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Showing 1 to 20 of 59 for “"Spiking neural networks"”.
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Pattern Recognition Using Spiking Neural Networks
… pattern recognition in comparison to traditional neural networks convinced neuroscientists to introduce a biologically plausible model of the neuron, which is known as spiking neurons. In opposition to conventional neuron, spiking neurons use a short electrical pulse known as a spike to transfer …
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Spiking neural networks and their applications
<p>"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations …
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Analysis framework for adaptive spiking neural networks
… understanding how mental states might arise from spiking activity. Cortical modeling has traditionally focused on the mechanisms and behaviors at the cellular level. However, developments with respect to group or population level phenomena indicate that a shift in focus is necessary to understand …
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Constructive spiking neural networks for simulations of neuroplasticity
Artificial neural networks are important tools in machine learning and neuroscience; however, a difficult step in their implementation is the selection of the neural network size and structure. This thesis develops fundamental theory on algorithms for constructing neurons in spiking neural networks …
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Biologically motivated reinforcement learning in spiking neural networks
… Learning (RL) in a biologically feasible neural network model, as a proxy for investigating RL in the brain itself. Recent research has demonstrated that synaptic plasticity in the higher regions of the brain (such as the cortex and striatum) depends on neuromodulatory signals which …
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Spiking Neural Networks for Low-Power Medical Applications
… series of "spikes" instead of continuous values, spiking neural networks (SNN) may be the right model architecture to address these concerns. This work investigates the proposed advantages of SNNs compared to more traditional architectures when tested on various medical datasets. We compare the …
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Efficient Processing of Spiking Neural Networks: A Memory-Based Approach
… of functionality and processing efficiency. Spiking neural networks (SNNs) are widely used as the computing model for the neuromorphic system. The neurons in the SNN communicate using spikes, which allows the SNN to be efficiently implemented on hardware. In addition, the SNNs are based on …
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Inference and Learning in Spiking Neural Networks for Neuromorphic Systems
… (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not …
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Inference And Learning In Spiking Neural Networks For Neuromorphic Systems
… (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not …
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Neuromorphic audio processing through real-time embedded spiking neural networks.
… and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound …
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Polychronization as a mechanism for language acquisition in spiking neural networks
… understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic …
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Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks
… Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic …
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An Exploration of Spiking Neural Networks and their use on Reinforcement Learning Tasks
Artificial neural networks have recently been the prominent architecture for reinforcement learning tasks. However, there is emerging evidence that spiking neural networks can perform just as well and can retain this performance across similar environments. Spiking neural networks are experiencing …
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Binaural sound source localization using machine learning with spiking neural networks features extraction
… are used to acquire binaural signals and a spiking neural network is used to compare signals from the two sensors. The firing rates of coincidence-neurons in the spiking neural network model provide information as to the location of a sound source. Previous methods have used a …
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Energy-Efficient Neuromorphic Hardware. Design and Optimization of Brain-Inspired Computing Paradigms for Spiking Neural Networks
L'abstract è presente nell'allegato / the abstract is in the attachment
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Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications
<p>Spiking neural networks have been used to investigate the mechanisms of processing</p><p>in biological neural circuits or to propose hypotheses that can be tested in exper-</p><p>iments. Because of their biological plausibility and event-based information trans-</p><p>mission, Spiking Neural …
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Achieving baseline states in sparsely connected spiking-neural networks: stochastic and dynamic approaches in mathematical neuroscience
Networks of simple spiking neurons provide abstract models for studying the dynamics of biological neural tissue. At the expense of cellular-level complexity, they are a frame-work in which we can gain a clearer understanding of network-level dynamics. Substantial insight can be gained …
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