Information-Transmission Rates in Manual Control of Unstable Systems With Time Delays

2015 ◽  
Vol 62 (1) ◽  
pp. 342-351 ◽  
Author(s):  
Mircea F. Lupu ◽  
Mingui Sun ◽  
Fei-Yue Wang ◽  
Zhi-Hong Mao
Keyword(s):  
Information Transmission ◽  
Time Delays ◽  
Manual Control ◽  
Unstable Systems ◽  
Transmission Rates

scholarly journals How Far can Neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli Processes

2019 ◽  
Vol 29 (08) ◽  
pp. 1950003 ◽  
Author(s):  
Agnieszka Pregowska ◽  
Ehud Kaplan ◽  
Janusz Szczepanski
Keyword(s):  
Information Transmission ◽  
Crucial Role ◽  
Transition Probabilities ◽  
Temporal Code ◽  
Rate Code ◽  
Neural Codes ◽  
Loss Of Information ◽  
Transmission Rates ◽  
Firing Rates ◽  
Markov Source

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon’s definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code). In a previous paper we found that a crucial role in the relation between information transmission rates (ITRs) and firing rates is played by a parameter [Formula: see text], which is the sum of transition probabilities from the no-spike state to the spike state and vice versa. We found that in this case too a crucial role is played by the same parameter [Formula: see text]. We calculated the maximal and minimal bounds of the quotient of ITRs for these sources. Next, making use of the entropy grouping axiom, we determined the loss of information in a Markov source compared with the information in the corresponding Bernoulli source for a given word length. Our results show that in the case of correlated signals the loss of information is relatively small, and thus temporal codes, which are more energetically efficient, can replace rate codes effectively. These results were confirmed by experiments.

Enhanced Dynamic Set-point Weighting Design for Two-Input-Two-Output (TITO) Unstable Processes

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Purushottama Rao Dasari ◽  
A. Seshagiri Rao
Keyword(s):  
Pid Control ◽  
Time Delays ◽  
Closed Loop ◽  
Control Law ◽  
Unstable Systems ◽  
Set Point ◽  
Point Tracking ◽  
Unstable Processes ◽  
Tito Systems ◽  
Independent Loops

Abstract Control of unstable systems with time delays usually result in overshoots in the closed loop responses. The intricacy involved in multivariable unstable processes further makes the problem more challenging. In industry, set-point weighting is one of the recommended methods to minimize the overshoot. However, design of the set-point weighting parameters determines the percentage of minimization of the overshoot. In this paper, a method is proposed to design the set-point weighting parameters for unstable multivariable processes which is relatively simple and also reduces the overshoot. Weighting is considered for both proportional (β) and derivative (γ) terms in the PID control law. In the closed loop relation for set-point tracking, the coefficients of ‘s’ and ‘s3’ both in the numerator and denominator are made equal in order to find dynamically β and γ. The obtained expressions for β and γ are simple and dynamically depends on the controller parameters and are applied to TITO systems in present work. Decouplers are used in TITO systems mainly to reduce the interaction between the loops so that they can be viewed as independent loops. Decoupler design suggested by (Hazarika and Chidambaram [1] has been used in this work and two TITO unstable processes with time delays are illustrated here. Comparison with the reported methods available in literature verifies that the proposed method gives improved closed loop performance.

Rate of Information Transmission in Human Manual Control of an Unstable System

2013 ◽  
Vol 43 (2) ◽  
pp. 259-263 ◽  
Author(s):  
Mircea F. Lupu ◽  
Mingui Sun ◽  
Ruiping Xia ◽  
Zhi-Hong Mao
Keyword(s):  
Information Transmission ◽  
Manual Control ◽  
Unstable System

scholarly journals Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 92
Author(s):  
Agnieszka Pregowska
Keyword(s):  
Information Transmission ◽  
Action Potentials ◽  
Transition Probabilities ◽  
Transmission Rate ◽  
Spike Trains ◽  
Learning Mechanisms ◽  
Transmission Rates ◽  
Stationary Stochastic Processes ◽  
System Information ◽  
Selection Of

