A Little Energy Goes a Long Way: Energy-Efficient, Accurate Conversion from Convolutional Neural Networks to Spiking Neural Networks

Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training high accuracy models. A major thread of research on SNNs is on converting a pre-trained convolutional neural network (CNN) to an SNN of the same structure. State-of-the-art conversion methods are approaching the accuracy limit, i.e., the near-zero accuracy loss of SNN against the original CNN. However, we note that this is made possible only when significantly more energy is consumed to process an input. In this paper, we argue that this trend of "energy for accuracy" is not necessary -- a little energy can go a long way to achieve the near-zero accuracy loss. Specifically, we propose a novel CNN-to-SNN conversion method that is able to use a reasonably short spike train (e.g., 256 timesteps for CIFAR10 images) to achieve the near-zero accuracy loss. The new conversion method, named as explicit current control (ECC), contains three techniques (current normalisation, thresholding for residual elimination, and consistency maintenance for batch-normalisation), in order to explicitly control the currents flowing through the SNN when processing inputs. We implement ECC into a tool nicknamed SpKeras, which can conveniently import Keras CNN models and convert them into SNNs. We conduct an extensive set of experiments with the tool -- working with VGG16 and various datasets such as CIFAR10 and CIFAR100 -- and compare with state-of-the-art conversion methods. Results show that ECC is a promising method that can optimise over energy consumption and accuracy loss simultaneously

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

Spiking neural networks (SNNs), a variant of artificial neural networks (ANNs) with the benefit of energy efficiency, have achieved the accuracy close to its ANN counterparts, on benchmark datasets such as CIFAR10/100 and ImageNet. However, comparing with frame-based input (e.g., images), event-based inputs from e.g., Dynamic Vision Sensor (DVS) can make a better use of SNNs thanks to the SNNs' asynchronous working mechanism. In this paper, we strengthen the marriage between SNNs and event-based inputs with a proposal to consider anytime optimal inference SNNs, or AOI-SNNs, which can terminate anytime during the inference to achieve optimal inference result. Two novel optimisation techniques are presented to achieve AOI-SNNs: a regularisation and a cutoff. The regularisation enables the training and construction of SNNs with optimised performance, and the cutoff technique optimises the inference of SNNs on event-driven inputs. We conduct an extensive set of experiments on multiple benchmark event-based datasets, including CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results demonstrate that our techniques are superior to the state-of-the-art with respect to the accuracy and latency

PSN-PC: A Novel Antimicrobial and Anti-Biofilm Peptide from the Skin Secretion of Phyllomedusa-camba with Cytotoxicity on Human Lung Cancer Cell

Peptides derived from amphibian skin secretion are promising drug prototypes for combating widespread infection. In this study, a novel peptide belonging to the phylloseptin family of antimicrobial peptides was isolated from the skin secretion of the Phyllomedusa camba, namely phylloseptin-PC (PSN-PC). The biosynthetic precursor was obtained by molecular cloning and the mature peptide sequence was confirmed through tandem mass spectrometry (MS/MS) fragmentation sequencing in the skin secretion. The synthetic replicate exhibited a broad spectrum antimicrobial activity against Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Candida albicans at concentrations of 2, 2, 8, 32 and 2 µM, respectively. It also showed the capability of eliminating S. aureus biofilm with a minimal biofilm eradication concentration of 8 µM. The haemolysis of this peptide was not significant at low concentrations but had a considerable increase at high concentrations. Additionally, this peptide showed an anti-proliferation effect on the non-small cell lung cancer cell line (NCI-H157), with low cytotoxicity on the human microvascular endothelial cell line (HMEC-1). The discovery of the novel peptide may provide useful clues for new drug discoveries

A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.

This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network (SNN), which takes advantage of the sophisticated training regime of Convolutional Neural Network (CNN) and converts a well-trained CNN to an SNN. We observe that the existing CNN-to-SNN conversion algorithms may keep a certain amount of residual current in the spiking neurons in SNN, and the residual current may cause significant accuracy loss when inference time is short. To deal with this, we propose a unified framework to equalize the output of the convolutional or dense layer in CNN and the accumulated current in SNN, and maximally align the spiking rate of a neuron with its corresponding charge. This framework enables us to design a novel explicit current control (ECC) method for the CNN-to-SNN conversion which considers multiple objectives at the same time during the conversion, including accuracy, latency, and energy efficiency. We conduct an extensive set of experiments on different neural network architectures, e.g., VGG, ResNet, and DenseNet, to evaluate the resulting SNNs. The benchmark datasets include not only the image datasets such as CIFAR-10/100 and ImageNet but also the Dynamic Vision Sensor (DVS) image datasets such as DVS-CIFAR-10. The experimental results show the superior performance of our ECC method over the state-of-the-art

