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Related papers: A Stochastic Approach to STDP

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The Dynamic Vision Sensor (DVS) has many attributes that allow it to be well suited to the task for UAV Detection. This paper is the first to look at exploiting the features of an Event Camera solely for Drone Detection while combining it…

Image and Video Processing · Electrical Eng. & Systems 2019-04-30 Paul Kirkland

Atomic-scale materials synthesis via layer deposition techniques present a unique opportunity to control material structures and yield systems that display unique functional properties that cannot be stabilized using traditional bulk…

Computational Physics · Physics 2020-06-30 Siyan Liu , Nikolay Borodinov , Lukas Vlcek , Dan Lu , Nouamane Laanait , Rama K. Vasudevan

We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation,…

Machine Learning · Statistics 2025-12-11 Yihang Gao , Michael K. Ng , Michael W. Mahoney , Sen Na

The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of…

Neural and Evolutionary Computing · Computer Science 2020-11-19 Alireza Nadafian , Mohammad Ganjtabesh

Stochastic Gradient Descent (SGD) has played a central role in machine learning. However, it requires a carefully hand-picked stepsize for fast convergence, which is notoriously tedious and time-consuming to tune. Over the last several…

Machine Learning · Computer Science 2019-06-10 Zhenxun Zhuang , Ashok Cutkosky , Francesco Orabona

Recent research in the field of spiking neural networks (SNNs) has shown that recurrent variants of SNNs, namely long short-term SNNs (LSNNs), can be trained via error gradients just as effective as LSTMs. The underlying learning method…

Neural and Evolutionary Computing · Computer Science 2020-06-18 Manuel Traub , Martin V. Butz , R. Harald Baayen , Sebastian Otte

This paper presents a methodology for using varying sample sizes in sequential quadratic programming (SQP) methods for solving equality constrained stochastic optimization problems. The first part of the paper deals with the delicate issue…

Optimization and Control · Mathematics 2023-03-23 Albert S. Berahas , Raghu Bollapragada , Baoyu Zhou

Scalable algorithms of posterior approximation allow Bayesian nonparametrics such as Dirichlet process mixture to scale up to larger dataset at fractional cost. Recent algorithms, notably the stochastic variational inference performs local…

Machine Learning · Computer Science 2025-02-25 Kart-Leong Lim , Xudong Jiang

Dynamic simulation plays a crucial role in power system transient stability analysis, but traditional numerical integration-based methods are time-consuming due to the small time step sizes. Other semi-analytical solution methods, such as…

Systems and Control · Electrical Eng. & Systems 2025-03-14 Kaiyang Huang , Yang Liu , Kai Sun , Feng Qiu

Spiking Neural Networks (SNNs) have emerged as a hardware efficient architecture for classification tasks. The challenge of spike-based encoding has been the lack of a universal training mechanism performed entirely using spikes. There have…

Neural and Evolutionary Computing · Computer Science 2023-08-25 Anmol Biswas , Vivek Saraswat , Udayan Ganguly

Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on neuromorphic hardware. However, the majority of existing…

Neural and Evolutionary Computing · Computer Science 2024-07-02 Yi Jiang , Sen Lu , Abhronil Sengupta

Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks…

Neural and Evolutionary Computing · Computer Science 2024-10-29 Bing Han , Feifei Zhao , Yi Zeng , Guobin Shen

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing…

Neural and Evolutionary Computing · Computer Science 2019-09-26 Ruthvik Vaila , John Chiasson , Vishal Saxena

Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly…

Neurons and Cognition · Quantitative Biology 2009-05-20 Yuko K. Takahashi , Hiroshi Kori , Naoki Masuda

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are…

Neural and Evolutionary Computing · Computer Science 2022-02-28 J. Lu , J. J. Hagenaars , G. C. H. E. de Croon

We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability density is known and can be evaluated. Our method uses a novel…

Machine Learning · Statistics 2025-06-19 Agnimitra Dasgupta , Javier Murgoitio-Esandi , Ali Fardisi , Assad A Oberai

We develop a high-dimensional scaling limit for Stochastic Gradient Descent with Polyak Momentum (SGD-M) and adaptive step-sizes. This provides a framework to rigourously compare online SGD with some of its popular variants. We show that…

Machine Learning · Statistics 2026-02-19 Aukosh Jagannath , Taj Jones-McCormick , Varnan Sarangian

The collective dynamics of excitatory pulse coupled neural networks with spike timing dependent plasticity (STDP) is studied. Depending on the model parameters stationary states characterized by High or Low Synchronization can be observed.…

Disordered Systems and Neural Networks · Physics 2015-04-14 Kaare Mikkelsen , Alberto Imparato , Alessandro Torcini

The performance of standard stochastic approximation implementations can vary significantly based on the choice of the steplength sequence, and in general, little guidance is provided about good choices. Motivated by this gap, in the first…

Optimization and Control · Mathematics 2015-03-19 Farzad Yousefian , Angelia Nedić , Uday V. Shanbhag

Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency. Yet, the implementation of the attention mechanism using…

Hardware Architecture · Computer Science 2024-11-12 Zihang Song , Prabodh Katti , Osvaldo Simeone , Bipin Rajendran
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