scholarly journals Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

2016 ◽  
Vol 12 (2) ◽  
pp. e1004720 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taşkin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Spiking Neural Networks ◽  
Closed Loop Control ◽  
Loop Control ◽  
Dynamics And Function ◽  
And Function

scholarly journals Recovery of dynamics and function in spiking neural networks by closed-loop control

2015 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taskin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Brain Activity ◽  
Open Loop ◽  
Spiking Neural Networks ◽  
Delayed Feedback Control ◽  
Loop Control ◽  
Wide Range ◽  
Underlying Network ◽  
And Function

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

scholarly journals Verification of Closed-loop Systems with Neural Network Controllers

10.29007/btv1 ◽  
2019 ◽  
Author(s):  
Diego Manzanas Lopez ◽  
Patrick Musau ◽  
Hoang-Dung Tran ◽  
Taylor T. Johnson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Systems ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Feed Forward ◽  
Loop Control ◽  
Closed Loop Systems ◽  
Feed Forward Neural Networks ◽  
Neural Network Controllers

This benchmark suite presents a detailed description of a series of closed-loop control systems with artificial neural network controllers. In many applications, feed-forward neural networks are heavily involved in the implementation of controllers by learning and representing control laws through several methods such as model predictive control (MPC) and reinforcement learning (RL). The type of networks that we consider in this manuscript are feed-forward neural networks consisting of multiple hidden layers with ReLU activation functions and a linear activation function in the output layer. While neural network con- trollers have been able to achieve desirable performance in many contexts, they also present a unique challenge in that it is difficult to provide any guarantees about the correctness of their behavior or reason about the stability a system that employs their use. Thus, from a controls perspective, it is necessary to verify them in conjunction with their corresponding plants in closed-loop. While there have been a handful of works proposed towards the verification of closed-loop systems with feed-forward neural network controllers, this area still lacks attention and a unified set of benchmark examples on which verification techniques can be evaluated and compared. Thus, to this end, we present a range of closed-loop control systems ranging from two to six state variables, and a range of controllers with sizes in the range of eleven neurons to a few hundred neurons in more complex systems.

Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control

2013 ◽  
pp. 142-161 ◽  
Author(s):  
George Thomas ◽  
Timothy Wilmot ◽  
Steve Szatmary ◽  
Dan Simon ◽  
William Smith
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Closed Loop ◽  
Knee Prosthesis ◽  
Open Loop ◽  
Closed Loop Control ◽  
Open Loop Control ◽  
Loop Control ◽  
Prosthetic Knee ◽  
Artificial Neural

This chapter discusses closed-loop control development and simulation results for a semi-active above-knee prosthesis. This closed-loop control is a delta control that is added to previously developed open-loop control. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. Closed-loop control using artificial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective closed-loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The research also contributes to the field of ANNs; it shows that they are able to mitigate some of the effects of noise and disturbances that will be common in normal operation of a prosthesis and that they can provide better robustness and safer operation with less risk of stumbles and falls. It is demonstrated that ANNs are able to improve average performance over open-loop control by up to 8% and that they show the greatest improvement in performance when there is high risk of stumbles.

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

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