Subdiffusive Dynamics of Bump Attractors: Mechanisms and Functional Roles

2015 ◽  
Vol 27 (2) ◽  
pp. 255-280 ◽  
Author(s):  
Yang Qi ◽  
Michael Breakspear ◽  
Pulin Gong
Keyword(s):  
Neural Coding ◽  
Spatial Working Memory ◽  
Activity Patterns ◽  
Stimulus Presentation ◽  
Generalized Langevin Equation ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Normal Diffusion ◽  
Characteristic Features ◽  
Noisy Inputs

Bump attractors are localized activity patterns that can self-sustain after stimulus presentation, and they are regarded as the neural substrate for a host of perceptual and cognitive processes. One of the characteristic features of bump attractors is that they are neutrally stable, so that noisy inputs cause them to drift away from their initial locations, severely impairing the accuracy of bump location-dependent neural coding. Previous modeling studies of such noise-induced drifting activity of bump attractors have focused on normal diffusive dynamics, often with an assumption that noisy inputs are uncorrelated. Here we show that long-range temporal correlations and spatial correlations in neural inputs generated by multiple interacting bumps cause them to drift in an anomalous subdiffusive way. This mechanism for generating subdiffusive dynamics of bump attractors is further analyzed based on a generalized Langevin equation. We demonstrate that subdiffusive dynamics can significantly improve the coding accuracy of bump attractors, since the variance of the bump displacement increases sublinearly over time and is much smaller than that of normal diffusion. Furthermore, we reanalyze existing psychophysical data concerning the spread of recalled cue position in spatial working memory tasks and show that its variance increases sublinearly with time, consistent with subdiffusive dynamics of bump attractors. Based on the probability density function of bump position, we also show that the subdiffusive dynamics result in a long-tailed decay of firing rate, greatly extending the duration of persistent activity.

scholarly journals Tracking the Contents of Spatial Working Memory during an Acute Bout of Aerobic Exercise

2021 ◽  
pp. 1-16
Author(s):  
Jordan Garrett ◽  
Tom Bullock ◽  
Barry Giesbrecht
Keyword(s):  
Working Memory ◽  
Aerobic Exercise ◽  
Neural Coding ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Stimulus Presentation ◽  
Visual Responses ◽  
Acute Bout ◽  
Exercise Condition ◽  
Sensory Evoked

Abstract Recent studies have reported enhanced visual responses during acute bouts of physical exercise, suggesting that sensory systems may become more sensitive during active exploration of the environment. This raises the possibility that exercise may also modulate brain activity associated with other cognitive functions, like visual working memory, that rely on patterns of activity that persist beyond the initial sensory evoked response. Here, we investigated whether the neural coding of an object location held in memory is modulated by an acute bout of aerobic exercise. Participants performed a spatial change detection task while seated on a stationary bike at rest and during low-intensity cycling (∼50 watts/50 RPM). Brain activity was measured with EEG. An inverted encoding modeling technique was employed to estimate location-selective channel response functions from topographical patterns of alpha-band (8–12 Hz) activity. There was strong evidence of robust spatially selective responses during stimulus presentation and retention periods both at rest and during exercise. During retention, the spatial selectivity of these responses decreased in the exercise condition relative to rest. A temporal generalization analysis indicated that models trained on one time period could be used to reconstruct the remembered locations at other time periods, providing evidence that a unitary code supports memory during both conditions. However, generalization was degraded during exercise. Together, these results demonstrate that it is possible to reconstruct the contents of working memory at rest and during exercise, but that exercise can result in degraded responses, which contrasts with the enhancements observed in early sensory processing.

Reducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations

2020 ◽  
Vol 14 (2) ◽  
pp. 167-175
Author(s):  
Li Zhang ◽  
Volker Schwieger
Keyword(s):  
Low Cost ◽  
Ground Plane ◽  
Single Frequency ◽  
Spatial Correlations ◽  
Raw Data ◽  
Multipath Effect ◽  
Temporal Correlations ◽  
Multipath Effects ◽  
Gnss Receivers ◽  
Multipath Signals

AbstractThe investigations on low-cost single frequency GNSS receivers at the Institute of Engineering Geodesy (IIGS) show that u-blox GNSS receivers combined with low-cost antennas and self-constructed L1-optimized choke rings can reach an accuracy which almost meets the requirements of geodetic applications (see Zhang and Schwieger [25]). However, the quality (accuracy and reliability) of low-cost GNSS receiver data should still be improved, particularly in environments with obstructions. The multipath effects are a major error source for the short baselines. The ground plate or the choke ring ground plane can reduce the multipath signals from the horizontal reflector (e. g. ground). However, the shieldings cannot reduce the multipath signals from the vertical reflectors (e. g. walls).Because multipath effects are spatially and temporally correlated, an algorithm is developed for reducing the multipath effect by considering the spatial correlations of the adjoined stations (see Zhang and Schwieger [24]). In this paper, an algorithm based on the temporal correlations will be introduced. The developed algorithm is based on the periodic behavior of the estimated coordinates and not on carrier phase raw data, which is easy to use. Because, for the users, coordinates are more accessible than the raw data. The multipath effect can cause periodic oscillations but the periods change over time. Besides this, the multipath effect’s influence on the coordinates is a mixture of different multipath signals from different satellites and different reflectors. These two properties will be used to reduce the multipath effect. The algorithm runs in two steps and iteratively. Test measurements were carried out in a multipath intensive environment; the accuracies of the measurements are improved by about 50 % and the results can be delivered in near-real-time (in ca. 30 minutes), therefore the algorithm is suitable for structural health monitoring applications.

scholarly journals Abnormal brain activity patterns during spatial working memory task in patients with end-stage renal disease on maintenance hemodialysis: a fMRI study

2020 ◽  
Author(s):  
Jinzhuang Huang ◽  
Lei Xie ◽  
Ruiwei Guo ◽  
Jinhong Wang ◽  
Jinquan Lin ◽  
...  
Keyword(s):  
Working Memory ◽  
Frontal Cortex ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Activity Patterns ◽  
Parietal Lobule ◽  
Stage Renal Disease ◽  
End Stage ◽  
Esrd Patients ◽  
Back Task

Abstract Hemodialysis (HD) is associated with cognitive impairment in patients with end-stage renal disease (ESRD). However, the neural mechanism of spatial working memory (SWM) impairment in HD-ESRD patients remains unclear. We investigated the abnormal alterations in SWM-associated brain activity patterns in HD-ESRD patients using blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) technique during n-back tasks. Twenty-two HD-ESRD patients and 22 well-matched controls underwent an fMRI scan while undergoing a three-load n-back tasks with different difficulty levels. Cognitive and mental states were assessed using a battery of neuropsychologic tests. The HD-ESRD patients exhibited worse memory abilities than controls. Compared with the control group, the HD-ESRD patient group showed lower accuracy and longer response time under the n-back tasks, especially in the 2-back task. The patterns of brain activation changed under different working memory loads in the HD-ESRD patients, showing decreased activity in the right medial frontal gyrus and inferior frontal gyrus under 0-back and 1-back task, while more decreased activation in the bilateral frontal cortex, parietal lobule, anterior/posterior cingulate cortex and insula cortex under 2-back task. With the increase of task difficulty, the activation degree of the frontal and parietal cortex decreased. More importantly, we found that lower activation in frontal cortex and parietal lobule was associated with worse cognitive function in the HD-ESRD patients. These results demonstrate that the abnormal brain activity patterns of frontal cortex and parietal lobule may reflect the neural mediation of SWM impairment.

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

Adaptation and Temporal Decorrelation by Single Neurons in the Primary Visual Cortex

2003 ◽  
Vol 89 (6) ◽  
pp. 3279-3293 ◽  
Author(s):  
Xiao-Jing Wang ◽  
Yinghui Liu ◽  
Maria V. Sanchez-Vives ◽  
David A. McCormick
Keyword(s):  
Cortical Neurons ◽  
Neural Coding ◽  
Model Neuron ◽  
Ionic Currents ◽  
Cellular Mechanism ◽  
Membrane Properties ◽  
Sensory Inputs ◽  
V1 Neurons ◽  
Temporal Correlations ◽  
Temporal Decorrelation

Limiting redundancy in the real-world sensory inputs is of obvious benefit for efficient neural coding, but little is known about how this may be accomplished by biophysical neural mechanisms. One possible cellular mechanism is through adaptation to relatively constant inputs. Recent investigations in primary visual (V1) cortical neurons have demonstrated that adaptation to prolonged changes in stimulus contrast is mediated in part through intrinsic ionic currents, a Ca2+-activated K+ current ( IKCa) and especially a Na+-activated K+ current ( IKNa). The present study was designed to test the hypothesis that the activation of adaptation ionic currents may provide a cellular mechanism for temporal decorrelation in V1. A conductance-based neuron model was simulated, which included an IKCa and an IKNa. We show that the model neuron reproduces the adaptive behavior of V1 neurons in response to high contrast inputs. When the stimulus is stochastic with 1/ f 2 or 1/ f-type temporal correlations, these autocorrelations are greatly reduced in the output spike train of the model neuron. The IKCa is effective at reducing positive temporal correlations at approximately 100-ms time scale, while a slower adaptation mediated by IKNa is effective in reducing temporal correlations over the range of 1–20 s. Intracellular injection of stochastic currents into layer 2/3 and 4 (pyramidal and stellate) neurons in ferret primary visual cortical slices revealed neuronal responses that exhibited temporal decorrelation in similarity with the model. Enhancing the slow afterhyperpolarization resulted in a strengthening of the decorrelation effect. These results demonstrate the intrinsic membrane properties of neocortical neurons provide a mechanism for decorrelation of sensory inputs.

scholarly journals Two adaptive radiative transfer schemes for numerical weather prediction models

2007 ◽  
Vol 7 (21) ◽  
pp. 5659-5674 ◽  
Author(s):  
V. Venema ◽  
A. Schomburg ◽  
F. Ament ◽  
C. Simmer
Keyword(s):  
Radiative Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Limited Area ◽  
Perturbation Scheme ◽  
Spatial Correlations ◽  
Radiative Fluxes ◽  
Temporal Correlations ◽  
Core Idea ◽  
Computational Resources

Abstract. Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models.

