large spiking neural networks
Direct Training for Spiking Neural Networks: Faster, Larger, Better
... Spiking neural networks (SNNs) that enables energy effi- cient implementation on emerging neuromorphic hardware are gaining more ...artificial neural net- works (ANNs), due to the lack of ...
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Efficient computation in adaptive artificial spiking neural networks
... analog neural units that use the derived half-sigmoid-like transfer function, both for fully connected feed- forward ANNs and for various deep convolutional neural network ...ImageNet Large-Scale ...
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Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition
... There are many parameters related to the architecture, loss function, and optimization method that are not learned, but rather must be chosen a priori ; these are known as hyperparameters (examples include learning ...
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GPU-based implementation of real-time system for spiking neural networks
... Implementation exhibits good scalability and is targeted for large network simulations with a large number of synaptic connections. Simulation accuracy plays a secondary role. As a consequence, the 1 st ...
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Feature Extraction using Spiking Convolutional Neural Networks
... Spiking neural networks are biologically plausible counter- parts of the artificial neural networks, artificial neural net- works are usually trained with stochastic gradient ...
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Evolving unipolar memristor spiking neural networks
... unipolar networks are based on the concept of setting up “weight oscillators” to create a context-sensitive dynamic connectivity map through ...bipolar networks, activity can be perturbed in a single ...
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Surrogate Gradient Learning in Spiking Neural Networks
... train large ANNs on vast amounts of ...Unlike large-scale ANNs, which had to wait for several decades until the necessary computational resources were available for training them, we currently have the ...
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Theory and tools for the conversion of analog to spiking convolutional neural networks
... all samples of the training set. This is a very conservative approach, which ensures that the SNN firing rates never exceed the maximum firing rate. The drawback is that this procedure is prone to be influenced by ...
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Computational modeling with spiking neural networks
... In [37.100, 101], the eSNN was applied on a real world data set in the context of an ecological modeling prob- lem. For many invertebrate species little is known about their response to environmental variables over ...
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Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
... Adaptive Spiking Neuron To create artificial spiking neural networks based on sigma-delta spike-time coding, we address the limited dynamic range of standard LIF or corresponding ...
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Homeostatic Fault Tolerance in Spiking Neural Networks : A Dynamic Hardware Perspective
... to establish homeostasis. During this experiment, we consider the left direction to be free of an obstacle. The system could achieve a constant rate up to 75% degradation in the synaptic input. Thereafter, the forward ...
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A survey on real time processing with spiking neural networks
... Neural networks have emerged has one of the powerful tools for real time processing such as pattern classification, recognition, prediction and ...of neural networks have motivated researchers ...
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Design Considerations for Training Memristor Crossbars Used in Spiking Neural Networks
... artificial neural networks that, with advancements in training algorithms and architectures, have cemented themselves as the future of learning and data processing in modern ...current neural ...
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Unsupervised learning in an ensemble of spiking neural networks mediated by ITDP
... our spiking system is not as adept at directly and quickly filtering out the wrong output from the ensemble WTAs when an output neuron in the ensemble fires for multiple ...our spiking ensemble system can ...
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Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
... What has been referred to in this Thesis as PB(reset) is really PB(i, d, w, c, o) with w = reset, which, in our implementation, is simply a “very large number.” If w = reset, then that will cause the neuron N to ...
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Financial Time Series Prediction Using Spiking Neural Networks
... of neural network needs to take into account that the PSN functions in a fundamentally different way to the other neural networks; unlike the other neural networks and the LPC, the PSN ...
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Fault-tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs
... of neural self-learning can be implemented on the FPGA with minimal hardware overhead and power con- ...considering large network architectures and applications, the proposed implementation should be ...
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Neuromorphic audio processing through real-time embedded spiking neural networks.
... of spiking neural network layers to process the information provided by the sensor, trying to mimic the interconnection that biological neurons ...receiving large amounts of rate-coded information ...
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Learning spatio-temporally encoded pattern transformations in structured spiking neural networks.
... multilayer networks to map between spike patterns, it proved neces- sary to represent input patterns with sufficiently rich spiking activity at each input neuron; preliminary simulations indicated that ...
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FPGA Based Platform for Spiking Neural Network
... for large scale spiking neural networks, Understanding of mathematical model of Neuron and Hardware utilization versus number of ...reconfigurable neural layer, which is implemented ...
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