An analogue recurrent neural networks for trajectory learning and other industrial applications

A real-time analogue recurrent neural network (RNN) can extract and learn the unknown dynamics (and features) of a typical control system such as a robot manipulator. The task at hand is a tracking problem in the presence of disturbances. With reference to the tasks assigned to an industrial robot, one important issue is to determine the motion of the joints and the effector of the robot. In order to model robot dynamics we use a neural network that can be implemented in hardware. The synaptic weights are modelled as variable gain cells that can be implemented with a few MOS transistors. The network output signals portray the periodicity and other characteristics of the input signal in unsupervised mode. For the specific purpose of demonstrating the trajectory learning capabilities, a periodic signal with varying characteristics is used. The developed architecture, however, allows for more general learning tasks typical in applications of identification and control. The periodicity of the input signal ensures convergence of the output to a limit cycle. Online versions of the synaptic update can be formulated using simple CMOS circuits. Because the architecture depends on the network generating a stable limit cycle, and consequently a periodic solution which is robust over an interval of parameter uncertainties, we currently place the restriction of a periodic format for the input signals. The simulated network contains interconnected recurrent neurons with continuous-time dynamics. The system emulates random-direction descent of the error as a multidimensional extension to the stochastic approximation. To achieve unsupervised learning in recurrent dynamical systems we propose a synapse circuit which has a very simple structure and is suitable for implementation in VLSI

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Synchronized Oscillations During Cooperative Feature Linking in a Cortical Model of Visual Perception

A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.Air Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); National Aeronautics and Space Administration (NGT-50497

Programming Synthetic Microbial Communities for Coexistence, Coordination, and Information Processing

Synthetic microbial communities offer a variety of potential advantages over single species approaches for many medical, industrial, and environmental applications. At the cellular level, metabolic pathways can be distributed amongst several community residents to lower the metabolic burden on individual cells and to enable optimization of reaction conditions for different parts of metabolic pathways. At the population level, diverse microbial communities in different natural contexts have been shown to be more productive, efficient, stable, and resistant to invasion by foreign agents. Along with these potential advantages, however, come a variety of new challenges as well. First, different species or cell types of interest must be able to coexist. Additionally, in many scenarios the relative abundance of each resident can impact the overall property of the community. Beyond coexistence and community composition, information processing and sharing is often essential to the types of complex, coordinated behavior that is required for many desired medical, industrial, and environmental applications. My dissertation has centered around the design and implementation of two novel systems which address some of the challenges discussed above that must be overcome to realize the potential of synthetic microbial communities for use in technological applications. In the first system our goal was to develop a tool that can be used to enable coexistence and program community composition within a synthetic microbial community. We use xvi temperature as a modality to enable coexistence of two microorganisms, Escherichia coli and Pseudomonas putida, with different thermal niches and to further program the composition of this model synthetic bi-culture. Specifically, I developed two different approaches, referred to as a constant temperature regime and a cycling temperature regime. Employing a combination of wet-lab experiments and mathematical modeling, I showed that a variety of parameters such as temperature, cycle duration, etc. can be manipulated to achieve desired community compositions. Building on this work, I then used a mathematical framework developed by ecologists to explore design principles and specific mechanisms underlying the observed relationship between culture temperature and coexistence. In the second system, I designed a novel synthetic microbial community with a distributed sensing and centralized reporting architecture that is enabled by what we have termed bacteriophage-mediated information transfer. Our goal is to explore a novel distributed sensing with centralized memory system architecture that is capable of addressing limitations of previously developed systems. A modular genetic circuit was developed that connects the input of an environmental signal of interest to activation of a lysogenic lambda bacteriophage which is used to transfer information about the sensing event from the sensor cell population to a reporter cell population. A variety of different ways to encode and store information were explored. While seemingly different, the lines of work described above are connected by a common thread of developing generalizable and modular approaches for engineering synthetic microbial communities to deliver the potential advantages they offer in a variety of medical, industrial, and environmental applications. Synthetic microbial communities are capable of xvii performing complex and varied functions within these contexts and this dissertation is contributing to the rapidly growing body of research work for addressing the challenges that must be overcome to realize that potential.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163080/1/agkrieg_1.pd