Bio-inspired spiking neural network for nonlinear systems control

Synthesis of recurrent neural dynamics for monotone inclusion with application to Bayesian inference

We propose a top-down approach to construct recurrent neural circuit dynamics for the mathematical problem of monotone inclusion (MoI). MoI in a general optimization framework that encompasses a wide range of contemporary problems, including Bayesian inference and Markov decision making. We show that in a recurrent neural circuit/network with Poisson neurons, each neuron's firing curve can be understood as a proximal operator of a local objective function, while the overall circuit dynamics constitutes an operator-splitting system of ordinary differential equations whose equilibrium point corresponds to the solution of the MoI problem. Our analysis thus establishes that neural circuits are a substrate for solving a broad class of computational tasks. In this regard, we provide an explicit synthesis procedure for building neural circuits for specific MoI problems and demonstrate it for the specific case of Bayesian inference and sparse neural coding.

Externally Recurrent Neural Network based identification of dynamic systems using Lyapunov stability analysis

This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the unknown dynamics of complex nonlinear systems and time series prediction. The proposed model utilizes the present as well as delayed values of the system outputs as well as of the external input. The weight update equations are tested for their boundedness by applying the Lyapunov stability method. Further, the error convergence proof is also given. The proposed model is put to test by considering various nonlinear examples and its performance is also compared with other state of the art methods. The results obtained in the present study indicate that the method is efficient and has provided accurate results.

Design of Neural Network Quantizers for Networked Control Systems

Nowadays, networked control systems (NCSs) are being widely implemented in many applications. However, several problems negatively affect and compromise the design of practical NCSs. One of them is the performance degradation of the system due to quantization. This paper aims to develop dynamic quantizers for NCSs and their design methods that alleviate the effects of the quantization problem. In this paper, we propose a type of dynamic quantizers implemented with neural networks and memories, which can be tuned by a time series data of the plant inputs and outputs. Since the proposed quantizer can be designed without the model information of the system, the quantizer could be applied to any system with uncertainty or nonlinearity. This paper gives two types of quantizers, and they differ from each other in the neural networks structure. The effectiveness of these quantizers and their design method are verified using numerical examples. Besides, their performances are compared among each other using statistical analysis tools.