Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

• MeSH
• Algorithms MeSH
• Genomics methods   MeSH
• Humans MeSH
• RNA, Small Nucleolar genetics   MeSH
• MicroRNAs genetics   MeSH
• Mice MeSH
• RNA, Untranslated genetics   MeSH
• Neural Networks, Computer MeSH
• Software MeSH
• Computational Biology methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• RNA, Small Nucleolar MeSH
• MicroRNAs MeSH
• RNA, Untranslated MeSH

Several studies have demonstrated the important role of non-coding RNAs as regulators of posttranscriptional processes, including stem cells self-renewal and neural differentiation. Human embryonic stem cells (hESCs) and induced pluripotent stem cells (ihPSCs) show enormous potential in regenerative medicine due to their capacity to differentiate to virtually any type of cells of human body. Deciphering the role of non-coding RNAs in pluripotency, self-renewal and neural differentiation will reveal new molecular mechanisms involved in induction and maintenances of pluripotent state as well as triggering these cells toward clinically relevant cells for transplantation. In this brief review we will summarize recently published studies which reveal the role of non-coding RNAs in pluripotency and neural differentiation of hESCs and ihPSC.

• Keywords
• differentiation, human embryonic stem cells, non-coding RNA, pluripotency, pluripotent stem cells,
• Publication type
• Journal Article MeSH
• Review MeSH

Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.

• Keywords
• Neural architecture search, Spiking neural networks,
• MeSH
• Algorithms * MeSH
• Neural Networks, Computer * MeSH
• Publication type
• Journal Article MeSH

• Publication type
• Journal Article MeSH

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

• Keywords
• Dynamically evolving spiking neural networks, Electronic nose, McNemar’s test, Spike latency coding, Spiking neural network, Tea,
• MeSH
• Algorithms MeSH
• Biomimetics MeSH
• Tea * MeSH
• Olfactory Perception MeSH
• Equipment Design MeSH
• Electronic Nose * MeSH
• Neural Networks, Computer * MeSH
• Normal Distribution MeSH
• Nose MeSH
• Odorants * MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Tea * MeSH

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.

• Keywords
• Fisher information, Metabolic cost, Rate coding, Temporal coding,
• MeSH
• Time Factors MeSH
• Energy Metabolism * MeSH
• Humans MeSH
• Membrane Potentials MeSH
• Models, Neurological * MeSH
• Nerve Net cytology   physiology   MeSH
• Neural Networks, Computer * MeSH
• Neurons physiology   MeSH
• Computer Simulation MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN conversion method, which is the most effective SNN training method, has successfully converted moderately deep ANNs to SNNs with satisfactory performance. However, this method requires a large number of time-steps, which hurts the energy efficiency of SNNs. How to effectively covert a very deep ANN (e.g., more than 100 layers) to an SNN with a small number of time-steps remains a difficult task. To tackle this challenge, this paper makes the first attempt to propose a novel error analysis framework that takes both the "quantization error" and the "deviation error" into account, which comes from the discretization of SNN dynamicsthe neuron's coding scheme and the inconstant input currents at intermediate layers, respectively. Particularly, our theories reveal that the "deviation error" depends on both the spike threshold and the input variance. Based on our theoretical analysis, we further propose the Threshold Tuning and Residual Block Restructuring (TTRBR) method that can convert very deep ANNs (>100 layers) to SNNs with negligible accuracy degradation while requiring only a small number of time-steps. With very deep networks, our TTRBR method achieves state-of-the-art (SOTA) performance on the CIFAR-10, CIFAR-100, and ImageNet classification tasks.

• Keywords
• ANN-to-SNN conversion, Conversion error analysis, Spiking neural networks,
• MeSH
• Neural Networks, Computer * MeSH
• Computers * MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: The hippocampal representation of space, formed by the collective activity of populations of place cells, is considered as a substrate of spatial memory. Alzheimer's disease (AD), a widespread severe neurodegenerative condition of multifactorial origin, typically exhibits spatial memory deficits among its early clinical signs before more severe cognitive impacts develop. OBJECTIVE: To investigate mechanisms of spatial memory impairment in a double transgenic rat model of AD. METHODS: In this study, we utilized 9-12-month-old double-transgenic TgF344-AD rats and age-matched controls to analyze the spatial coding properties of CA1 place cells. We characterized the spatial memory representation, assessed cells' spatial information content and direction-specific activity, and compared their population coding in familiar and novel conditions. RESULTS: Our findings revealed that TgF344-AD animals exhibited lower precision in coding, as evidenced by reduced spatial information and larger receptive zones. This impairment was evident in maps representing novel environments. While controls instantly encoded directional context during their initial exposure to a novel environment, transgenics struggled to incorporate this information into the newly developed hippocampal spatial representation. This resulted in impairment in orthogonalization of stored activity patterns, an important feature directly related to episodic memory encoding capacity. CONCLUSIONS: Overall, the results shed light on the nature of impairment at both the single-cell and population levels in the transgenic AD model. In addition to the observed spatial coding inaccuracy, the findings reveal a significantly impaired ability to adaptively modify and refine newly stored hippocampal memory patterns.

• Keywords
• Alzheimer’s disease, TgF344-AD rats, place cell directionality, place field size, spatial memory,
• MeSH
• Alzheimer Disease * physiopathology   MeSH
• Amyloid beta-Protein Precursor genetics   MeSH
• CA1 Region, Hippocampal physiopathology   MeSH
• Hippocampus physiopathology   MeSH
• Rats MeSH
• Humans MeSH
• Disease Models, Animal * MeSH
• Memory Disorders etiology   physiopathology   MeSH
• Rats, Inbred F344 MeSH
• Rats, Transgenic * MeSH
• Spatial Memory physiology   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Humans MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Amyloid beta-Protein Precursor MeSH

Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.

• Keywords
• Cell shape modeling, Generative model, Implicit neural representation, Neural network,
• MeSH
• Caenorhabditis elegans * MeSH
• Humans MeSH
• Mitosis MeSH
• Lung Neoplasms * MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

We investigate an artificial neural network model with a modified Hebb rule. It is an auto-associative neural network similar to the Hopfield model and to the Willshaw model. It has properties of both of these models. Another property is that the patterns are sparsely coded and are stored in cycles of synchronous neural activities. The cycles of activity for some ranges of parameter increase the capacity of the model. We discuss basic properties of the model and some of the implementation issues, namely optimizing of the algorithms. We describe the modification of the Hebb learning rule, the learning algorithm, the generation of patterns, decomposition of patterns into cycles and pattern recall.

• MeSH
• Models, Neurological * MeSH
• Nerve Net * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH