Optimal solid state neurons

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.ISSN:2041-172

Robust online estimation of biophysical neural circuits

The control of neuronal networks, whether biological or neuromorphic, relies on tools for estimating parameters in the presence of model uncertainty. In this work, we explore the robustness of adaptive observers for neuronal estimation. Inspired by biology, we show that decentralization and redundancy help recover the performance of a centralized recursive mean square algorithm in the presence of uncertainty and mismatch on the internal dynamics of the model.Comment: 6 pages, 5 figures, accepted at the 62nd IEEE Conference on Decision and Contro

Data assimilation for conductance-based neuronal models

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the estimation problem for conductance-based neuron models is derived. In Chapter 2, these techniques are applied to a minimal conductance-based model, the Morris-Lecar model. This model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure and it is shown that the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation, and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. Chapter 3 extends the ideas of variational data assimilation to include a control term to relax the problem further in a process that is referred to as nudging from the geoscience community. The nudged 4D-Var is applied to twin experiments from a more complex, Hodgkin-Huxley-type two-compartment model for various time-sampling strategies. This controlled 4D-Var with nonuniform time-samplings is then applied to voltage traces from current-clamp recordings of suprachiasmatic nucleus neurons in diurnal rodents to improve upon our understanding of the driving forces in circadian (~24) rhythms of electrical activity. In Chapter 4 the complementary strengths of 4D-Var and UKF are leveraged to create a two-stage algorithm that uses 4D-Var to estimate fast timescale parameters and UKF for slow timescale parameters. This coupled approach is applied to data from a conductance-based model of neuronal bursting with distinctive slow and fast time-scales present in the dynamics. In Chapter 5, the ideas of identifiability and sensitivity are introduced. The Morris-Lecar model and a subset of its parameters are shown to be identifiable through the use of numerical techniques. Chapter 6 frames the selection of stimulus waveforms to inject into neurons during patch-clamp recordings as an optimal experimental design problem. Results on the optimal stimulus waveforms for improving the identifiability of parameters for a Hodgkin-Huxley-type model are presented. Chapter 7 shows the preliminary application of data assimilation for voltage-clamp, rather than current-clamp, data and expands on voltage-clamp principles to formulate a reduced assimilation problem driven by the observed voltage. Concluding thoughts are given in Chapter 8

Biomimetic Based Applications

The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions

Research and Technology Objectives and Plans Summary (RTOPS)

This publication represents the NASA research and technology program for FY-93. It is a compilation of the Summary portions of each of the RTOP's (Research and Technology Objectives and Plans) used for management review and control of research currently in progress throughout NASA. The RTOP Summary is designed to facilitate communication and coordination among concerned technical personnel in government, in industry, and in universities. The first section containing citations and abstracts of the RTOP's is followed by four indexes: Subject, Technical Monitor, Responsible NASA Organization, and RTOP Number

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs