Bulletin of the American Physical Society
2008 APS March Meeting
Volume 53, Number 2
Monday–Friday, March 10–14, 2008; New Orleans, Louisiana
Session Y36: Focus Session: Artificial Neurons |
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Sponsoring Units: FIAP Chair: Unil Perera, Georgia State University Room: Morial Convention Center 228 |
Friday, March 14, 2008 11:15AM - 11:51AM |
Y36.00001: Understanding the dynamical control of animal movement Invited Speaker: Over the last 50 years, neurophysiologists have described many neural circuits that transform sensory input into motor commands, while biomechanicians and behavioral biologists have described many patterns of animal movement that occur in response to sensory input. Attempts to link these two have been frustrated by our technical inability to record from the necessary neurons in a freely behaving animal. As a result, we don't know how these neural circuits function in the closed loop context of free behavior, where the sensory and motor context changes on a millisecond time-scale. To address this problem, we have developed a software package, AnimatLab (www.AnimatLab.com), that enables users to reconstruct an animal's body and its relevant neural circuits, to link them at the sensory and motor ends, and through simulation, to test their ability to reproduce appropriate patterns of the animal's movements in a simulated Newtonian world. A Windows-based program, AnimatLab consists of a neural editor, a body editor, a world editor, stimulus and recording facilities, neural and physics engines, and an interactive 3-D graphical display. We have used AnimatLab to study three patterns of behavior: the grasshopper jump, crayfish escape, and crayfish leg movements used in postural control, walking, reaching and grasping. In each instance, the simulation helped identify constraints on both nervous function and biomechanical performance that have provided the basis for new experiments. Colleagues elsewhere have begun to use AnimatLab to study control of paw movements in cats and postural control in humans. We have also used AnimatLab simulations to guide the development of an autonomous hexapod robot in which the neural control circuitry is downloaded to the robot from the test computer. [Preview Abstract] |
Friday, March 14, 2008 11:51AM - 12:03PM |
Y36.00002: Biomechanical Analysis of Locust Jumping in a Physically Realistic Virtual Environment David Cofer, Gennady Cymbalyuk, William Heitler, Donald Edwards The biomechanical and neural components that underlie locust jumping have been extensively studied. Previous research suggested that jump energy is stored primarily in the extensor apodeme, and in a band of cuticle called the semi-lunar process (SLP). As it has thus far proven impossible to experimentally alter the SLP without rendering a locust unable to jump, it has not been possible to test whether the energy stored in the SLP has a significant impact on the jump. To address problems such as this we have developed a software toolkit, AnimatLab, which allows researchers to build and test virtual organisms. We used this software to build a virtual locust, and then asked how the SLP is utilized during jumping. The results show that without the SLP the jump distance was reduced by almost half. Further, the simulations were also able to show that loss of the SLP had a significant impact on the final phase of the jump. We are currently working on postural control mechanisms for targeted jumping in locust. [Preview Abstract] |
Friday, March 14, 2008 12:03PM - 12:15PM |
Y36.00003: Design of real-time locomotion generator with map-based neuronal models Nikolai Rulkov, Joseph Ayers, Mark Hunt We are developing an electronic nervous system for a biomimetic robot based on an established neurobiological model system, the Sea Lamprey. Undulatory locomotion of the lamprey is coordinated by a concatenated network of over 100 segmental central pattern generators (CPGs). To achieve real time operation in a DSP chip, we are using simple phenomenological models of neurons and synapses based on the dynamics of nonlinear maps. CPG networks based on known neuronal circuitry have replicated main properties of the dynamical behavior of the animal model. The results of numerical studies of the neuronal activity coordinating various swimming patterns in the reduced model of the CPG are considered. Both ascending and descending connections between segmental CPGs can mediate both forward and backward propagating flexion waves based on anterior or posterior bias by descending premotor commands. Bilaterally asymmetric biases of descending commands can mediate turning. The CPG outputs control 5 shape memory alloy actuators on each side to generate coordinated undulations. Two dorsal and ventral pitch actuators control the angle between the hull and undulator to control dive and climb. Descending commands are modulated by an analog compass, inclinometers, accelerometers and a short baseline sonar array to mediate homing by the vehicle on a sonar beacon. [Preview Abstract] |
Friday, March 14, 2008 12:15PM - 12:27PM |
Y36.00004: Genesis and synchronization properties of fast neural oscillations Maxim Bazhenov, Nikolai Rulkov Fast neural network oscillations in gamma (30-80 Hz) range are associated with attentiveness and sensory perception and have strong relation to both cognitive processing and temporal binding of sensory stimuli. These oscillations are found in different brain systems including cerebral cortex, hippocampus and olfactory bulb. Cortical gamma oscillations may become synchronized within 1-2 msec over distances up to a few millimeters. In this study we used computational network models to analyze basic synaptic mechanisms and synchronization properties of fast neural oscillations. Using the network models of synaptically coupled pyramidal neurons (up to 500,000 cells) and fast spiking interneurons (up to 125,000 cells) we found that the strength of feedback inhibition determined the network synchronization state: either global network oscillations with near zero phase lag between remote sites or waves of gamma activity propagating through the network. Long-range excitatory connections between pyramidal cells were not required for long-range synchronization. The model predicts that local inhibitory circuits can mediate global network synchronization with phase delays being much smaller than activity propagation time between remote network sites. [Preview Abstract] |
Friday, March 14, 2008 12:27PM - 12:39PM |
