###### Syst

- Int. J. Neural Syst. (springer.com)

###### Class of Neural Networks

- Xu W, Cao J, Xiao M et al (2015) A new framework for analysis on stability and bifurcation in a class of neural networks with discrete and distributed delays. (springer.com)

###### complex-valued neural networks

- Hirose A (2012) Complex-valued neural networks. (springer.com)
- Ding X, Cao J, Zhao X et al (2017) Finite-time stability of fractional-order complex-valued neural networks with time delays. (springer.com)
- Gong W, Liang J, Zhang C et al (2016) Nonlinear measure approach for the stability analysis of complex-valued neural networks. (springer.com)
- Song Q, Zhao Z (2016) Stability criterion of complex-valued neural networks with both leakage delay and time-varying delays on time scales. (springer.com)
- Li Y, Liao X, Li H (2016) Global attracting sets of non-autonomous and complex-valued neural networks with time-varying delays. (springer.com)
- Hirose A (1992) Dynamics of fully complex-valued neural networks. (springer.com)
- Aizenberg I (2011) Complex-valued neural networks with multi-valued neurons. (springer.com)
- Nitta T (2004) Orthogonality of decision boundaries in complex-valued neural networks. (springer.com)
- Chen XF, Song QK (2013) Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales. (springer.com)
- Xu XH, Zhang JY, Shi JZ (2014) Exponential stability of complex-valued neural networks with mixed delays. (springer.com)
- Zhou C, Zhang WL, Yang XS, Xu C, Feng JW (2017) Finite-time synchronization of complex-valued neural networks with mixed delays and uncertain perturbations. (springer.com)
- Pan J, Liu XZ, Xie WC (2015) Exponential stability of a class of complex-valued neural networks with time-varying delays. (springer.com)
- Song QK, Zhao ZJ, Liu YR (2015) Stability analysis of complex-valued neural networks with probabilistic time-varying delays. (springer.com)
- Song QK, Yan H, Zhao ZJ, Liu YR (2016) Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects. (springer.com)

###### neurons

- Such a recurrent network can generate internal noise optimal for stochastic resonance effects on spike-based communications between neurons. (frontiersin.org)
- Do prefrontal cortical neurons support specifically the requirements of persistent activity networks, beyond known differences in recurrent connectivity ( Elston, 2003 )? (jneurosci.org)
- we discuss the impact of the extra sensitivity to the amplitude fluctuations on the stability of persistent activity states in recurrent networks of IF neurons. (jneurosci.org)

###### 2016

- Wang Z, Huang L (2016) Global stability analysis for delayed complex-valued BAM neural networks. (springer.com)
- Liu XW, Chen TP (2016) Global exponential stability for complex-valued recurrent neural networks with asynchronous time delays. (springer.com)

###### synchronization

- We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. (frontiersin.org)
- Li Y (2017) Impulsive synchronization of stochastic neural networks via controlling partial states. (springer.com)

###### stochastic

- Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. (springer.com)
- Dawes, R.L.: Quantum neurodynamics: neural stochastic filtering with the Schroedinger equation. (springer.com)

###### Exponential Stability

- Cao, J., Wang, J.: Global Exponential Stability and Periodicity of Recurrent Neural Networks with Time Delays. (springer.com)
- Jiang, H., Teng, Z.: Global Exponential Stability of Cellular Neural Networks with Time-varying Coefficients and Delays. (springer.com)

###### grounded spikin

- Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. (frontiersin.org)

###### Time-varyin

- Cao, J., Wang, J.: Global Asymptotic Stability of a General Class of Recurrent Neural Networks with Time-varying Delays. (springer.com)
- Liao, X.F., Wang, J.: Global and Robust Stability of Interval Hopfield Neural Networks with Time-varying Delays. (springer.com)

###### correlates

- Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R (2005) Neural correlates of reach errors. (springer.com)
- We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. (frontiersin.org)