A Combined Molecular Cloning and Mass Spectrometric Method to Identify, Characterize, and Design Frenatin Peptides from the Skin Secretion of Litoria infrafrenata

Amphibian skin secretions are unique sources of bioactive molecules, particularly bioactive peptides. In this study, the skin secretion of the white-lipped tree frog (Litoria infrafrenata) was obtained to identify peptides with putative therapeutic potential. By utilizing skin secretion-derived mRNA, a cDNA library was constructed, a frenatin gene was cloned and its encoded peptides were deduced and confirmed using RP-HPLC, MALDI-TOF and MS/MS. The deduced peptides were identified as frenatin 4.1 (GFLEKLKTGAKDFASAFVNSIKGT) and a post-translationally modified peptide, frenatin 4.2 (GFLEKLKTGAKDFASAFVNSIK.NH2). Antimicrobial activity of the peptides was assessed by determining their minimal inhibitory concentrations (MICs) using standard model microorganisms. Through studying structure–activity relationships, analogues of the two peptides were designed, resulting in synthesis of frenatin 4.1a (GFLEKLKKGAKDFASALVNSIKGT) and frenatin 4.2a (GFLLKLKLGAKLFASAFVNSIK.NH2). Both analogues exhibited improved antimicrobial activities, especially frenatin 4.2a, which displayed significant enhancement of broad spectrum antimicrobial efficiency. The peptide modifications applied in this study, may provide new ideas for the generation of leads for the design of antimicrobial peptides with therapeutic applications

Synthesis of 1, 2, 4-triazole, 1, 3, 4-thiadiazole and 1, 3, 4-oxadiazole derivatives of 6-nitrobenzimidazole

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Discovery of Novel Bacterial Cell-Penetrating Phylloseptins in Defensive Skin Secretions of the South American Hylid Frogs, Phyllomedusa duellmani and Phyllomedusa coelestis

Phylloseptin (PS) peptides, derived from South American hylid frogs (subfamily Phyllomedusinae), have been found to have broad-spectrum antimicrobial activities and relatively low haemolytic activities. Although PS peptides have been identified from several well-known and widely-distributed species of the Phyllomedusinae, there remains merit in their study in additional, more obscure and specialised members of this taxon. Here, we report the discovery of two novel PS peptides, named PS-Du and PS-Co, which were respectively identified for the first time and isolated from the skin secretions of Phyllomedusa duellmani and Phyllomedusa coelestis. Their encoding cDNAs were cloned, from which it was possible to deduce the entire primary structures of their biosynthetic precursors. Reversed-phase high-performance liquid chromatography (RP-HPLC) and tandem mass spectrometry (MS/MS) analyses were employed to isolate and structurally-characterise respective encoded PS peptides from skin secretions. The peptides had molecular masses of 2049.7 Da (PS-Du) and 1972.8 Da (PS-Co). They shared typical N-terminal sequences and C-terminal amidation with other known phylloseptins. The two peptides exhibited growth inhibitory activity against E. coli (NCTC 10418), as a standard Gram-negative bacterium, S. aureus (NCTC 10788), as a standard Gram-positive bacterium and C. albicans (NCPF 1467), as a standard pathogenic yeast, all as planktonic cultures. Moreover, both peptides demonstrated the capability of eliminating S. aureus biofilm

Apolipoprotein-induced conversion of phosphatidylcholine bilayer vesicles into nanodisks

AbstractApolipoprotein mediated formation of nanodisks was studied in detail using apolipophorin III (apoLp-III), thereby providing insight in apolipoprotein–lipid binding interactions. The spontaneous solubilization of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) vesicles occured only in a very narrow temperature range at the gel–liquid–crystalline phase transition temperature, exhibiting a net exothermic interaction based on isothermal titration calorimetry analysis. The resulting nanodisks were protected from proteolysis by trypsin, endoproteinase Glu-C, chymotrypsin and elastase. DMPC solubilization and the simultaneous formation of nanodisks were promoted by increasing the vesicle diameter, protein to lipid ratio and concentration. Inclusion of cholesterol in DMPC dramatically enhanced the rate of nanodisk formation, presumably by stabilization of lattice defects which form the main insertion sites for apolipoprotein α-helices. The presence of fully saturated acyl chains with a length of 13 or 14 carbons in phosphatidylcholine allowed the spontaneous vesicle solubilization upon apolipoprotein addition. Nanodisks with C13:0-phosphatidylcholine were significantly smaller with a diameter of 11.7±3.1nm compared to 18.5±5.6nm for DMPC nanodisks determined by transmission electron microscopy. Nanodisk formation was not observed when the phosphatidylcholine vesicles contained acyl chains of 15 or 16 carbons. However, using very high concentrations of lipid and protein (>10mg/ml), 1,2,-dipalmitoyl-sn-glycero-3-phosphocholine nanodisks could be produced spontaneously although the efficiency remained low