Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models
نویسندگان
چکیده
منابع مشابه
Adaptive stimulus design for dynamic recurrent neural network models
We present a theoretical application of an optimal experiment design (OED) methodology to the development of mathematical models to describe the stimulus-response relationship of sensory neurons. Although there are a few related studies in the computational neuroscience literature on this topic, most of them are either involving non-linear static maps or simple linear filters cascaded to a stat...
متن کاملFitting of dynamic recurrent neural network models to sensory stimulus-response data
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) which h...
متن کاملK-Component Adaptive Recurrent Neural Network Language Models
Conventional n-gram language models for automatic speech recognition are insufficient in capturing long-distance dependencies and brittle with respect to changes in the input domain. We propose a k-component recurrent neural network language model (KARNNLM) that addresses these limitations by exploiting the long-distance modeling ability of recurrent neural networks and by making use of k diffe...
متن کاملDesign of Self-Constructing Recurrent-Neural- Network-Based Adaptive Control
Recently, neural-network-based adaptive control technique has attracted increasing attentions, because it has provided an efficient and effective way in the control of complex nonlinear or ill-defined systems (Duarte-Mermoud et al., 2005; Hsu et al., 2006; Lin and Hsu, 2003; Lin et al., 1999; Peng et al. 2004). The key elements of this success are the approximation capabilities of the neural ne...
متن کاملDesign of Adaptive Robot Control System Using Recurrent Neural Network
The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed contro...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neural Circuits
سال: 2019
ISSN: 1662-5110
DOI: 10.3389/fncir.2018.00119