddenly increasing visual motion amplitude threatens balance if gain remains high, and rapid down-weighting of the sensory signal is required to avoid falling. By contrast, slow up-weighting suggests a conservative CNS strategy. It may not be functional to rapidly up-weight with transient changes in the sensory environment. Only sustained changes necessitate the slower up-weighting process. Such results add to our understanding of adaptive processing, identifying a temporal asymmetry in sensory re-weighting dynamics that could be a general property of adaptive estimation in the nervous system.

A primary goal in operational neuroscience is to create translational pathways linking laboratory observations with real-world applications. Achieving this requires a method that enables study of variability in operator performance that does not typically emerge under controlled laboratory circumstances; the present paper describes the development of such a paradigm. An essential aspect of the design process involved eliciting subject engagement without using extrinsic incentive (e.g. money) as a motivating stressor and, instead, tapping an appropriate intrinsic incentive (i.e. competitive stress). Two sources of competition were initially considered including one based on self-competition and another based on competition with another individual; ultimately, the latter approach was selected. A virtual competitor was designed to affect individual valuation of momentary successes and failures in specific ways and preliminary results revealed early indicators of success in meeting this goal. Discussion focuses on implications and challenges for future research using similar translational paradigms.

Postural sway is considered to have two fundamental stochastic components, a slow nonoscillatory component and a faster damped-oscillatory component. The slow component has been shown to account for the majority of sway variance during quiet stance. Postural control is generally viewed as a feedback loop in which sway is detected by sensory systems and appropriate motor commands are generated to stabilize the body's orientation. Whereas the mechanistic source for the damped-oscillatory sway component is most likely feedback control of an inverted pendulum, the underlying basis for the slow component is less clear. We investigated whether the slow process was inside or outside the feedback loop by providing standing subjects with sum-of-sines visual motion. Linear stochastic models were fit to the experimental sway trajectories to determine the stochastic structure of sway as well as the transfer function from visual motion to sway. The results supported a fifth-order stochastic model, consisting of a slow process and two damped-oscillatory components. Importantly, the slow process was determined to be inside the feedback loop. This supports the hypothesis that the slow component is due to errors in state estimation because state estimation is inside the feedback loop rather than a moving reference point or an exploratory process outside the feedback loop.

We analyze the stochastic structure of postural sway and demonstrate that this structure imposes important constraints on models of postural control. Linear stochastic models of various orders were fit to the center-of-mass trajectories of subjects during quiet stance in four sensory conditions: (i) light touch and vision, (ii) light touch, (iii) vision, and (iv) neither touch nor vision. For each subject and condition, the model of appropriate order was determined, and this model was characterized by the eigenvalues and coefficients of its autocovariance function. In most cases, postural-sway trajectories were similar to those produced by a third-order model with eigenvalues corresponding to a slow first-order decay plus a faster-decaying damped oscillation. The slow-decay fraction, which we define as the slow-decay autocovariance coefficient divided by the total variance, was usually near 1. We compare the stochastic structure of our data to two linear control-theory models: (i) a proportional-integral-derivative control model in which the postural system's state is assumed to be known, and (ii) an optimal-control model in which the system's state is estimated based on noisy multisensory information using a Kalman filter. Under certain assumptions, both models have eigenvalues consistent with our results. However, the slow-decay fraction predicted by both models is less than we observe. We show that our results are more consistent with a modification of the optimal-control model in which noise is added to the computations performed by the state estimator. This modified model has a slow-decay fraction near 1 in a parameter regime in which sensory information related to the body's velocity is more accurate than sensory information related to position and acceleration. These findings suggest that: (i) computation noise is responsible for much of the variance observed in postural sway, and (ii) the postural control system under the conditions tested resides in the regime of accurate velocity information.

Previous investigations using relatively large amplitude sensory stimuli or complete removal of sensory input have demonstrated non-linear processing of sensory information for postural control. In the present study, we asked whether a linear range of sensory fusion exists when smaller amplitude stimuli are used. The amplitudes of visual and somatosensory input were simultaneously co-varied within a trial. The postural responses were characterized by analyzing how the Fourier transform of postural sway at the driving frequency varied with sensory movement amplitudes. If the postural control system is linear with constant weighting of sensory inputs, then the pattern of Fourier transforms should be a linear function of movement amplitude. However, in 28 of 58 trials we observed non-linearity in this function. The results clearly show that even at very small amplitudes of sensory change, the nervous system processes multisensory information in a non-linear fashion.

