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 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 The Modelling, Simulation and FPGA-Based Implementation for Stepper Motor Wide Range Speed Closed-Loop Drive System Design

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 56 ◽  
Author(s):  
Chiu-Keng Lai ◽  
Jhang-Shan Ciou ◽  
Chia-Che Tsai
Keyword(s):  
Embedded System ◽  
High Speed ◽  
Closed Loop ◽  
Open Loop ◽  
Drive System ◽  
Stepper Motor ◽  
Motor Drive ◽  
Digital Controllers ◽  
Loop Control ◽  
Wide Range

Owing to the benefits of programmable and parallel processing of field programmable gate arrays (FPGAs), they have been widely used for the realization of digital controllers and motor drive systems. Furthermore, they can be used to integrate several functions as an embedded system. In this paper, based on Matrix Laboratory (Matlab)/Simulink and the FPGA chip, we design and implement a stepper motor drive. Generally, motion control systems driven by a stepper motor can be in open-loop or closed-loop form, and pulse generators are used to generate a series of pulse commands, according to the desired acceleration/run/deceleration, in order to the drive system to rotate the motor. In this paper, the speed and position are designed in closed-loop control, and a vector control strategy is applied to the obtained rotor angle to regulate the phase current of the stepper motor to achieve the performance of operating it in low, medium, and high speed situations. The results of simulations and practical experiments based on the FPGA implemented control system are given to show the performances for wide range speed control.

scholarly journals Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE)

2021 ◽  
Author(s):  
Mark Schatza ◽  
Ethan Blackwood ◽  
Sumedh Nagrale ◽  
Alik S Widge
Keyword(s):  
Open Source ◽  
Real Time ◽  
Closed Loop ◽  
Brain Activity ◽  
Loop Control ◽  
Oscillatory Phase ◽  
Wide Range ◽  
Real Time Tracking ◽  
Therapeutic Tools ◽  
And Behavior

Closing the loop between brain activity and behavior is one of the most active areas of development in neuroscience. There is particular interest in developing closed-loop control of neural oscillations. Many studies report correlations between oscillations and functional processes. Oscillation-informed closed-loop experiments might determine whether these relationships are causal and would provide important mechanistic insights which may lead to new therapeutic tools. These closed-loop perturbations require accurate estimates of oscillatory phase and amplitude, which are challenging to compute in real time. We developed an easy to implement, fast and accurate Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE). TORTE operates with the open-source Open Ephys GUI (OEGUI) system, making it immediately compatible with a wide range of acquisition systems and experimental preparations. TORTE efficiently extracts oscillatory phase and amplitude from a target signal and includes a variety of options to trigger closed-loop perturbations. Implementing these tools into existing experiments is easy and adds minimal latency to existing protocols. Most labs use in-house lab-specific approaches, limiting replication and extension of their experiments by other groups. Accuracy of the extracted analytic signal and accuracy of oscillation-informed perturbations with TORTE match presented results by these groups. However, TORTE provides access to these tools in a flexible, easy to use toolkit without requiring proprietary software. We hope that the availability of a high-quality, open-source, and broadly applicable toolkit will increase the number of labs able to perform oscillatory closed-loop experiments, and will improve the replicability of protocols and data across labs.

scholarly journals Closed-loop optogenetic control of normal and pathological network dynamics

2020 ◽  
Author(s):  
Boubker Zaaimi ◽  
Mark Turnbull ◽  
Anupam Hazra ◽  
Yujiang Wang ◽  
Carolina Gandara de Souza ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Activity ◽  
Nonlinear Dynamical System ◽  
Brain Slices ◽  
Open Loop ◽  
Loop Control ◽  
Nonlinear Dynamical ◽  
Light Emitting

Abstract Electrical neurostimulation is effective in treating neurological disorders, but associated recording artefacts generally limit applications to ‘open-loop’ stimuli. Since light does not prevent concurrent electrical recordings, optogenetics enables real-time, continuous ‘closed-loop’ control of brain activity. Here we show that closed-loop optogenetic stimulation with excitatory opsins (CLOSe) affords precise manipulation of neural dynamics, both in vitro, in brain slices from transgenic mice, and in vivo, with anesthetised monkeys. We demonstrate the generation of oscillations in quiescent tissue, enhancement or suppression of endogenous patterns in active tissue, and modulation of seizure-like bursts elicited by 4-aminopyridine. New network properties, emergent under CLOSe, depended on the phase-shift imposed between neural activity and optical stimulation, and could be modelled with a nonlinear dynamical system. In particular, CLOSe could stabilise or destabilise limit cycles associated with seizure oscillations, evident from systematic changes in the variability and entropy of seizure trajectories that correlated with their altered duration and intensity. Furthermore, CLOSe was achieved using intracortical optrodes incorporating light-emitting diodes, paving the way for translation of closed-loop optogenetics towards therapeutic applications in humans.

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.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

Active Control of Combustion Instability Using Symmetric and Asymmetric Premix Fuel Modulation

2007 ◽  
Author(s):  
Daniel Guyot ◽  
Christian Oliver Paschereit
Keyword(s):  
Heat Release ◽  
Closed Loop ◽  
Modulation Frequency ◽  
Combustion Instability ◽  
Open Loop ◽  
Closed Loop Control ◽  
Nox Emissions ◽  
Loop Control ◽  
Asymmetric Modulation ◽  
Control Schemes

Active instability control was applied to an atmospheric swirl-stabilized premixed combustor using open loop and closed loop control schemes. Actuation was realised by two on-off valves allowing for symmetric and asymmetric modulation of the premix fuel flow while maintaining constant time averaged overall fuel mass flow. Pressure and heat release fluctuations in the combustor as well as NOx, CO and CO2 emissions in the exhaust were recorded. In the open loop circuit the heat release response of the flame was first investigated during stable combustion. For symmetric fuel modulation the dominant frequency in the heat release response was the modulation frequency, while for asymmetric modulation it was its first harmonic. In stable open loop control a reduction of NOx emissions due to fuel modulation of up to 19% was recorded. In the closed loop mode phase-shift control was applied while triggering the valves at the dominant oscillation frequency as well as at its second subharmonic. Both, open and closed loop control schemes were able to successfully control a low-frequency combustion instability, while showing only a small increase in NOx emissions compared to, for example, secondary fuel modulation. Using premixed open loop fuel modulation, attenuation was best when modulating the fuel at frequencies different from the dominant instability frequency and its subharmonic. The performance of asymmetric fuel modulation was generally slightly better than for symmetric modulation in terms of suppression levels as well as emissions. Suppression of the instability’s pressure rms level of up to 15.7 dB was recorded.

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