In the nervous system, information is conveyed by sequence of action potentials, called spikes-trains. As MacKay and McCulloch suggested, spike-trains can be represented as bits sequences coming from Information Sources (IS). Previously, we studied relations between spikes’ Information Transmission Rates (ITR) and their correlations, and frequencies. Now, I concentrate on the problem of how spikes fluctuations affect ITR. The IS are typically modeled as stationary stochastic processes, which I consider here as two-state Markov processes. As a spike-trains’ fluctuation measure, I assume the standard deviation σ, which measures the average fluctuation of spikes around the average spike frequency. I found that the character of ITR and signal fluctuations relation strongly depends on the parameter s being a sum of transitions probabilities from a no spike state to spike state. The estimate of the Information Transmission Rate was found by expressions depending on the values of signal fluctuations and parameter s. It turned out that for smaller s<1, the quotient ITRσ has a maximum and can tend to zero depending on transition probabilities, while for 1<s, the ITRσ is separated from 0. Additionally, it was also shown that ITR quotient by variance behaves in a completely different way. Similar behavior was observed when classical Shannon entropy terms in the Markov entropy formula are replaced by their approximation with polynomials. My results suggest that in a noisier environment (1<s), to get appropriate reliability and efficiency of transmission, IS with higher tendency of transition from the no spike to spike state should be applied. Such selection of appropriate parameters plays an important role in designing learning mechanisms to obtain networks with higher performance.

scholarly journals Controlling Virus Spread Using an Intermittent Quarantine Strategy on Multiplex Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xingguo Li ◽  
Xiaoping Luo ◽  
Yiwu Wang
Keyword(s):  
Information Transmission ◽  
Time Delays ◽  
Information Diffusion ◽  
Mean Field ◽  
Transmission Probability ◽  
Virus Spread ◽  
Spreading Dynamics ◽  
Control Virus ◽  
Optimal Information ◽  
Virus Spreading

Virus spreading on the Internet will negatively affect cybersecurity. An intermittent quarantine immunization strategy to control virus spreading when containing information diffusion is proposed herein. In this model, information and virus spread on different subnetworks and interact with each other. We further develop a heterogeneous mean-field approach with time delays to investigate this model and use Monte Carlo simulations to systematically investigate the spreading dynamics. For a relatively short intermittent period, the optimal information transmission probability of the virus will be significantly suppressed. However, when the intermittent period is extremely long; increasing the probability of information transmission can control the virus spreading as well as suppress the increase in the intermittent period. Finally, it is shown that the average degree of the two subnetworks does not qualitatively affect the spreading dynamics.

HAMILTONIAN MODELS FOR QUANTUM MEMORY CHANNELS

2011 ◽  
Vol 09 (02) ◽  
pp. 625-635
Author(s):  
ANTONIO D'ARRIGO ◽  
GIULIANO BENENTI ◽  
GIUSEPPE FALCI
Keyword(s):  
Quantum Information ◽  
Information Transmission ◽  
Quantum Memory ◽  
Memory Effects ◽  
Transmission Rates ◽  
Quantum Transmission ◽  
Hamiltonian Models

Quantum memory channels are attracting growing interest, motivated by both realistic possibilities of transferring information by means of quantum hardware and inadequacies of the memoryless approximation. In fact, subsequent uses of the same quantum transmission resource can be significantly correlated. In this paper we review two Hamiltonian models describing memory effects in a purely dephasing spin-boson channel and in a channel with damping visualized by a micromaser system, respectively. In both cases, we show that the quantum information transmission rates are higher than in the memoryless limit.

Equilibrating errors: reliable estimation of information transmission rates in biological systems with spectral analysis-based methods

2014 ◽  
Vol 108 (3) ◽  
pp. 305-320 ◽  
Author(s):  
Irina Ignatova ◽  
Andrew S. French ◽  
Esa-Ville Immonen ◽  
Roman Frolov ◽  
Matti Weckström
Keyword(s):  
Spectral Analysis ◽  
Information Transmission ◽  
Biological Systems ◽  
Transmission Rates ◽  
Reliable Estimation
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