[Show abstract][Hide abstract] ABSTRACT: In order to study the ability of coupled neural oscillators to synchronize in the presence of intrinsic as opposed to synaptic noise, we constructed hybrid circuits consisting of one biological and one computational model neuron with reciprocal synaptic inhibition using the dynamic clamp. Uncoupled, both neurons fired periodic trains of action potentials. Most coupled circuits exhibited qualitative changes between one-to-one phase-locking with fairly constant phasic relationships and phase slipping with a constant progression in the phasic relationships across cycles. The phase resetting curve (PRC) and intrinsic periods were measured for both neurons, and used to construct a map of the firing intervals for both the coupled and externally forced (PRC measurement) conditions. For the coupled network, a stable fixed point of the map predicted phase locking, and its absence produced phase slipping. Repetitive application of the map was used to calibrate different noise models to simultaneously fit the noise level in the measurement of the PRC and the dynamics of the hybrid circuit experiments. Only a noise model that added history-dependent variability to the intrinsic period could fit both data sets with the same parameter values, as well as capture bifurcations in the fixed points of the map that cause switching between slipping and locking. We conclude that the biological neurons in our study have slowly-fluctuating stochastic dynamics that confer history dependence on the period. Theoretical results to date on the behavior of ensembles of noisy biological oscillators may require re-evaluation to account for transitions induced by slow noise dynamics.
[Show abstract][Hide abstract] ABSTRACT: The injection of computer-simulated conductances through the dynamic clamp technique has allowed researchers to probe the intercellular and intracellular dynamics of cardiac and neuronal systems with great precision. By coupling computational models to biological systems, dynamic clamp has become a proven tool in electrophysiology with many applications, such as generating hybrid networks in neurons or simulating channelopathies in cardiomyocytes. While its applications are broad, the approach is straightforward: synthesizing traditional patch clamp, computational modeling, and closed-loop feedback control to simulate a cellular conductance. Here, we present two example applications: artificial blocking of the inward rectifier potassium current in a cardiomyocyte and coupling of a biological neuron to a virtual neuron through a virtual synapse. The design and implementation of the necessary software to administer these dynamic clamp experiments can be difficult. In this chapter, we provide an overview of designing and implementing a dynamic clamp experiment using the Real-Time eXperiment Interface (RTXI), an open-source software system tailored for real-time biological experiments. We present two ways to achieve this using RTXI's modular format, through the creation of a custom user-made module and through existing modules found in RTXI's online library.
[Show abstract][Hide abstract] ABSTRACT: This article summarizes the discussions held during the first IEEE Life Sciences Grand Challenges Conference held on October 4-5, 2012 at the National Academy of Sciences in Washington DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach towards engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas, including engineering the brain and nervous system, engineering the cardiovascular system, engineering of cancer diagnostics, therapeutics, and prevention, translation of discoveries to clinical applications, and education and training. A number of these challenges are identified and summarized in this article.
IEEE transactions on bio-medical engineering 02/2013; · 2.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Neuromodulators, such as amines and neuropeptides, alter the activity of neurons and neuronal networks. In this work, we investigate how neuromodulators, which activate G(q)-protein second messenger systems, can modulate the bursting frequency of neurons in a critical portion of the respiratory neural network, the pre-Bötzinger complex (preBötC). These neurons are a vital part of the ponto-medullary neuronal network, which generates a stable respiratory rhythm whose frequency is regulated by neuromodulator release from the nearby Raphe nucleus. Using a simulated 50-cell network of excitatory preBötC neurons with a heterogeneous distribution of persistent sodium conductance and Ca(2+), we determined conditions for frequency modulation in such a network by simulating interaction between Raphe and preBötC nuclei. We found that the positive feedback between the Raphe excitability and preBötC activity induces frequency modulation in the preBötC neurons. In addition, the frequency of the respiratory rhythm can be regulated via phasic release of excitatory neuromodulators from the Raphe nucleus. We predict that the application of a G(q) antagonist will eliminate this frequency modulation by the Raphe and keep the network frequency constant and low. In contrast, application of a G(q) agonist will result in a high frequency for all levels of Raphe stimulation. Our modeling results also suggest that high [K(+)] requirement in respiratory brain slice experiments may serve as a compensatory mechanism for low neuromodulatory tone.
[Show abstract][Hide abstract] ABSTRACT: This manuscript describes a pilot study in ethics education employing a problem-based learning approach to the study of novel, complex, ethically fraught, unavoidably public, and unavoidably divisive policy problems, called "fractious problems," in bioscience and biotechnology. Diverse graduate and professional students from four US institutions and disciplines spanning science, engineering, humanities, social science, law, and medicine analyzed fractious problems employing "navigational skills" tailored to the distinctive features of these problems. The students presented their results to policymakers, stakeholders, experts, and members of the public. This approach may provide a model for educating future bioscientists and bioengineers so that they can meaningfully contribute to the social understanding and resolution of challenging policy problems generated by their work.
Science and Engineering Ethics 03/2012; · 0.74 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Conduction block using high-frequency alternating current (HFAC) stimulation has been shown to reversibly block conduction through various nerves. However, unlike simulations and experiments on myelinated fibers, prior experimental work in our lab on the sea-slug, Aplysia, found a nonmonotonic relationship between frequency and blocking thresholds in the unmyelinated fibers. To resolve this discrepancy, we investigated the effect of HFAC waveforms on the compound action potential of the sciatic nerve of frogs. Maximal stimulation of the nerve produces a compound action potential consisting of the A-fiber and C-fiber components corresponding to the myelinated and unmyelinated fibers' response. In our study, HFAC waveforms were found to induce reversible block in the A-fibers and C-fibers for frequencies in the range of 5-50 kHz and for amplitudes from 0.1-1 mA. Although the A-fibers demonstrated the monotonically increasing threshold behavior observed in published literature, the C-fibers displayed a nonmonotonic relationship, analogous to that observed in the unmyelinated fibers of Aplysia. This differential blocking behavior observed in myelinated and unmyelinated fibers during application of HFAC waveforms has diverse implications for the fields of selective stimulation and pain management.
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 08/2011; 19(5):550-7. · 2.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The network of coupled neurons in the pre-Bötzinger complex (pBC) of the medulla generates a bursting rhythm, which underlies the inspiratory phase of respiration. In some of these neurons, bursting persists even when synaptic coupling in the network is blocked and respiratory rhythmic discharge stops. Bursting in inspiratory neurons has been extensively studied, and two classes of bursting neurons have been identified, with bursting mechanism depends on either persistent sodium current or changes in intracellular Ca(2+), respectively. Motivated by experimental evidence from these intrinsically bursting neurons, we present a two-compartment mathematical model of an isolated pBC neuron with two independent bursting mechanisms. Bursting in the somatic compartment is modeled via inactivation of a persistent sodium current, whereas bursting in the dendritic compartment relies on Ca(2+) oscillations, which are determined by the neuromodulatory tone. The model explains a number of conflicting experimental results and is able to generate a robust bursting rhythm, over a large range of parameters, with a frequency adjusted by neuromodulators.
Journal of Computational Neuroscience 06/2011; 30(3):515-28. · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Using two-cell and 50-cell networks of square-wave bursters, we studied how excitatory coupling of individual neurons affects the bursting output of the network. Our results show that the effects of synaptic excitation vs. electrical coupling are distinct. Increasing excitatory synaptic coupling generally increases burst duration. Electrical coupling also increases burst duration for low to moderate values, but at sufficiently strong values promotes a switch to highly synchronous bursts where further increases in electrical or synaptic coupling have a minimal effect on burst duration. These effects are largely mediated by spike synchrony, which is determined by the stability of the in-phase spiking solution during the burst. Even when both coupling mechanisms are strong, one form (in-phase or anti-phase) of spike synchrony will determine the burst dynamics, resulting in a sharp boundary in the space of the coupling parameters. This boundary exists in both two cell and network simulations. We use these results to interpret the effects of gap-junction blockers on the neuronal circuitry that underlies respiration.