Y36.00005: Periodic vs. Transient Estimation of Phase Response Curves Jianxia Cui, Srisairam Achuthan, Carmen Canavier, Robert Butera 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 a spike. Experimental FPRCs in \textit{Aplysia} pacemaker neurons were different from single pulse PRCs because of adaptation. Adaptation was incorporated by plotting the effective period, observed just after the pulse train is terminated, as a function of the entrained period during the pulse train. The effective intrinsic period was used iteratively in the prediction method instead of the unperturbed intrinsic period. Incorporating adaptation improved the accuracy of prediction of phase-locked modes in a model network of adapting oscillators characterized by both single pulse and multiple pulse PRCs compared to those characterized by single pulse PRCs alone. [Preview Abstract] |
Friday, March 14, 2008 12:39PM - 12:51PM |
Y36.00006: Homoclinic Spike adding in a neuronal model in the presence of noise Ibiyinka Fuwape, Alexander Neiman, Andrey Shilnikov We study the influence of noise on a spike adding transitions within the bursting activity in a Hodgkin-Huxley-type model of the leech heart interneuron. Spike adding in this model occur via homoclinic bifurcation of a saddle periodic orbit. Although narrow chaotic regions are observed near bifurcation transition, overall bursting dynamics is regular and is characterized by a constant number of spikes per burst. Experimental studies, however, show variability of bursting patterns whereby number of spikes per burst varies randomly. Thus, introduction of external synaptic noise is a necessary step to account for variability of burst durations observed experimentally. We show that near every such transition the neuron is highly sensitive to random perturbations that lead to and enhance broadly the regions of chaotic dynamics of the cell. For each spike adding transition there is a critical noise level beyond which the dynamics of the neuron becomes chaotic throughout the entire region of the given transition. Noise-induced chaotic dynamics is characterized in terms of the Lyapunov exponents and the Shannon entropy and reflects variability of firing patterns with various numbers of spikes per burst, traversing wide range of the neuron's parameters [Preview Abstract] |
Friday, March 14, 2008 12:51PM - 1:27PM |
Y36.00007: Adaptive Neurotechnology for Making Neural Circuits Functional . Invited Speaker: Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions. [Preview Abstract] |
Friday, March 14, 2008 1:27PM - 1:39PM |
Y36.00008: Investigating the Dynamics of Functional Brain Networks with MRI Shella Keilholz, Waqas Majeed Functional Magnetic Resonance Imaging (fMRI) is sensitive to changes in blood oxygenation levels. While fMRI has traditionally mapped changes in these levels that localize to brain areas activated by an external stimulus, recent work has focused on detecting correlated, non-stimulus-related fluctuations in the fMRI signal throughout the brain. These fluctuations are believed to arise from spontaneous variations in local neural activity, and so correlated fluctuations from different brain areas may indicate coordinated activity. Maps of ``functional connectivity'' based upon these fluctuations show reproducible patterns of correlated signals. To date, research has focused on steady-state networks that persist over the entire imaging session (minutes). We are exploring the possibility of detecting changes in network activity on much shorter time scales (seconds). Preliminary analysis shows that power in the frequency band used to map functional connectivity varies over time, and that power differences correspond to changes in correlation between areas. We also detected phase differences in fluctuations that are consistent with propagating waves. These results indicate that time-varying analysis of fMRI data may provide insight into the dynamics of functional networks in the brain. [Preview Abstract] |
Friday, March 14, 2008 1:39PM - 1:51PM |
Y36.00009: Estimating Granger causality from Fourier and wavelet transforms of time series data Mukesh Dhamala Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. We have recently extended the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directionalinfluences. We illustrate the utility of the proposed methods using artificial data and real brain data . [Preview Abstract] |
Friday, March 14, 2008 1:51PM - 2:03PM |
Y36.00010: Anomalous Effect of Surface Diffusion on NMR Signal: Tracing the Fiber Geometry Vadym Apalkov, Neranjan Edirisinghe, Gennady Cymbalyuk We show the strong qualitative effect of the surface diffusion channel on the echo attenuation of the NMR signal from restricted geometry, e.g. fiber system. In some range of parameters of the system the residual echo signal, which is obtained by subtracting the background value, can have anomalous behavior, which means that the echo signal has a maximum value at some finite value of the magnitude of the gradient pulses. This fact can be used to enhance the accuracy of the measurements by studying the echo signal around the maximum value. Effect described here could be also used for tuning the MRI measurements to trace fibers with particular characteristic diameters or for timely detection of changes in the diffusion coefficients and fiber diameters. [Preview Abstract] |
Friday, March 14, 2008 2:03PM - 2:15PM |
Y36.00011: Application of real time systems to the analysis of neuronal dynamics Gennady Cymbalyuk, Andrey Shilnikov Neurons exhibit various activity regimes and transitions in between. The central pattern generator controlling the leech's heartbeat contains identified pairs of mutually inhibitory neurons (Calabrese et al. 1995). We describe real time systems approaches to the analysis of their activity. The hybrid system consists of a living neuron and a model neuron (or an artificial silicon neuron) interacting in the real time. Dynamic clamp is used to implement artificial ionic currents and synapses in the system (Sharp et al. 1993). Our study determines the mechanisms underlying and regulating bursting activity, based on intrinsic membrane dynamics and network interactions. The complexity of endogenous dynamics originates from the diversity of ionic currents operating on different time scales. Hybrid system analysis and slow-fast dynamical systems analysis have been combined in our studies of bursting, its origin and transformations in heart interneurons both as single cells and in the mutually inhibitory configuration. [Preview Abstract] |
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