scholarly journals LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2123 ◽  
Author(s):  
Lingfei Mo ◽  
Minghao Wang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Structure ◽  
Large Scale ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Logical Operation ◽  
Large Scale Network ◽  
Learning Capabilities ◽  
Scale Network

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

scholarly journals Small- and large-scale network structure of live fish movements in Scotland

2009 ◽  
Vol 91 (2-4) ◽  
pp. 261-269 ◽  
Author(s):  
Darren Michael Green ◽  
Alison Gregory ◽  
Lorna Ann Munro
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Fish Movements ◽  
Large Scale Network ◽  
Live Fish ◽  
Scale Network

scholarly journals Inference and Influence of Large-Scale Social Networks Using Snapshot Population Behaviour without Network Data

2020 ◽  
Author(s):  
Antonia Godoy-Lorite ◽  
Nick S. Jones
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Large Scale ◽  
Social Preferences ◽  
Mayoral Elections ◽  
Large Scale Network ◽  
Scale Network ◽  
Eu Referendum ◽  
Behaviour Type ◽  
The Eu

Population behaviours, such as voting and vaccination, depend on social networks. Social networks can differ depending on behaviour type and are typically hidden. However, we do often have large-scale behavioural data, albeit only snapshots taken at one timepoint. We present a method that jointly infers large-scale network structure and a networked model of human behaviour using only snapshot population behavioural data. This exploits the simplicity of a few-parameter, geometric socio-demographic network model and a spin-based model of behaviour. We illustrate, for the EU Referendum and two London Mayoral elections, how the model offers both prediction and the interpretation of our homophilic inclinations. Beyond offering the extraction of behaviour-specific network-structure from large-scale behavioural datasets, our approach yields a crude calculus linking inequalities and social preferences to behavioural outcomes. We give examples of potential network-sensitive policies: how changes to income inequality, a social temperature and homophilic preferences might have reduced polarisation in a recent election.

scholarly journals Uncovering the transcriptional signatures of hub connectivity in neural networks

2018 ◽  
Author(s):  
Aurina Arnatkeviciute ◽  
Ben Fulcher ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Large Scale ◽  
Expression Patterns ◽  
Genetic Influences ◽  
Gene Expression Patterns ◽  
Small Subset ◽  
Present Evidence ◽  
Large Scale Network ◽  
Scale Network

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different of species and scales ranging from C.elegans to mouse, rat, cat, macaque and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

scholarly journals Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Seunghyun Park ◽  
Jin-Young Choi
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Malicious Code ◽  
Machine Learning Algorithms ◽  
Security Technology ◽  
Detection Systems ◽  
External Threats ◽  
Large Scale Network ◽  
Scale Network

The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.

scholarly journals Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

2019 ◽  
Vol 9 (4) ◽  
pp. 283-291 ◽  
Author(s):  
Sou Nobukawa ◽  
Haruhiko Nishimura ◽  
Teruya Yamanishi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Pattern Classification ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Problems ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Self Organized

Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.

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