Fabrication and Application of a Polymer Neuromorphic Circuitry Based on Polymer Memristive Devices and Polymer Transistors

Neuromorphic engineering is a discipline that aims to address the shortcomings of today\u27s serial computers, namely large power consumption, susceptibility to physical damage, as well as the need for explicit programming, by applying biologically-inspired principles to develop neural systems with applications such as machine learning and perception, autonomous robotics and generic artificial intelligence. This doctoral dissertation presents work performed fabricating a previously developed type of polymer neuromorphic architecture, termed Polymer Neuromorphic Circuitry (PNC), inspired by the McCulloch-Pitts model of an artificial neuron. The major contribution of this dissertation is a development of processing techniques necessary to realize the Polymer Neuromorphic Circuitry, which required a development of individual polymer electronics elements, as well as customization of fabrication processes necessary for the realization of the circuitry on separate substrates as well as on a single substrate. This is the first demonstration of a fabrication of an entire neuron, and more importantly, a network of such neurons, that includes both the weighting functionality of a synapse and the somatic summing, all realized with polymer electronics technology. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the conventional, inorganic electronics in the form of physical flexibility, low cost and ease of fabrication, manufacturing compatibility with many substrate materials, as well as greater biological compatibility. These advantages were the primary motivation for the choice to fabricate all of the electrical components required to realize the PNC, namely polymer transistors, polymer memristive devices, and polymer resistors, with polymer electronics components. The efficacy of this design is validated by demonstrating that the activation function of a single neuron approximates the sigmoidal function commonly employed by artificial neural networks. The utility of the neuromorphic circuitry is further corroborated by illustrating that a network of such neurons, and even a single neuron, are capable of performing linear classification for a real-life problem

Simulation, Application, and Resilience of an Organic Neuromorphic Architecture, Made with Organic Bistable Devices and Organic Field Effect Transistors

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at University of Denver. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the inorganic electronics in the form of physical flexibility and low cost of fabrication. However, their device variability between individual devices is also much greater. Therefore the second major contribution of this thesis is the analysis of resilience of neural networks subjected to physical damage and other manufacturing faults

Organic Log-Domain Integrator Synapse

Synapses play a critical role in memory, learning, and cognition. Their main functions include converting presynaptic voltage spikes to postsynaptic currents, as well as scaling the input signal. Several brain-inspired architectures have been proposed to emulate the behavior of biological synapses. While these are useful to explore the properties of nervous systems, the challenge of making biocompatible and flexible circuits with biologically plausible time constants and tunable gain remains. Here, a physically flexible organic log-domain integrator synaptic circuit is shown to address this challenge. In particular, the circuit is fabricated using organic-based materials that are electrically active, offer flexibility and biocompatibility, as well as time constants (critical in learning neural codes and encoding spatiotemporal patterns) that are biologically plausible. Using a 10 nF synaptic capacitor, the time constant reached 126 and 221 ms before and during bending, respectively. The flexible synaptic circuit is characterized before and during bending, followed with studies on the effects of weighting voltage, synaptic capacitance, and disparity in presynaptic signals on the time constant

An organic synaptic circuit: toward flexible and biocompatible organic neuromorphic processing

In the nervous system synapses play a critical role in computation. In neuromorphic systems, biologically inspired hardware implementations of spiking neural networks, electronic synaptic circuits pass signals between silicon neurons by integrating pre-synaptic voltage pulses and converting them into post-synaptic currents, which are scaled by the synaptic weight parameter. The overwhelming majority of neuromorphic systems are implemented using inorganic, mainly silicon, technology. As such, they are physically rigid, require expensive fabrication equipment and high fabrication temperatures, are limited to small-area fabrication, and are difficult to interface with biological tissue. Organic electronics are based on electronic properties of carbon-based molecules and polymers and offer benefits including physical flexibility, low cost, low temperature, and large-area fabrication, as well as biocompatibility, all unavailable to inorganic electronics. Here, we demonstrate an organic differential-pair integrator synaptic circuit, a biologically realistic synapse model, implemented using physically flexible complementary organic electronics. The synapse is shown to convert input voltage spikes into output current traces with biologically realistic time scales. We characterize circuit’s responses based on various synaptic parameters, including gain and weighting voltages, time-constant, synaptic capacitance, and circuit response due to inputs of different frequencies. Time constants comparable to those of biological synapses and the neurons are critical in processing real-world sensory signals such as speech, or bio-signals measured from the body. For processing even slower signals, e.g., on behavioral time scales, we demonstrate time constants in excess of two seconds, while biologically plausible time constants are achieved by deploying smaller synaptic capacitors. We measure the circuit synaptic response to input voltage spikes and present the circuit response properties using custom-made circuit simulations, which are in good agreement with the measured behavior

Vaccine-Induced Immunity in Baboons by Using DNA and Replication-Incompetent Adenovirus Type 5 Vectors Expressing a Human Immunodeficiency Virus Type 1 gag Gene

This is the published version. Copyright 2003 American Society for Microbiology.The cellular immunogenicity of formulated plasmid DNA and replication-defective human adenovirus serotype 5 (Ad5) vaccine vectors expressing a codon-optimized human immunodeficiency virus type 1 gag gene was examined in baboons. The Ad5 vaccine was capable of inducing consistently strong, long-lived CD8+-biased T-cell responses and in vitro cytotoxic activities. The DNA vaccine-elicited immune responses were weaker than those elicited by the Ad5 vaccine and highly variable; formulation with chemical adjuvants led to moderate increases in the levels of Gag-specific T cells. Increasing the DNA-primed responses with booster doses of either Ad5 or modified vaccinia virus Ankara vaccines suggests a difference in the relative levels of cytotoxic and helper responses. The implications of these results are discussed

Evolutionary Modeling and Prediction of Non-Coding RNAs in Drosophila

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3′ end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA

RNAcentral : a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality. RNAcentral is freely available at https://rnacentral.org