Despite extensive research on the influence of visual, vestibular and somatosensory information on human postural control, it remains unclear how these sensory channels are fused for self-orientation. The focus of the present study was to test whether a linear additive model could account for the fusion of touch and vision for postural control. We simultaneously manipulated visual and somatosensory (touch) stimuli in five conditions of single- and multisensory stimulation. The visual stimulus was a display of random dots projected onto a screen in front of the standing subject. The somatosensory stimulus was a rigid plate which subjects contacted lightly (

The goal of the present study was to determine the properties of the somatosensory stimulus that alter its temporal coupling to body sway. Six standing subjects were tested while touching a metal plate positioned either directly in front of or lateral to the subject. In each condition, the plate moved 4 mm at 0.2 Hz in either the medial-lateral (ML) or anterior-posterior direction (AP). The results showed that coupling between body sway and touch plate movement was strongest when the touch plate moved in a direction along the longitudinal axis of the arm. Coupling strength was weaker when the touch plate moved perpendicular to the longitudinal axis of the arm. The results consistently show that a radial expansion stimulus was more effective than a lamellar-type stimulus at the fingertip. Moreover, somatosensory information from a surface is interpreted in terms of the orientation of the contact limb and the potential degrees of freedom available through its movement.

There is a large general interest in the development of brain-computer interactive technologies (BCIT), which couple the power of machines with the complexity and flexibility of the human brain. However, currently there is a gap between a general understanding of brain function and related neuroimaging tools, which is mostly limited to highly trained neuroscientists and engineers who wish to capitalize on this knowledge base. This program proposal outlines a three-pronged approach aimed to help bridge this gap while simultaneously spurring public interest in the BCIT field. It entails: (1) development and distribution of low-cost, modular electroencephalography (EEG) headsets, which can be assembled and used by a novice, (2) development of a series of tutorial modules on EEG data collection intended for users to become armature developers of BCIT applications, and (3) a Grand Challenge series centered around the use of crowdsourcing as a means of widespread application development. Included are some programmatic suggestions, as well as exemplar applications to fit this end goal.

This report describes the design and functionality of version 0.2 of the Graph-theoretic Interactive Narrative (Gin) System, a system intended to increase interactivity of virtual reality environments (VEs) used for human experimentation, while still allowing for necessary experimental constraints to be enforced so that the data can be analyzed. An initial step towards increasing interactivity is to allow human subjects to control their own navigation through the VE. In order to make this initial step, the Gin system uses the graph theory to model the topography of the VE in an abstract fashion; monitor the movement of subjects through the environment; and add, move, activate, deactivate, and/or remove experimental stimuli so that the experimental subjects are exposed to the stimuli in the way that the experiment designer intends for them to be. In the long term, the goal of the Gin system is to represent both a environmental topography and experimental context graph, allowing for both increased tractability of experimental scenario development, and increased tractability of experimental data analysis.

Future technologies such as Brain-Computer Interaction Technologies (BCIT) or affective Brain Computer Interfaces (aBCI) will need to function in an environment with higher noise and complexity than seen in traditional laboratory settings, and while individuals perform concurrent tasks. In this paper, we describe preliminary results from an experiment in a complex virtual environment. For analysis, we classify between a subject hearing and reacting to an audio stimulus that is addressed to them, and the same subject hearing an irrelevant audio stimulus. We performed two offline classifications, one using BCILab [1], the other using LibSVM [2]. Distinct classifiers were trained for each individual in order to improve individual classifier performance [3]. The highest classification performance results were obtained using individual frequency bands as features and classifying with an SVM classifier with an RBF kernel, resulting in mean classification performance of 0.67, with individual classifier results ranging from 0.60 to 0.79.

We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method. Copyright © 2011 by Walter de Gruyter, Berlin, Boston.

Tomorrows military systems will require novel methods for assessing Soldier performance and situational awareness (SA) in mobile operations involving mixed-initiative systems. Although new methods may augment Soldier assessments, they may also reduce Soldier performance as a function of demand on workload, requiring concurrent performance of mission and assessment tasks. The present paper describes a unique approach that supports assessment in environments approximating the operational context within which future systems will be deployed. A complex distributed system was required to emulate the operational environment. Separate computational and visualization systems provided an environment representative of the military operational context, including a 3D urban environment with dynamic human entities. Semi-autonomous driving was achieved with a simulated autonomous mobility system and SA was assessed through digital reports. A military crew station mounted on a 6-DOF motion simulator was used to create the physical environment. Cognitive state evaluation was enabled using physiological monitoring. Analyses indicated individual differences in temporal and accuracy components when identifying key features of potential threats; i.e., comparing Soldiers and insurgents with non-insurgent civilians. The assessment approach provided a natural, operationally-relevant means of assessing needs of future secure mobility systems and detecting key factors affecting Soldier-system performance as foci for future development.