Journal of Computational Neuroscience 05/2011; 31(3):701-11. · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A phase resetting curve (PRC) keeps track of the extent to which a perturbation at a given phase advances or delays the next spike, and can be used to predict phase locking in networks of oscillators. The PRC can be estimated by convolving the waveform of the perturbation with the infinitesimal PRC (iPRC) under the assumption of weak coupling. The iPRC is often defined with respect to an infinitesimal current as z(i)(ϕ), where ϕ is phase, but can also be defined with respect to an infinitesimal conductance change as z(g)(ϕ). In this paper, we first show that the two approaches are equivalent. Coupling waveforms corresponding to synapses with different time courses sample z(g)(ϕ) in predictably different ways. We show that for oscillators with Type I excitability, an anomalous region in z(g)(ϕ) with opposite sign to that seen otherwise is often observed during an action potential. If the duration of the synaptic perturbation is such that it effectively samples this region, PRCs with both advances and delays can be observed despite Type I excitability. We also show that changing the duration of a perturbation so that it preferentially samples regions of stable or unstable slopes in z(g)(ϕ) can stabilize or destabilize synchrony in a network with the corresponding dynamics.
Journal of Computational Neuroscience 04/2011; 30(2):373-90. · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A significant degree of heterogeneity in synaptic conductance is present in neuron to neuron connections. We study the dynamics of weakly coupled pairs of neurons with heterogeneities in synaptic conductance using Wang-Buzsaki and Hodgkin-Huxley model neurons which have Types I and II excitability, respectively. This type of heterogeneity breaks a symmetry in the bifurcation diagrams of equilibrium phase difference versus the synaptic rate constant when compared to the identical case. For weakly coupled neurons coupled with identical values of synaptic conductance a phase locked solution exists for all values of the synaptic rate constant, α. In particular, in-phase and anti-phase solutions are guaranteed to exist for all α. Heterogeneity in synaptic conductance results in regions where no phase locked solution exists and the general loss of the ubiquitous in-phase and anti-phase solutions of the identically coupled case. We explain these results through examination of interaction functions using the weak coupling approximation and an in-depth analysis of the underlying multiple cusp bifurcation structure of the systems of coupled neurons.
Journal of Computational Neuroscience 04/2011; 30(2):455-69. · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Multistability, the coexistence of multiple attractors in a dynamical system, is explored in bursting nerve cells. A modeling study is performed to show that a large class of bursting systems, as defined by a shared topology when represented as dynamical systems, is inherently suited to support multistability. We derive the bifurcation structure and parametric trends leading to multistability in these systems. Evidence for the existence of multirhythmic behavior in neurons of the aquatic mollusc Aplysia californica that is consistent with our proposed mechanism is presented. Although these experimental results are preliminary, they indicate that single neurons may be capable of dynamically storing information for longer time scales than typically attributed to nonsynaptic mechanisms.
[Show abstract][Hide abstract] ABSTRACT: The Real-time Experiment Interface (RTXI) is a fast and versatile real-time biological experimentation system based on Real-Time Linux. RTXI is open source and free, can be used with an extensive range of experimentation hardware, and can be run on Linux or Windows computers (when using the Live CD). RTXI is currently used extensively for two experiment types: dynamic patch clamp and closed-loop stimulation pattern control in neural and cardiac single cell electrophysiology. RTXI includes standard plug-ins for implementing commonly used electrophysiology protocols with synchronized stimulation, event detection, and online analysis. These and other user-contributed plug-ins can be found on the website (http://www.rtxi.org).
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4160-3.
[Show abstract][Hide abstract] ABSTRACT: The widespread adoption of neural prosthetic devices is currently hindered by our inability to reliably record neural signals from chronically implanted electrodes. The extent to which the local tissue response to implanted electrodes influences recording failure is not well understood. To investigate this phenomenon, impedance spectroscopy has shown promise for use as a non-invasive tool to estimate the local tissue response to microelectrodes. Here, we model impedance spectra from chronically implanted rats using the well-established Cole model, and perform a correlation analysis of modeled parameters with histological markers of astroglial scar, including glial fibrillary acid protein (GFAP) and 4',6-diamidino-2- phenylindole (DAPI). Correlations between modeled parameters and GFAP were significant for three parameters studied: Py value, R(o) and |Z|(1 kHz), and in all cases were confined to the first 100 microm from the interface. Py value was the only parameter also correlated with DAPI in the first 100 microm. Our experimental results, along with computer simulations, suggest that astrocytes are a predominant cellular player affecting electrical impedance spectra. The results also suggest that the largest contribution from reactive astrocytes on impedance spectra occurs in the first 100 microm from the interface, where electrodes are most likely to record electrical signals. These results form the basis for future approaches where impedance spectroscopy can be used to evaluate neural implants, evaluate strategies to minimize scar and potentially develop closed-loop prosthetic devices.
Journal of Neural Engineering 11/2009; 6(5):055005. · 3.28 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The application of high-frequency alternating current (HFAC) stimulation to reversibly block conduction in peripheral nerves has been under investigation for decades. Computational studies have produced ambiguous results since they have been based on axon models that are perhaps not valid for the nerves in which the phenomenon has been demonstrated. Though simulations based on the Hodgkin-Huxley unmyelinated nerve cable model have been used to understand the phenomena, the isolated response of an unmyelinated nerve to HFAC waveforms has not been experimentally investigated. To understand the effect of HFAC waveforms in homogenous nerves, experiments were conducted on purely unmyelinated nerves of the sea-slug Aplysia californica. Sinusoidal waveforms in the range of 5-50 kHz were used to block the propagation of action potentials through the nerves. The time for complete recovery from block was found to be dependent on the duration of application of the HFAC waveform but was independent of the frequency of the waveform tested. Unlike data from simulations and experiments on myelinated nerves, the minimum HFAC amplitude for blocking conduction in these unmyelinated nerves exhibited a unique nonmonotonic relationship with frequency, which may be advantageous in various neurophysiological applications.
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 09/2009; 17(6):537-44. · 2.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phase response curves (PRCs) for a single neuron are often used to predict the synchrony of mutually coupled neurons. Previous theoretical work on pulse-coupled oscillators used single-pulse perturbations. We propose an alternate method in which functional PRCs (fPRCs) are generated using a train of pulses applied at a fixed delay after each spike, with the PRC measured when the phasic relationship between the stimulus and the subsequent spike in the neuron has converged. The essential information is the dependence of the recovery time from pulse onset until the next spike as a function of the delay between the previous spike and the onset of the applied pulse. Experimental fPRCs in Aplysia pacemaker neurons were different from single-pulse PRCs, principally due to adaptation. In the biological neuron, convergence to the fully adapted recovery interval was slower at some phases than that at others because the change in the effective intrinsic period due to adaptation changes the effective phase resetting in a way that opposes and slows the effects of adaptation. The fPRCs for two isolated adapting model neurons were used to predict the existence and stability of 1:1 phase-locked network activity when the two neurons were coupled. A stability criterion was derived by linearizing a coupled map based on the fPRC and the existence and stability criteria were successfully tested in two-simulated-neuron networks with reciprocal inhibition or excitation. The fPRC is the first PRC-based tool that can account for adaptation in analyzing networks of neural oscillators.
Journal of Neurophysiology 06/2009; 102(1):387-98. · 3.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The dynamic-clamp technique has been recognized and used by electrophysiologists for over 15 years. Nevertheless, only a small
number of papers have been written focusing on the performance and reliability of this protocol and how the accuracy of a
dynamic-clamp system can be assessed. Here we review the published literature to date, focusing on how experimental, computational,
and algorithmic factors contribute to the reliability of the dynamic-clamp protocol. Several of these papers point towards
a common and technologically realizable solution – the need for dynamic-clamp systems that run at computational rates much
faster (100–200kHz) than currently available. At present, dynamic-clamp rates are limited by the use of desktop computers
and rationalized by the kinetics of the model simulated. Recent results show that faster and lower latency systems would result
in a greater range of conductances that could be utilized, improved stability, and more accurate real-time model simulations.