Real-time human-robot interaction underlying neurorobotic trust and intent recognition.

In the past three decades, the interest in trust has grown significantly due to its important role in our modern society. Everyday social experience involves "confidence" among people, which can be interpreted at the neurological level of a human brain. Recent studies suggest that oxytocin is a centrally-acting neurotransmitter important in the development and alteration of trust. Its administration in humans seems to increase trust and reduce fear, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unknown. We propose the first biologically realistic model for trust, simulating spiking neurons in the cortex in a real-time human-robot interaction simulation. At the physiological level, oxytocin cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. As trust was established in the simulation, this architecture had a direct inhibitory effect on the amygdala tonic firing, which resulted in a willingness to exchange an object from the trustor (virtual neurorobot) to the trustee (human actor). Our software and hardware enhancements allowed the simulation of almost 100,000 neurons in real time and the incorporation of a sophisticated Gabor mechanism as a visual filter. Our brain was functional and our robotic system was robust in that it trusted or distrusted a human actor based on movement imitation.

A circuit-level model of hippocampal place field dynamics modulated by entorhinal grid and suppression-generating cells.

Hippocampal "place cells" and the precession of their extracellularly recorded spiking during traversal of a "place field" are well-established phenomena. More recent experiments describe associated entorhinal "grid cell" firing, but to date only conceptual models have been offered to explain the potential interactions among entorhinal cortex (EC) and hippocampus. To better understand not only spatial navigation, but mechanisms of episodic and semantic memory consolidation and reconsolidation, more detailed physiological models are needed to guide confirmatory experiments. Here, we report the results of a putative entorhinal-hippocampal circuit level model that incorporates recurrent asynchronous-irregular non-linear (RAIN) dynamics, in the context of recent in vivo findings showing specific intracellular-extracellular precession disparities and place field destabilization by entorhinal lesioning. In particular, during computer-simulated rodent maze navigation, our model demonstrate asymmetric ramp-like depolarization, increased theta power, and frequency (that can explain the phase precession disparity), and a role for STDP and K(AHP) channels. Additionally, we propose distinct roles for two entorhinal cell populations projecting to hippocampus. Grid cell populations transiently trigger place field activity, while tonic "suppression-generating cell" populations minimize aberrant place cell activation, and limit the number of active place cells during traversal of a given field. Applied to place-cell RAIN networks, this tonic suppression explains an otherwise seemingly discordant association with overall increased firing. The findings of this circuit level model suggest in vivo and in vitro experiments that could refute or support the proposed mechanisms of place cell dynamics and modulating influences of EC.

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON.

As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments to analysis and visualizations. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specification problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in the simulator's native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation.

Functional polymorphism of detoxification gene NQO1 predicts intensity of empirical treatment of childhood asthma.

The management of moderate to severe childhood asthma remains empirical. Genotypic variation has been proposed as a way to tailor specific pharmaceutical regimens to individual patients. The objective of this study was to determine the factors associated with asthma treatment progression, including functional polymorphisms of phase II detoxification enzymes, demographics, and environmental factors. In a study of 120 asthmatic children cared for in a single pediatric pulmonary practice, intensity of medical treatment over the year prior was modeled as a function of null mutations of glutathione S transferase (GST) M1 and T1, ile105val variant of GSTP1, and pro187ser variant of NAD(P)H:quinone oxidoreductase 1 (NQO1). The model included demographics, medical information, and environmental factors obtained via questionnaire analyzed with multivariate logistic regression and artificial neural networks. Multivariate logistic regression with bootstrapped validation identified a polymorphic variant of NQO1 as significantly contributing to increasing the odds of receiving more aggressive medical therapy (odds ratio, 11.56; p=0.0001). Parent income and education inversely correlated with medical treatment (odds ratio, 1.50; p=0.001 and odds ratio, 0.375; p=0.002, respectively). Age and reporting restricted physical activity due to asthma also impacted medical treatment (odds ratio, 0.63; p=0.0001 and odds ratio, 5.90; p=0.004, respectively). The optimism-adjusted discriminative ability (c-index) of the model was 0.881 (close to Bayes optimum of 0.902) with 80% overall classification accuracy. Our study supports the role of NQO1 polymorphism as an important factor determining the intensity of medical therapy in asthmatic children after adjusting for significance relating to parental income and education level, age, and restricted physical activity. Asthmatic children with a functional polymorphism of NQO1 may require more intensive pharmaceutical treatment to effectively control their asthma.

Framework and implications of virtual neurorobotics.

Despite decades of societal investment in artificial learning systems, truly "intelligent" systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain "algorithm" itself-trying to replicate uniquely "neuromorphic" dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or "avatars", to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications.

Clinical hospital medicine fellowships: perspectives of employers, hospitalists, and medicine residents.