An important question for ecologically valid virtual environments is whether cohort characteristics affect immersion. If a method for assessing a certain neurocognitive capacity (e.g. attentional processing) is adapted to a cohort other than the one that was used for the initial normative distribution, data obtained in the new cohort may not be reflective of the neurocognitive capacity in question. We assessed the psychophysiological impact of different levels of immersion upon persons from two cohorts: 1) civilian university students; and 2) West Point Cadets. Cadets were found to have diminished startle eyeblink amplitude compared with civilians, which may reflect that cadets experienced less negative affect during the scenario in general. Further, heart rate data revealed that Cadets had significantly lower heart rates than Civilians in the "low" but not "high" immersion condition. This suggests that "low" immersion conditions may not have the ecological validity necessary to evoke consistent affect across cohorts.

We examined the generally held belief that the postural control system is able to re-weight its available sensory inputs in order to optimize stance control in altered sensory environments. Our view is that previous accounts of sensory re-weighting provide only indirect evidence, which is subject to alternative explanations. The present results provide strong evidence for sensory re-weighting as the primary mechanism for changes observed in postural sway between conditions. Subjects were presented with small-amplitude, oscillatory visual and somatosensory stimuli at 0.20 and 0.28 Hz, respectively, in five conditions that manipulated the amplitudes of stimulus motion. Gain calculated in each trial with respect to each of the two stimuli was found to change systematically as stimulus motion amplitudes changed across condition. The observed pattern of gain rules out a constant-weight, linear account of posture and is consistent with the re-weighting hypothesis. Parameter fits of a third-order, linear stochastic model to postural sway trajectories in each condition showed that changes in gain across condition were primarily due to changes in coupling coefficients rather than changes in parameters that characterize the stability of the postural system. Visual gain was found to depend upon visual motion amplitude and touch gain was found to depend upon touch motion amplitude, indicating intra-modality dependencies. Visual gain also depended upon touch motion amplitude, indicating an inter-modality dependence. To our knowledge, simultaneous re-weighting of more than one sensory input has never been rigorously demonstrated. These techniques may be able to resolve the source of balance control deficits across populations with far more certainty than currently possible.

The propositions that the senses are separate and that the global array may be sufficient for adequate perception are questioned. There is evidence that certain tasks may be primarily “input-driven,” but these are a special case along the behavioral continuum. Many tasks involve sensory information that is ambiguous, and other sources of information may be required for adequate perception.

Jeka, John, Kelvin Oie, Gregor Schöner, Tjeerd Dijkstra, and Elaine Henson. Position and velocity coupling of postural sway to somatosensory drive. J. Neurophysiol. 79: 1661–1674, 1998. Light touch contact of a fingertip to a stationary surface provides orientation information that enhances control of upright stance. Slight changes in contact force at the fingertip lead to sensory cues about the direction of body sway, allowing attenuation of sway. In the present study, the coupling of postural sway to a moving contact surface was investigated in detail. Head, center of mass, and center of pressure displacement were measured as the contact surface moved rhythmically at 0.1, 0.2, 0.4, 0.6, and 0.8 Hz. Stimulus amplitude decreased with frequency to maintain peak velocity constant across frequency. Head and body sway were highly coherent with contact surface motion at all frequencies except 0.8 Hz, where a drop-off in coherence was observed. Mean frequency of head and body sway matched the driving frequency ≤0.4 Hz. At higher frequencies, non-1:1 coupling was evident. The phase of body sway relative to the touch plate averaged 20–30° at 0.1-Hz drive and decreased approximately linearly to −130° at 0.8-Hz drive. System gain was ∼1 across frequency. The large phase lags observed cannot be accounted for with velocity coupling alone but indicate that body sway also was coupled to the position of the touch plate. Fitting of a linear second-order model to the data suggests that postural control parameters are not fixed but adapt to the moving frame of reference. Moreover, coupling to both position and velocity suggest that a spatial reference frame is defined by the somatosensory system.