State-space models reveal bursty movement behaviour of dance event visitors

AbstractPedestrian movements during large crowded events naturally consist of different modes of movement behaviour. Despite its importance for understanding crowd dynamics, intermittent movement behaviour is an aspect missing in the existing crowd behaviour literature. Here we analyse movement data generated from nearly 600 Wi-Fi sensors during large entertainment events in the Johan Cruijff ArenA football stadium in Amsterdam. We use the state-space modeling framework to investigate intermittent motion patterns. Movement models from the field of movement ecology are used to analyse individual pedestrian movement. Joint estimation of multiple movement tracks allows us to investigate statistical properties of measured movement metrics. We show that behavioural switching is not independent of external events, and the probability of being in one of the behavioural states changes over time. In addition, we show that the distribution of waiting times deviates from the exponential and is best fit by a heavy-tailed distribution. The heavy-tailed waiting times are indicative of bursty movement dynamics, which are here for the first time shown to characterise pedestrian movements in dense crowds. Bursty crowd behaviour has important implications for various diffusion-related processes, such as the spreading of infectious diseases.

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Elucidating the significance of spatial memory on movement decisions by African savannah elephants using state–space models

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2014.3042 ◽

2015 ◽

Vol 282 (1805) ◽

pp. 20143042 ◽

Cited By ~ 67

Author(s):

Leo Polansky ◽

Werner Kilian ◽

George Wittemyer

Keyword(s):

Spatial Memory ◽

State Space ◽

Animal Movement ◽

Behavioural Change ◽

Social Contexts ◽

Spatial Knowledge ◽

State Space Models ◽

Movement Behaviour ◽

Movement Data ◽

Directional Movement

Spatial memory facilitates resource acquisition where resources are patchy, but how it influences movement behaviour of wide-ranging species remains to be resolved. We examined African elephant spatial memory reflected in movement decisions regarding access to perennial waterholes. State–space models of movement data revealed a rapid, highly directional movement behaviour almost exclusively associated with visiting perennial water. Behavioural change point (BCP) analyses demonstrated that these goal-oriented movements were initiated on average 4.59 km, and up to 49.97 km, from the visited waterhole, with the closest waterhole accessed 90% of the time. Distances of decision points increased when switching to different waterholes, during the dry season, or for female groups relative to males, while selection of the closest waterhole decreased when switching. Overall, our analyses indicated detailed spatial knowledge over large scales, enabling elephants to minimize travel distance through highly directional movement when accessing water. We discuss the likely cognitive and socioecological mechanisms driving these spatially precise movements that are most consistent with our findings. By applying modern analytic techniques to high-resolution movement data, this study illustrates emerging approaches for studying how cognition structures animal movement behaviour in different ecological and social contexts.

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Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

Neural Computation ◽

10.1162/neco_a_01375 ◽

2021 ◽

pp. 1-31

Author(s):

Yalda Amidi ◽

Behzad Nazari ◽

Saeid Sadri ◽

Ali Yousefi

Keyword(s):

State Space ◽

Point Process ◽

Goodness Of Fit ◽

Estimation Accuracy ◽

Model Parameters ◽

Synaptic Connections ◽

Synaptic Coupling ◽

Modeling Framework ◽

Space Modeling ◽

Spiking Activity

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

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Bayesian Decoder Models with a Discriminative Observation Process

10.1101/2020.07.11.198564 ◽

2020 ◽

Author(s):

Mohammad R. Rezaei ◽

Alex E. Hadjinicolaou ◽

Sydney S. Cash ◽

Uri T. Eden ◽

Ali Yousefi

Keyword(s):

State Space ◽

Neural Activity ◽

Model Development ◽

High Dimensional ◽

Modeling Framework ◽

Movement Trajectories ◽

State Process ◽

Observation Process ◽

Space Modeling ◽

Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

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A Framework for Control Oriented Modeling of PEM Fuel Cells

Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications ◽

10.1115/dscc2015-9735 ◽

2015 ◽

Cited By ~ 2

Author(s):

Benjamin L. Pence ◽

Jixin Chen

Keyword(s):

Fuel Cells ◽

State Space ◽

Polymer Electrolyte Membrane ◽

Pem Fuel Cells ◽

Algebraic Equations ◽

Transient Effects ◽

Modeling Framework ◽

Two Phase ◽

One Dimensional ◽

Space Modeling

This paper develops a framework for along-the-channel and through-the-membrane control oriented modeling of polymer electrolyte membrane (PEM) fuel cells. The initial modeling framework is spatially one-dimensional by one-dimensional (1+1D) and is described by unsteady partial differential equations (PDEs). Numerical techniques convert the PDEs and boundary conditions to ordinary differential and algebraic equations that are convenient for state-space modeling. The modeling framework includes two-phase, thermal, and other transient effects. The generality of the modeling framework and its ability to be represented in state-space form facilitate complexity reduction and control-oriented application.

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