wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding. Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Revised:  In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding. 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning. We evaluated our contributions on the hand-written digit/letter classification tasks. The experimental results show that BioSNet not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Edit intent: meaning-changed
Original:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Revised:  Simulation results show that our Inception-like architecture exhibits superior performance than widely used Fully-Connected (FC) and Locally-Connected (LC) architectures. Our SNN not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Edit intent: meaning-changed
Original:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Revised:  Simulation results show that BioSNet not only achieves comparable results with the state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Edit intent: meaning-changed
Original:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Revised:  Simulation results show that BioSNet not only achieves state-of-the-art unsupervised SNNs , but also exhibits superior learning efficiency and high robustness.
Edit intent: meaning-changed
Original:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness.
Revised:  Simulation results show that BioSNet not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also shows superior learning efficiency and high robustness.
Edit intent: clarity
Original:  Simulation results show that BioSNet not only achieves  state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and high robustness. Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Revised:  Simulation results show that BioSNet not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets , but also exhibits superior learning efficiency and robustness. Our SNN trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Edit intent: clarity
Original:  Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Revised:  Specifically, the BioSNet trained with only hundreds of iterations can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Edit intent: clarity
Original:  Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Revised:  Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification result, and randomly destructing network only leads to slight performance degradation.
Edit intent: clarity
Original:  Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to slight performance degradation.
Revised:  Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80\% and randomly deleting even 95\% of synapses or neurons in the BioSNet only leads to negligible performance degradation.
Edit intent: clarity
Original: Spiking Neural Network (SNN) , as a brain-inspired  machine learning algorithm , is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware.
Revised: Spiking Neural Network (SNN) is a brain-inspired machine learning algorithm , is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware.
Edit intent: coherence
Original: Spiking Neural Network (SNN) , as a brain-inspired  machine learning algorithm , is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware.
Revised: Spiking Neural Network (SNN) , as a brain-inspired , event-driven machine learning algorithm , is attracting attention due to its data-driven, event-based computing style and its potential to produce ultra-high-energy-efficient hardware.
Edit intent: fluency
Original:   Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP).
Revised:  Among existing SNNs, unsupervised SNNs are based on synaptic plasticity and considered to have more potential in imitating the learning process of biological brain. Most unsupervised SNNs are trained through competitive learning with Spike-Timing-Dependent Plasticity (STDP).
Edit intent: meaning-changed
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to overcome these limitations, we : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: clarity
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) designed a high-parallelism network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: clarity
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature ; 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: fluency
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) extended a widely used vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: clarity
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding ; 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: fluency
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to further accelerate the network's learning.
Edit intent: fluency
Original:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to  further accelerate the network's learning.
Revised:  In this paper, to ease these limitations : 1) We designed a highly parallel multi-pathway network architecture, inspired by the Inception module in the Artificial Neural Network (ANN) literature . 2) We extended the classic vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding . 3) We proposed to use adaptive repolarization (i.e. resetting) in the spiking neuron model to enhance the spiking activities and thus further accelerate the network's learning.
Edit intent: clarity
Original:  We evaluated our contributions on the hand-written digit / letter classification tasks. The experimental results show that our Inception-like architecture exhibits superior performance than widely used Fully-Connected (FC) and Locally-Connected (LC) architectures.
Revised:  We evaluated our contributions on the hand-written digit / EMNIST). Our experimental results show that our Inception-like architecture exhibits superior performance than widely used Fully-Connected (FC) and Locally-Connected (LC) architectures.
Edit intent: meaning-changed
Original:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs  , but also shows superior learning efficiency and robustness  .
Revised:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) , but also shows superior learning efficiency and robustness .
Edit intent: meaning-changed
Original:  Among existing SNNs, unsupervised SNNs are based on synaptic plasticity and considered to have more potential in imitating the learning process of  biological brain.
Revised:  Among existing SNNs, unsupervised SNNs are based on synaptic plasticity , especially Spike-Timing-Dependent Plasticity (STDP), are considered to have more potential in imitating the learning process of biological brain.
Edit intent: meaning-changed
Original:  However, the STDP-based SNNs are limited by slow learning speed and/or constrained learning capability.
Revised:  However, the STDP-based SNNs are limited by slow learning speed and/or slow learning speed. Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.
Edit intent: meaning-changed
Original:  2) extended a widely used vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer to reduce the information loss in the spike decoding ;
Revised:  2) extended a widely used vote-based spike decoding scheme to a Vote-for-All (VFA) decoding layer as a replacement to the standard vote-based spike decoding scheme, to reduce the information loss in the spike decoding ;
Edit intent: meaning-changed
Original:  3) proposed to use adaptive repolarization ( i.e. resetting) in the spiking neuron model to enhance the spiking activities and thus further accelerate the network' s learning . We evaluated our contributions on the two established benchmark datasets (MNIST/EMNIST) .
Revised:  3) proposed to use adaptive repolarization ( resetting) mechanism that accelerates SNNs' learning by enhancing spiking activities. Our experimental results on two established benchmark datasets (MNIST/EMNIST) .
Edit intent: clarity
Original:  We evaluated our contributions on the two established benchmark datasets (MNIST/EMNIST) . Our experimental results show that our architecture exhibits superior performance than widely used Fully-Connected (FC ) and Locally-Connected (LC) architectures.
Revised:  We evaluated our contributions on the two established benchmark datasets (MNIST/EMNIST) show that our architecture exhibits superior performance than widely used Fully-Connected (FC ) and Locally-Connected (LC) architectures.
Edit intent: coherence
Original:  Our experimental results show that our architecture exhibits superior performance than widely used Fully-Connected (FC ) and Locally-Connected (LC) architectures. Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) , but also shows superior learning efficiency and robustness against hardware damage.
Revised:  Our experimental results show that our architecture exhibits superior performance than widely used Fully-Connected (FC ) and Locally-Connected (LC) architecture, and that our SNN achieved competitive results with state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) , but also shows superior learning efficiency and robustness against hardware damage.
Edit intent: clarity
Original:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) , but also shows superior learning efficiency and robustness against hardware damage.
Revised:  Our SNN not only achieves comparable results with the state-of-the-art unsupervised SNNs (95.64\%/80.11\% accuracy on the MNIST/EMNISE dataset) while having superior learning efficiency and robustness against hardware damage.
Edit intent: clarity
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we model the travelling connectivity as a scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under voluntary vaccination scheme.
Edit intent: clarity
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we focus on modelling the travelling network as scale-free network , and investigate dependencies between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under voluntary vaccination scheme.
Edit intent: clarity
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics . In doing so we extend an SIR-network model in game-theoretic framework with imitation dynamics under voluntary vaccination scheme.
Edit intent: clarity
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model with game-theoretic framework with imitation dynamics under voluntary vaccination scheme.
Edit intent: fluency
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic components, capturing the imitation dynamics under voluntary vaccination scheme.
Edit intent: clarity
Original:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under  voluntary vaccination scheme.
Revised:  In this paper, we focus on modelling the travelling network as scale-free network to investigate the correlation between the network's assortativity and the resultant epidemic and vaccination dynamics , by adopting a SIR-network model in game-theoretic framework with imitation dynamics under a voluntary vaccination scheme.
Edit intent: fluency
Original:  Our results show a non-linear correlation between epidemic dynamics and  network's assortativity, highlighting that highly disassortative networks are particularly impactful at containing epidemics . To explain the finding, we propose a novel network-theoretic measure, \psi, defined as the ratio between Shannon information and maximum coreness. Higher \psi is found in highly disassortative networks, which provides an explanation on why disassortative networks may be better at suppressing epidemics.
Revised:  Our results show a non-linear correlation between epidemic dynamics and network's assortativity, highlighting that networks with high assortativity tend to suppress epidemics under certain conditions. In highly assortative networks, the suppression is sustained producing an early convergence to equilibrium. In highly disassortative networks, which provides an explanation on why disassortative networks may be better at suppressing epidemics.
Edit intent: meaning-changed