scholarly journals Pulsed neural networks consisting of single-flux-quantum spiking neurons

2007 ◽  
Vol 463-465 ◽  
pp. 1072-1075 ◽  
Author(s):  
T. Hirose ◽  
T. Asai ◽  
Y. Amemiya
Keyword(s):  
Neural Networks ◽  
Spiking Neurons ◽  
Flux Quantum ◽  
Pulsed Neural Networks

scholarly journals A Low-Order Model of Biological Neural Networks

2011 ◽  
Vol 23 (10) ◽  
pp. 2626-2682
Author(s):  
James Ting-Ho Lo
Keyword(s):  
Neural Networks ◽  
Logic Gate ◽  
Dendritic Tree ◽  
Spiking Neurons ◽  
Learning Mechanisms ◽  
Order Model ◽  
Biological Neural Networks ◽  
Feedback Connections ◽  
Component Models ◽  
Low Order

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.

SPIKING NEURAL NETWORKS

2009 ◽  
Vol 19 (04) ◽  
pp. 295-308 ◽  
Author(s):  
SAMANWOY GHOSH-DASTIDAR ◽  
HOJJAT ADELI
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Information Transfer ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Classification Problems ◽  
Temporal Dimension ◽  
Information Encoding ◽  
Dynamic Representation ◽  
Insight Into

Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models. In the past decade, Spiking Neural Networks (SNNs) have been developed which comprise of spiking neurons. Information transfer in these neurons mimics the information transfer in biological neurons, i.e., via the precise timing of spikes or a sequence of spikes. To facilitate learning in such networks, new learning algorithms based on varying degrees of biological plausibility have also been developed recently. Addition of the temporal dimension for information encoding in SNNs yields new insight into the dynamics of the human brain and could result in compact representations of large neural networks. As such, SNNs have great potential for solving complicated time-dependent pattern recognition problems because of their inherent dynamic representation. This article presents a state-of-the-art review of the development of spiking neurons and SNNs, and provides insight into their evolution as the third generation neural networks.

Quantized state simulation of spiking neural networks

SIMULATION ◽  
2011 ◽  
Vol 88 (3) ◽  
pp. 299-313 ◽  
Author(s):  
Guillermo L Grinblat ◽  
Hernán Ahumada ◽  
Ernesto Kofman
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Discrete Event ◽  
State System ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Computational Costs ◽  
Simulation Results ◽  
Neuroscience Community

In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system that experience activity.

scholarly journals Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

2021 ◽  
Author(s):  
Bojian Yin ◽  
Federico Corradi ◽  
Sander M. Bohté
Keyword(s):  
Neural Networks ◽  
Time Domain ◽  
Recurrent Neural Networks ◽  
State Of The Art ◽  
Spiking Neurons ◽  
Recurrent Networks ◽  
Computationally Efficient ◽  
Hardware Implementations ◽  
Comparable Performance ◽  
The Time Domain

ABSTRACTInspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons’ energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that they theoretically are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance. Together, this positions SNNs as an attractive solution for AI hardware implementations.

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