scholarly journals Information dynamics in neuromorphic nanowire networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruomin Zhu ◽  
Joel Hochstetter ◽  
Alon Loeffler ◽  
Adrian Diaz-Alvarez ◽  
Tomonobu Nakayama ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Flow ◽  
Short Term Memory ◽  
Information Storage ◽  
Information Dynamics ◽  
Information Theoretic ◽  
Neuromorphic Systems ◽  
Processing Resources ◽  
Active Information Storage ◽  
Nanowire Networks

AbstractNeuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

scholarly journals Measuring inter- and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248166
Author(s):  
Christiane B. Wiebel-Herboth ◽  
Matti Krüger ◽  
Patricia Wollstadt
Keyword(s):  
Driving Simulator ◽  
Information Storage ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
First Order ◽  
Dynamic Task ◽  
Temporal Correlations ◽  
Scan Path ◽  
Active Information Storage ◽  
Scan Pattern

Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predictive of the next fixation in time. However, this assumption may not be sufficient in general, as recent research has shown that scan patterns may also exhibit more long-range temporal correlations. Thus, we here evaluate the active information storage (AIS) as a novel information-theoretic approach to quantifying scan path predictability in a dynamic task. In contrast to the GTE, the AIS provides means to statistically test and account for temporal correlations in scan path data beyond the previous last fixation. We compare AIS to GTE in a driving simulator experiment, in which participants drove in a highway scenario, where trials were defined based on an experimental manipulation that encouraged the driver to start an overtaking maneuver. Two levels of difficulty were realized by varying the time left to complete the task. We found that individual observers indeed showed temporal correlations beyond a single past fixation and that the length of the correlation varied between observers. No effect of task difficulty was observed on scan path predictability for either AIS or GTE, but we found a significant increase in predictability during overtaking. Importantly, for participants for which the first-order Markov chain assumption did not hold, this was only shown using AIS but not GTE. We conclude that accounting for longer time horizons in scan paths in a personalized fashion is beneficial for interpreting gaze pattern in dynamic tasks.

scholarly journals Information Processing in the Brain as Optimal Entropy Transport: A Theoretical Approach

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1231
Author(s):  
Carlos Islas ◽  
Pablo Padilla ◽  
Marco Antonio Prado
Keyword(s):  
Information Processing ◽  
Information Flow ◽  
Optimal Transport ◽  
Brain Activity ◽  
Entropy Condition ◽  
Informational Entropy ◽  
Information Theoretic ◽  
Entropy Transport ◽  
Monge Ampere ◽  
The Brain

We consider brain activity from an information theoretic perspective. We analyze the information processing in the brain, considering the optimality of Shannon entropy transport using the Monge–Kantorovich framework. It is proposed that some of these processes satisfy an optimal transport of informational entropy condition. This optimality condition allows us to derive an equation of the Monge–Ampère type for the information flow that accounts for the branching structure of neurons via the linearization of this equation. Based on this fact, we discuss a version of Murray’s law in this context.

scholarly journals Quantifying the Predictability of Visual Scanpaths Using Active Information Storage

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 167
Author(s):  
Patricia Wollstadt ◽  
Martina Hasenjäger ◽  
Christiane B. Wiebel-Herboth
Keyword(s):  
Information Storage ◽  
Gaze Behavior ◽  
Behavioral Experiments ◽  
Human Machine Interaction ◽  
Cognitive States ◽  
Visual Scanpath ◽  
Active Information Storage ◽  
Temporal Horizon ◽  
Machine Interaction ◽  
Transition Entropy

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.

Cognitive Information Processing Measures as Predictors of Monitoring Performance

1982 ◽  
Vol 54 (3_suppl) ◽  
pp. 1299-1302 ◽  
Author(s):  
Douglas Cellar ◽  
Gerald V. Barrett ◽  
Ralph Alexander ◽  
Dennis Doverspike ◽  
Jay C. Thomas ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Search ◽  
Short Term Memory ◽  
Female College Students ◽  
Short Term ◽  
Term Memory ◽  
Cognitive Information Processing ◽  
Precise Understanding ◽  
Cognitive Information ◽  
Complex Monitoring

To obtain a more precise understanding of the constructs underlying complex monitoring, measures of short-term memory and visual search were administered to 7 male and 13 female college students. The hypothesis was that more rapid short-term memory and visual search would be related to successful monitoring. A correlational analysis indicated that choice reaction time was related to performance ( r = –.38 and –.43) while rate of serial comparisons was not ( r = –.08 and –.28). It was concluded that information-processing measures enhanced the understanding of the underlying processes in monitoring beyond that provided by traditional cognitive tests.

scholarly journals A New Approach to the Fabrication of Memristive Neuromorphic Devices: Compositionally Graded Films

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3680
Author(s):  
Jong-Gul Yoon
Keyword(s):  
Short Term Memory ◽  
Chemical Vapor ◽  
Complementary Metal Oxide Semiconductor ◽  
Information Storage ◽  
Oxide Semiconductor ◽  
Film Electrode ◽  
Retention Performance ◽  
Short Term ◽  
Neuromorphic Computing ◽  
Term Memory

Energy-efficient computing paradigms beyond conventional von-Neumann architecture, such as neuromorphic computing, require novel devices that enable information storage at nanoscale in an analogue way and in-memory computing. Memristive devices with long-/short-term synaptic plasticity are expected to provide a more capable neuromorphic system compared to traditional Si-based complementary metal-oxide-semiconductor circuits. Here, compositionally graded oxide films of Al-doped MgxZn1−xO (g-Al:MgZnO) are studied to fabricate a memristive device, in which the composition of the film changes continuously through the film thickness. Compositional grading in the films should give rise to asymmetry of Schottky barrier heights at the film-electrode interfaces. The g-Al:MgZnO films are grown by using aerosol-assisted chemical vapor deposition. The current-voltage (I-V) and capacitance-voltage (C-V) characteristics of the films show self-rectifying memristive behaviors which are dependent on maximum applied voltage and repeated application of electrical pulses. Endurance and retention performance tests of the device show stable bipolar resistance switching (BRS) with a short-term memory effect. The short-term memory effects are ascribed to the thermally activated release of the trapped electrons near/at the g-Al:MgZnO film-electrode interface of the device. The volatile resistive switching can be used as a potential selector device in a crossbar memory array and a short-term synapse in neuromorphic computing.

scholarly journals Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1601
Author(s):  
Zheng Fang ◽  
David L. Dowe ◽  
Shelton Peiris ◽  
Dedi Rosadi
Keyword(s):  
Time Series ◽  
Environmental Science ◽  
Short Term Memory ◽  
Arima Model ◽  
Theoretic Approach ◽  
Arma Models ◽  
Information Theoretic ◽  
Minimum Message Length ◽  
Message Length ◽  
Real World Datasets

Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revisits a model selection technique based on Minimum Message Length (MML) and investigates its use in hybrid time series analysis. MML is a Bayesian information-theoretic approach and has been used in selecting the best ARMA model. We utilize the long short-term memory (LSTM) approach to construct a hybrid ARMA-LSTM model and show that MML performs better than AIC, BIC, and HQ in selecting the model—both in the traditional ARMA models (without LSTM) and with hybrid ARMA-LSTM models. These results held on simulated data and both real-world datasets that we considered. We also develop a simple MML ARIMA model.

Effect of Physical Exhaustion on Cognitive Functioning

1997 ◽  
Vol 84 (1) ◽  
pp. 291-298 ◽  
Author(s):  
Y-A. Féry ◽  
A. Ferry ◽  
A. Vom Hofe ◽  
M. Rieu
Keyword(s):  
Reaction Time ◽  
Perceived Exertion ◽  
Short Term Memory ◽  
Strenuous Exercise ◽  
Decision Task ◽  
Short Term ◽  
Physical Effort ◽  
Difficult Decision ◽  
Central Processing ◽  
Processing Resources

Experiments utilizing reaction time to measure the effects of fatigue on cognition must discern sensitivity of peripheral and central processing to strenuous exercise. The additive factors method enables one to stipulate that if fatigue interacts with subjects' reaction time in a decision task, central processing is affected by fatigue. While pedaling at different intensities, 13 physically-fit men had to perform a series of short-term memory tests. The tests were executed during a constant workload session and a progressive workload session in which subjects pedaled until exhaustion. Subjects provided ratings on Borg's 1970 scale to measure the psychological effects of the physical effort such as perceived exertion. Allocation of processing resources was also measured to determine attentional constraints exerted by the dual-task situation. Analysis showed that decision reaction time was affected only during the exhausting bout of the progressive workload session and for the more difficult decision task. We discuss our results in the context of arousal and the allocation of processing resources.

Memory

2018 ◽  
pp. 94-117
Keyword(s):  
Information Processing ◽  
Processing System ◽  
Information Storage And Retrieval ◽  
Point Of View ◽  
Information Storage ◽  
Retrieval Process ◽  
Information Processing System ◽  
Storage And Retrieval ◽  
Long Time ◽  
Primary Focus

Learning takes place if there is a repeat of stimuli, or otherwise, extinction occurs, which is forgetting. To make the consumers learn the product and not lose sight for a long time is a primary focus for marketers. It means that marketers are more concerned about individuals' information storage and retrieval process. This chapter discusses the information-processing system, parts of this system, and forgetting as well as memorizing. At the end of the chapter, memory is evaluated from the point of view of marketing.

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