A 77 MHz Relaxation Oscillator in 5nm FinFET with 3ns TIE over 10K cycles and ±0.3% Thermal Stability using Frequency-Error Feedback Loop

Author(s):  
Nandish Mehta ◽  
Stephen Tell ◽  
Walker Turner ◽  
Lamar Tatro ◽  
Giant Goh ◽  
...  
2021 ◽  
Author(s):  
Matthew S Price

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.


2019 ◽  
Vol 54 (7) ◽  
pp. 1952-1959 ◽  
Author(s):  
Ningxi Liu ◽  
Rishika Agarwala ◽  
Anjana Dissanayake ◽  
Daniel S. Truesdell ◽  
Sumanth Kamineni ◽  
...  

2021 ◽  
Author(s):  
Matthew S Price

Inhibitory learning promotes emotion regulation via systematic exposure to fear-inducing stimuli. Given that inconsistencies between expectations, states, and outcomes may be experienced as elements of inhibitory learning, to what extent are prediction errors – mismatches between expectations and outcomes – a core neural element of inhibitory learning? This paper takes a complex systems approach to prediction errors and postulates that a prediction error feedback loop – a series of self-perpetuating disparities between expected and perceived outcomes – could be a correlate of or responsible for improved emotion regulation from inhibitory learning. The inhibitory learning prediction error feedback loop may additionally elucidate how human and animal studies demonstrate improved emotion regulation in the form of reduced fear responses without exposure to specific fear-inducing stimuli.


2014 ◽  
Vol 43 (11) ◽  
pp. 1671-1686 ◽  
Author(s):  
Stanislaw Szczepanski ◽  
Bogdan Pankiewicz ◽  
Slawomir Koziel ◽  
Marek Wojcikowski

2020 ◽  
Vol 12 (17) ◽  
pp. 7048
Author(s):  
Aravind Chellachi Kathiresan ◽  
Jeyaraj PandiaRajan ◽  
Asokan Sivaprakash ◽  
Thanikanti Sudhakar Babu ◽  
Md. Rabiul Islam

Synchronization is a crucial problem in the grid-connected inverter’s control and operation. A phase-locked loop (PLL) is a typical grid synchronization strategy, which ought to have a high resistance to power system uncertainties since its sensitivity influences the generated reference signal. The traditional PLL catches the phase and frequency of the input signal via the feedback loop filter (LF). In general, to enhance the steady-state capability during distorted grid conditions generally, a filter tuned for nominal frequency is used. This PLL corrects large frequency deviations around the nominal frequency, which increases the PLL’s locking time. Therefore, this paper presents an adaptive feed-forward PLL, where the input signal frequency and phase under large frequency deviations are tracked precisely, which overcomes the above-mentioned limitations. The proposed adaptive PLL consists of a feedback loop that reduces the phase error. The feed-forward loop predicts the frequency and phase error, and the frequency adaptive FIR filter reduces the ripples in output, which is due to input distortions. The adaptive mechanism adjusts the gain of the filter in accordance with the supply frequency. This reduces the phase and frequency error and also decreases the locking time under wide frequency deviations. To verify the effectiveness of the proposed adaptive feed-forward PLL, the system was tested under different grid abnormal conditions. Further, the stability analysis has been carried out via a developed prototype test platform in the laboratory. To bring the proposed simulations into real-time implementations and for control strategies, an Altera Cyclone II field-programmable gate array (FPGA) board has been used. The obtained results of the proposed PLL via simulations and hardware are compared with conventional techniques, and it indicates the superiority of the proposed method. The proposed PLL effectively able to tackle the different grid uncertainties, which can be observed from the results presented in the result section.


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