Temporal Correlations in Presentation Order during Learning Affects Human Object Recognition

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 33-33
Author(s):  
G M Wallis ◽  
H H Bülthoff
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Recognition Performance ◽  
Presentation Order ◽  
Temporal Association ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Human Object ◽  
Network Simulations ◽  
Recognition Learning

The view-based approach to object recognition supposes that objects are stored as a series of associated views. Although representation of these views as combinations of 2-D features allows generalisation to similar views, it remains unclear how very different views might be associated together to allow recognition from any viewpoint. One cue present in the real world other than spatial similarity, is that we usually experience different objects in temporally constrained, coherent order, and not as randomly ordered snapshots. In a series of recent neural-network simulations, Wallis and Baddeley (1997 Neural Computation9 883 – 894) describe how the association of views on the basis of temporal as well as spatial correlations is both theoretically advantageous and biologically plausible. We describe an experiment aimed at testing their hypothesis in human object-recognition learning. We investigated recognition performance of faces previously presented in sequences. These sequences consisted of five views of five different people's faces, presented in orderly sequence from left to right profile in 45° steps. According to the temporal-association hypothesis, the visual system should associate the images together and represent them as different views of the same person's face, although in truth they are images of different people's faces. In a same/different task, subjects were asked to say whether two faces seen from different viewpoints were views of the same person or not. In accordance with theory, discrimination errors increased for those faces seen earlier in the same sequence as compared with those faces which were not ( p<0.05).

scholarly journals Self-organization of cortical areas in the development and evolution of neocortex

2020 ◽  
Vol 117 (46) ◽  
pp. 29212-29220 ◽  
Author(s):  
Nabil Imam ◽  
Barbara L. Finlay
Keyword(s):  
Activity Patterns ◽  
Basic Mechanism ◽  
Physical Parameters ◽  
Network Growth ◽  
Cortical Areas ◽  
Mirror Reversal ◽  
Correlated Activity ◽  
The Matrix ◽  
Characteristic Features ◽  
Development And Evolution

While the mechanisms generating the topographic organization of primary sensory areas in the neocortex are well studied, what generates secondary cortical areas is virtually unknown. Using physical parameters representing primary and secondary visual areas as they vary from monkey to mouse, we derived a network growth model to explore if characteristic features of secondary areas could be produced from correlated activity patterns arising from V1 alone. We found that V1 seeded variable numbers of secondary areas based on activity-driven wiring and wiring-density limits within the cortical surface. These secondary areas exhibited the typical mirror-reversal of map topography on cortical area boundaries and progressive reduction of the area and spatial resolution of each new map on the caudorostral axis. Activity-based map formation may be the basic mechanism that establishes the matrix of topographically organized cortical areas available for later computational specialization.

scholarly journals The Stochastic Resonance Behaviors of a Generalized Harmonic Oscillator Subject to Multiplicative and Periodically Modulated Noises

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Suchuan Zhong ◽  
Kun Wei ◽  
Lu Zhang ◽  
Hong Ma ◽  
Maokang Luo
Keyword(s):  
Stochastic Resonance ◽  
Internal Noise ◽  
Exact Expression ◽  
Resonance Phenomenon ◽  
Generalized Langevin Equation ◽  
Output Amplitude ◽  
Exact Analytic Expression ◽  
Characteristic Features ◽  
And Control ◽  
Biological Solutions

The stochastic resonance (SR) characteristics of a generalized Langevin linear system driven by a multiplicative noise and a periodically modulated noise are studied (the two noises are correlated). In this paper, we consider a generalized Langevin equation (GLE) driven by an internal noise with long-memory and long-range dependence, such as fractional Gaussian noise (fGn) and Mittag-Leffler noise (M-Ln). Such a model is appropriate to characterize the chemical and biological solutions as well as to some nanotechnological devices. An exact analytic expression of the output amplitude is obtained. Based on it, some characteristic features of stochastic resonance phenomenon are revealed. On the other hand, by the use of the exact expression, we obtain the phase diagram for the resonant behaviors of the output amplitude versus noise intensity under different values of system parameters. These useful results presented in this paper can give the theoretical basis for practical use and control of the SR phenomenon of this mathematical model in future works.

scholarly journals SCIAMACHY WFM-DOAS XCO<sub>2</sub>: comparison with CarbonTracker XCO<sub>2</sub> focusing on aerosols and thin clouds

2012 ◽  
Vol 5 (2) ◽  
pp. 2887-2931 ◽  
Author(s):  
J. Heymann ◽  
O. Schneising ◽  
M. Reuter ◽  
M. Buchwitz ◽  
V. V. Rozanov ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Temporal Correlation ◽  
Anthropogenic Activity ◽  
Data Sets ◽  
Spatial Correlations ◽  
Data Set ◽  
Cloud Data ◽  
Temporal Correlations ◽  
Retrieval Errors ◽  
The Difference

Abstract. Carbon dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

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