BACKGROUND: Clinical hospital medicine fellowships could accelerate the acquisition of increasingly demanding skills while enhancing esteem among subspecialty peers. We sought to determine whether medicine residents perceived such fellowships as relevant and would be willing to forgo substantial income during the training period, in the context of the perspectives of employers and practicing hospitalists. DESIGN: A series of 3 tandem nationwide cross-sectional surveys conducted over the Internet during late 2005 and early 2006. METHODS: Survey I was sent to 195 hospitalist employers identified through filtering classified advertisements. Survey II (containing Survey I results) was E-mailed by the Society of Hospital Medicine to its practicing hospitalists members. Survey III (containing results of the first 2 surveys) was E-mailed to U.S. internal medicine program directors for forwarding to their residents. RESULTS: Two-thirds of 103 employers would offer either a signing bonus or a starting salary increase of at least $10,000 to fellowship graduates (more than 20% would pay at least $20,000 more in the salary). Based on a median experience of 8 years, 91% of 101 practicing hospitalists believed that clinical fellowship could at least possibly be a favorable career move, with 58% recommending it as being a probably or strongly favorable career move. Of 279 categorical medicine residents, 44% were considering a hospital medicine career, of whom 57% would consider doing a year of clinical fellowship training if available. CONCLUSIONS: This study reveals a potentially unmet demand for clinical hospital medicine fellowship training. Further determination of need and related curricular development could be addressed under the leadership of national hospital medicine educational organizations.

Simulation of networks of spiking neurons: a review of tools and strategies.

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures.

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly "intelligent" systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material.

The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury.

OBJECTIVE: This study compared the accuracy of artificial neural networks to multiple regression and classification and regression trees in predicting outcomes of 1,644 patients in the Traumatic Brain Injury Model Systems database 1 year after injury. METHODS: Data from rehabilitation admission were used to predict discharge scores on the Functional Independence Measure, the Disability Rating Scale, and the Community Integration Questionnaire. RESULTS: Artificial neural networks did not demonstrate greater accuracy in predicting outcomes than did the more widely used method of multiple regression. Both of these methods outperformed classification and regression trees. CONCLUSION: Because of the sophisticated form of multiple regression with splines that was used, firm conclusions are limited about the relative accuracy of artificial neural networks compared to more widely used forms of multiple regression.

Fast oscillations trigger bursts of action potentials in neocortical neurons in vitro: a quasi-white-noise analysis study.

PURPOSE: Recent evidence supports the importance of action potential bursts in physiological neural coding, as well as in pathological epileptogenesis. To better understand the temporal dynamics of neuronal input currents that trigger burst firing, we characterized spectral patterns of stimulation current that generate bursts of action potentials from regularly spiking neocortical neurons in vitro. METHODS: Sharp microelectrodes were used for intracellular recording and stimulation of cortical neurons in rat brain slices. Quasi-white-noise (0-2 kHz) and "chirp" sine wave currents of decreasing wavelength were applied to represent a broad spectrum of stimulation frequencies. Action potential-related averaging of the stimulation current variations preceding bursting was used to characterize stimulation current patterns more likely to result in a burst rather than a single-spike response. RESULTS: Bursts of action potentials were most reliably generated by a preceding series of > or = 2 positive current transients at 164+/-37 Hz of the quasi-white-noise, and to sine wave currents with frequencies greater than 90 Hz. The intraburst action potential rate was linearly related to the frequency of the input sine wave current. CONCLUSIONS: This study demonstrates that regularly spiking cortical neurons in vitro burst in response to fast oscillations of input currents. In the presence of positive cortical feedback loops, encoding input frequency in the intraburst action potential rate may be safer than producing a high-frequency regular output spike train. This leads to the experimentally testable and therapeutically important hypothesis that burst firing could be an antiepileptogenic and/or anti-ictogenic mechanism.

Heterogeneity in the pyramidal network of the medial prefrontal cortex.

The prefrontal cortex is specially adapted to generate persistent activity that outlasts stimuli and is resistant to distractors, presumed to be the basis of working memory. The pyramidal network that supports this activity is unknown. Multineuron patch-clamp recordings in the ferret medial prefrontal cortex showed a heterogeneity of synapses interconnecting distinct subnetworks of different pyramidal cells. One subnetwork was similar to the pyramidal network commonly found in primary sensory areas, consisting of accommodating pyramidal cells interconnected with depressing synapses. The other subnetwork contained complex pyramidal cells with dual apical dendrites displaying nonaccommodating discharge patterns; these cells were hyper-reciprocally connected with facilitating synapses displaying pronounced synaptic augmentation and post-tetanic potentiation. These cellular, synaptic and network properties could amplify recurrent interactions between pyramidal neurons and support persistent activity in the prefrontal cortex.

Boolean search experience and abilities of medical students and practicing physicians.

BACKGROUND: Little is known about physician ability to utilize Boolean search skills to access information. PURPOSE: Determine the proficiency of medical students and practicing physicians to identify efficient Boolean phrases. METHODS: Experiential survey and multiple-choice questions administered to 49 4th-year medical students and 42 practicing physicians. Subjects identified the best answer or correctly ranked 3 Boolean search phrase options. RESULTS: Practicing physicians identified the single best query phrase significantly more often than did medical students (85.7% vs. 75.0%, p < 0.001), and both groups had significantly more difficulty correctly rank-ordering the queries (students, 75% vs. 54%, p < 0.001; practitioners, 85.7% vs. 57.1%, p < .04). Only recent MEDLINE use was an independent predictor of accuracy in both groups. CONCLUSION: Students and physicians demonstrated deficiencies in identifying optimal Boolean phrases. Although formal instruction has not demonstrated clear improvement in skills, more creative teaching of Boolean search techniques should be undertaken and tested.