###### stability

- In this paper, the problem of global robust stability (GRAS) is investigated for a class of interval neural networks described by nonlinear delayed differential equations of the neutral type. (springer.com)
- Cao, J., Ho, D.W.C.: A General Framework for Global Asymptotic Stability Analysis of Delayed Neural Networks Based on LMI Approach. (springer.com)
- Cao, J., Chen, T.: Globally Exponentially Robust Stability and Periodicity of Delayed Neural Networks. (springer.com)
- Cao, J., Li, X.: Stability in Delayed Cohen-Grossberg Neural Networks: LMI Optimization Approach. (springer.com)
- Hu J, Wang J (2012) Global stability of complex-valued recurrent neural networks with time-delays. (springer.com)
- Dong T, Liao X, Wang A (2015) Stability and Hopf bifurcation of a complex-valued neural network with two time delays. (springer.com)
- Zhao H, Yuan J, Zhang X (2015) Stability and bifurcation analysis of reaction-diffusion neural networks with delays. (springer.com)
- Zhang ZY, Lin C, Chen B (2014) Global stability criterion for delayed complex-valued recurrent neural networks. (springer.com)

###### Circuits

- Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. (frontiersin.org)
- The organization of neuronal wiring determines the flow of information in neural circuits and hence has significant implications for functions of the circuits. (frontiersin.org)

###### Networks

- Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. (frontiersin.org)
- Previous studies demonstrated that brief synchronized increase in a neural firing [Population Spikes (PS)] can be generated in homogenous recurrent neural networks with short-term synaptic depression (STD). (frontiersin.org)
- Hamzacebi, C., Kutay, F.: Electric Consumption Forecasting of Turkey Using Artificial Neural Networks Up to Year 2000. (springer.com)
- Lee, C.-H., Teng, C.-C.: Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks. (springer.com)
- In this paper, a class of fractional-order complex-valued Cohen-Grossberg neural networks is investigated. (springer.com)

###### Delays

- Dong T, Liao X (2013) Hopf-Pitchfork bifurcation in a simplified BAM neural network model with multiple delays. (springer.com)

###### generate

- We are seeking to understand the underlying neural mechanisms that generate gamma-band spectral peaks in the cerebral cortex by studying gamma activity in the local field potential (LFP) in the primary visual cortex, V1. (jneurosci.org)

###### illustrate

- We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. (frontiersin.org)

###### robust

- However, the implications for learning turn out to be significant: learning with a feedforward architecture is robust following changes in the stimulus-desired outcome mapping but not necessarily the motor command-outcome mapping, while learning with a recurrent architecture is robust under changes in the motor command-outcome mapping but not necessarily the stimulus-desired outcome mapping. (springer.com)

###### classification

- Major, T.C., Conrad, J.M.: The effects of pre-filtering and individualizing components for electroencephalography neural network classification. (springer.com)
- Amin MF, Murase K (2009) Single-layered complex-valued neural network for real-valued classification problems. (springer.com)

###### paper

- In this paper, a class of delayed complex-valued neural network with diffusion under Dirichlet boundary conditions is considered. (springer.com)
- In this paper we propose a fuzzy recurrent neural network (FRNN) based fuzzy time series forecasting method using genetic algorithm. (springer.com)

###### brain computer

- Quantum neural network-based EEG filtering for a brain-computer interface. (springer.com)

###### properties

- By using the properties of the Laplacian operator and separating the neural network into real and imaginary parts, the corresponding characteristic equation of neural network is obtained. (springer.com)

###### multiple

- Over the past decades, multiple strategies of neural network modeling have emerged in computational neuroscience. (frontiersin.org)

###### connections

- Neural Comput 19(1), 170-193, 2007b) have highlighted the potential importance of these recurrent connections by proposing an alternative architecture in which the cerebellum is embedded in a recurrent loop with brainstem control circuitry. (springer.com)

###### Systems

- In: Advances in neural information processing systems, vol 8. (springer.com)