Spike-time encoding as a data compression technique for pattern recognition of temporal data

Sengupta, Neelava; Kasabov, Nikola

doi:10.1016/j.ins.2017.04.017

Cited by 35 publications

(17 citation statements)

References 30 publications

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“…In the field of digital signal processing (DSP) [41], finite impulse response filter (FIRF) is one of the most commonly used components, which can perform the function of signal pre-modulation and frequency band selection and filtering. FIRF are widely employed in many fields such as communications [42], image processing [43], pattern recognition [44], wireless sensor network [45] and so on. Many methods of DSP were applied in the fundamental research of cytology, brain neurology, genetics and other fields.…”

Section: Materials and Methodologymentioning

confidence: 99%

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Chen

You

et al. 2019

BMC Genomics

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BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

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Section: Materials and Methodologymentioning

confidence: 99%

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Chen

You

et al. 2019

BMC Genomics

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“…This implies that the SNN needs to fire a large number of spikes in order to achieve comparable performance as its corresponding ANN, which implies high computational cost. In contrast to rate-coding, our work is based on temporal coding [19], which relies on the precise timing of the spikes to encode information instead of average firing rates. Temporal coding has a biological basis, and it helps to implement pattern recognition based on temporally coded spike trains, which is vital to biological sensory systems.…”

Section: Ann-to-snn Conversionmentioning

confidence: 99%

Efficient Spiking Neural Networks With Logarithmic Temporal Coding

Zhang

Zheng

et al. 2020

IEEE Access

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A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an equivalent SNN. To reduce the computational cost of the resulting SNN as measured by the number of spikes, we present Logarithmic Temporal Coding (LTC), where the number of spikes used to encode an activation grows logarithmically with the activation value; and the accompanying Exponentiate-and-Fire (EF) neuron model, which only involves efficient bit-shift and addition operations. Moreover, we improve the training process of ANN to compensate for approximation errors due to LTC. Experimental results indicate that the resulting SNN achieves competitive performance in terms of classification accuracy at significantly lower computational cost than related work. INDEX TERMS Spiking neural networks, temporal coding, neuromorphic computing.

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“…In the hypothesis of SA-ANN-based cognition mechanism model, human cognition can be regarded as a data-driven dynamical pattern recognition issue, essentially subjected to external input information, in which the process of cognition is the dynamical mapping from feature information to the system state patterns [39][40][41]. As a commonality, pattern recognition and human cognition both identify the characteristics of the target object and learn based on standard target states or a template for recognition.…”

Section: Pattern Recognition In Sa-ann Cognition Frameworkmentioning

confidence: 99%

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen

Tan

2018

Complexity

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Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNsbased cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing-artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.

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Spike-time encoding as a data compression technique for pattern recognition of temporal data

Cited by 35 publications

References 30 publications

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Efficient Spiking Neural Networks With Logarithmic Temporal Coding

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

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