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Molecular and DNA Artificial Neural Networks via fractional coding

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020年第3期14卷 490-503页

作者： Liu, Xingyi Parhi, Keshab K. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using fractional coding, a simple molecular perceptron that computes sigmoid of scaled weighted sum of the inputs was presented where the inputs and the weights lie between [-1,1]. Even for computing the perceptron, the prior approach suffers from two major limitations. First, it cannot compute the sigmoid of the weighted sum, but only the sigmoid of the scaled weighted sum. Second, many machine learning applications require the coefficients to be arbitrarily positive and negative numbers that are not bounded between [-1,1];such numbers cannot be handled by the prior perceptron using fractional coding. This paper makes four contributions. First molecular perceptrons that can handle arbitrary weights and can compute sigmoid of the weighted sums are presented. Thus, these molecular perceptrons are ideal for regression applications and multi-layer ANNs. A new molecular divider is introduced and is used to compute sigmoid(ax) where a > 1. Second, based on fractional coding, a molecular artificial neural network (ANN) with one hidden layer is presented. Third, a trained ANN classifier with one hidden layer from seizure prediction application from electroencephalogram is mapped to molecular reactions and DNA and their performances are presented. Fourth, molecular activation functions for rectified linear unit (ReLU) and softmax are also presented.

关键词： DNA Encoding Proteins Protein engineering Neural networks Medical treatment Molecular computing Artificial neural network (ANN) DNA fractional coding molcular ReLU molecular divider molecular neural networks molecular sigmoid molecular softmax stochastic logic

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DNA Computing Units Based on fractional coding 1

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18th International Conference on Unconventional Computation and Natural Computation (UCNC)

作者： Salehi, Sayed Ahmad Moore, Peyton Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9783030193119

ISBN: (纸本)9783030193119;9783030193102

fractional encoding has been recently proposed as a promising convention to represent information in molecular computing systems. This paper presents new 2-input molecular computing units based on unipolar fractional representation. The units calculate simple computational equations that can be used for the computation of more complex functions. The design of these molecular computing units is inspired by fan-in 2 logic gates in the field of stochastic computing. Each computing unit consists of four chemical reactions with two reactants and one product. We design the DNA reactions implementing the chemical reactions of each unit based on the toehold-mediated DNA strand-displacement mechanism. Every unit is designed by four input strands and eight fuel gate strands of DNA. Since DNA molecules related to the input and output of the units have the same form of domain-toehold-domain-toehold, output molecules of each unit can be used as input for other units and this provides the cascading of the units for designing complex circuits. The whole DNA pathway for each unit is composed of twenty DNA reactions. The simulation results by Visual DSD show that the DNA implementations follow the theoretically expected computations of each unit with the maximum of 9.33% error.

关键词： DNA computing fractional coding DNA strand-displacement

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Computing Radial Basis Function Support Vector Machine using DNA via fractional coding 19

Computing Radial Basis Function Support Vector Machine using...

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56th ACM/EDAC/IEEE Design Automation Conference (DAC)

作者： Liu, Xingyi Parhi, Keshab K. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781450367257

This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented;one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less regression error than that based on implicit conversion.

关键词： Molecular Computing fractional coding Stochastic Logic Support Vector Machine Radial Basis Function DNA computing

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Training DNA Perceptrons via fractional coding 53

Training DNA Perceptrons via Fractional Coding

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53rd Asilomar Conference on Signals, Systems, and Computers (ACSSC)

作者： Liu, Xingyi Parhi, Keshab K. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781728143002

This paper describes a novel approach to synthesize molecular reactions to train a perceptron, i.e., a single-layered neural network, with sigmoidal activation function. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. In prior work, a DNA perceptron with bipolar inputs and unipolar output was proposed for inference. The focus of this paper is on synthesis of molecular reactions for training of the DNA perceptron. A new molecular scaler that performs multiplication by a factor greater than 1 is proposed based on fractional coding. The training of the perceptron proposed in this paper is based on a modified backpropagation equation as the exact equation cannot be easily mapped to molecular reactions using fractional coding.

关键词： Molecular Computing fractional coding Perceptron Backpropagation Molecular Scaler DNA Computing

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Joint Transmit and Receive Antenna Selection in Mimo Systems Based on Swarm Intelligence Algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期126卷 605-620页

作者： Zhang, Yiwen Su, Sunqing Liao, Wenliang Lei, Guowei Yang, Guangsong Jimei Univ Sch Sci Xiamen 361021 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S Yorkshire England Jimei Univ Sch Informat Engn Xiamen 361021 Peoples R China

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding.

关键词： JTRAS fractional coding Cat swarm optimization Genetic algorithm Particle swarm optimization

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DNA Memristors and Their Application to Reservoir Computing

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ACS SYNTHETIC BIOLOGY 2022年第6期11卷 2202-2213页

作者： Liu, Xingyi Parhi, Keshab K. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

This paper introduces memristors realized by molecular and DNA reactions. Molecular memristors process one input molecule, generate two output molecules, and are realized using two molecular reactions with two different rate constants. The DNA memristors are realized using five DNA strand displacement (DSD) reactions with two effective rate constants. The hysteresis behavior is preserved in the proposed memristors, and this behavior can be altered by changing the ratios of the rate constants. The state of the memristor can be computed from the concentrations of the two output molecules using bipolar fractional coding. We describe how the proposed memristors can be used to learn the spatial and temporal properties of data via the reservoir computing (RC) model. An RC system can be divided into two parts: reservoir and readout layer. The first part transfers the information from the input space to a high-dimensional spatiotemporal feature space represented by the state of reservoirs. The connectivity structure of the reservoir will remain fixed through the dynamical evaluations. The readout layer effectively maps the projected features to the target output. A dynamical memristor array is used to implement an RC system that exploits the internal dynamical processes of the memristors. The readout layer implements a matrix-vector multiplication using molecular reactions, also based on bipolar fractional coding. All molecular reactions are mapped to DSD reactions. The RC system based on the DNA reservoir and the DNA readout layer is used to solve a handwritten digit recognition task and a second-order time series prediction task. The performance of the DNA RC system is comparable to that of an electronic memristor RC system for both tasks.

关键词： DNA memristors DNA reservoir computing fractional coding DNA readout layer digit classification time series prediction

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Analysis of Molecular MUX PUFs with Stochastic Challenges 66

Analysis of Molecular MUX PUFs with Stochastic Challenges

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Anderson, Carl Liu, Xingyi Parhi, Keshab K.

ISBN: (纸本)9798350302103

Physical unclonable functions (PUFs) are small circuits primarily used as hardware security primitives for authentication. PUFs work by generating unique signatures due to the inherent random variations in the PUF manufacturing process. This paper addresses molecular PUFs that can be used for authentication in biosecurity applications where these PUFs can be naturally implemented using bio-substrate (as opposed to electronic). This paper analyzes a novel stochastic molecular multiplexer (MUX) PUF where the input challenge corresponds to a stochastic number encoded using a fractional coding representation;thus, the challenge is not binary. Each multiplexer in the PUF is realized using six reactions. The source of randomness in the PUF is the intrinsic variations in the rate constants of each reaction. Intra-PUF and inter-PUF variations are simulated in linear MUX PUFs consisting of 8, 16, 32, 64, and 96 stages. These variations are used to compute the reliability and uniqueness of different PUF configurations. It is shown that, for the stochastic molecular PUFs to be feasible, the distributions of the rate constants require large variances. © 2023 IEEE.

关键词： biosecurity fractional coding molecular PUF Physical unclonable functions (PUF) stochastic molecular multiplexers stochastic MUX PUF

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