Plasticity and stability in recurrent neural networks

rodbar dam slope stability analysis using neural networks

در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...

Stability analysis of diagonal recurrent neural networks

Input output stability of recurrent neural networks

i Foreword Recurrent neural networks are an attractive tool for both practical applications and for the modeling of biological nerve nets, but their successful application requires an understanding of their dynamical properties, in particular, their stability. The present work provides an in-depth study of this challenging issue and contributes a number of new results that are also important fo...

Robust stability of stochastic fuzzy impulsive recurrent neural networks with\ time-varying delays

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

Specialisation in Recurrent Neural Networks with Spike-timing-dependent Plasticity

We examine how Spike-Timing-Dependent Plasticity (STDP) can strengthen recurrent excitatory connections in a network of Poisson neurons stimulated by two pools of external inputs, where only one pool has spike-time correlation. We derive conditions on the STDP and network parameters such that the network exhibits a stable activity in terms of spiking-rates and show how the evolution of the recu...