This document describes research on developing a stochastic model of zebrafish locomotion from experimental tracking data. The researchers:
1) Collected tracking data from videos of isolated zebrafish swimming in tanks to analyze their speed, turning, and interactions with walls over time.
2) Developed a stochastic differential equation model with coupled processes for speed and turning speed to reproduce the fish's motion characteristics.
3) Extended the model to include a coupling function representing how turning speed is affected by speed to better match the experimental speed-turning correlation.
4) Began exploring a multi-agent extension of the model to capture interactions between pairs of fish based on preliminary analysis of social forces between fish swimmers.
Biohybrid Robotic Jellyfish for Future Applications in Ocean Monitoringinside-BigData.com
In this deck from the Stanford HPC Conference, Nicole Xu from Stanford University describes how she transformed a common jellyfish into a bionic creature that is part animal and part machine.
"Animal locomotion and bioinspiration have the potential to expand the performance capabilities of robots, but current implementations are limited. Mechanical soft robots leverage engineered materials and are highly controllable, but these biomimetic robots consume more power than corresponding animal counterparts. Biological soft robots from a bottom-up approach offer advantages such as speed and controllability but are limited to survival in cell media. Instead, biohybrid robots that comprise live animals and self- contained microelectronic systems leverage the animals’ own metabolism to reduce power constraints and body as an natural scaffold with damage tolerance. We demonstrate that by integrating onboard microelectronics into live jellyfish, we can enhance propulsion up to threefold, using only 10 mW of external power input to the microelectronics and at only a twofold increase in cost of transport to the animal. This robotic system uses 10 to 1000 times less external power per mass than existing swimming robots in literature and can be used in future applications for ocean monitoring to track environmental changes."
Watch the video: https://youtu.be/HrmJFyvInj8
Learn more: https://sanfrancisco.cbslocal.com/2020/02/05/stanford-research-project-common-jellyfish-bionic-sea-creatures/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Ultrasound color Doppler imaging has been routinely used for the diagnosis of cardiovascular diseases, enabling real-time flow visualization through the Doppler effect. Yet, its inability to provide true flow velocity vectors due to its one-dimensional detection limits its efficacy. To overcome this limitation, various VFI schemes, including multi-angle beams, speckle tracking, and transverse oscillation, have been explored, with some already available commercially. However, many of these methods still rely on autocorrelation, which poses inherent issues such as underestimation, aliasing, and the need for large ensemble sizes. Conversely, speckle-tracking-based VFI enables lateral velocity estimation but suffers from significantly lower accuracy compared to axial velocity measurements.
To address these challenges, we have presented a speckle-tracking-based VFI approach utilizing multi-angle ultrafast plane wave imaging. Our approach involves estimating axial velocity components projected onto individual steered plane waves, which are then combined to derive the velocity vector. Additionally, we've introduced a VFI visualization technique with high spatial and temporal resolutions capable of tracking flow particle trajectories.
Simulation and flow phantom experiments demonstrate that the proposed VFI method outperforms both speckle-tracking-based VFI and autocorrelation VFI counterparts by at least a factor of three. Furthermore, in vivo measurements on carotid arteries using the Prodigy ultrasound scanner demonstrate the effectiveness of our approach compared to existing methods, providing a more robust imaging tool for hemodynamic studies.
Learning objectives:
- Understand fundamental limitations of color Doppler imaging.
- Understand principles behind advanced vector flow imaging techniques.
- Familiarize with the ultrasound speckle tracking technique and its implications in flow imaging.
- Explore experiments conducted using multi-angle plane wave ultrafast imaging, specifically utilizing the pulse-sequence mode on a 128-channel ultrasound research platform.
This paper explores how the adaptability of Caenorhabditis elegans locomotion behavior can be assessed through a movement-based assay. This assay is set up with a series of sinusoidal microchannels, featuring a fixed wavelength and modulating amplitude. These channels are comparable to the body diameter of the organism, and worms are allowed to travel from the input port to the output port. In regions that closely fit the worms' natural undulations, progress is quick and steady. As the channel amplitude changes along the device, the worm struggles to generate propulsive force, slows down, and eventually is unable to move forward. An array of locomotion parameters (average forward velocity, number and duration of pauses, range of contact angle, and cut-off region) are generated from the recorded videos to measure how the worm moves in the modulated sinusoidal channels. The device is tested on wild-type (N2) and two mutant (lev-8 and unc-38) C. elegans. We suggest that this passive, movement-based assay can be used to differentiate between nematodes with distinct locomotion phenotypes.
The ICES Symposium “Effects of fishing on benthic fauna, habitat and ecosystem function” took place in Tromsø, Norway from 16-19th June 2014.
Abstract:
Beam trawling causes physical disruption to the seafloor through physical contact of the gear components on the sediment and the resuspension of sediment into the water column in the turbulent wake of the gear. Recently Dutch beam trawlers have replaced tickler chains by electrodes as alternative stimulation for catching flatfish. It is claimed that benthic impacts are reduced. Here we report on trials in a medium sand fishing ground to compare the physical impact of a conventional 4m commercial tickler chain beam trawl with that of the new commercial “Delmeco” pulse trawl. We use a Kongsberg EM2040 multibeam echo sounder (MBES) to measure the extent to which the beam trawls modify the topography of the substrate and a particle size analyser (LISST 100X) to measure the concentration and particle size distribution of the sediment mobilized into the water column. MBES measurements reveal that the disturbed sediment in the trawl track was on average located at a centimetre deeper after trawling of the conventional beam trawl than after pulse trawling. Particle size distributions of the sediment plumes were measured at 25m, 45m and 65m behind the gear and did not reveal any differences in concentrations between the two trawls. Whereas the empirical data serve comparative purposes, their lack of predictive capacity limits extrapolation to fleet level. Finite element (FE) models have shown to overcome this for otter trawls by predicting the penetration depth and sediment displacement associated with each gear component in different sediment types. In this study, FE models were developed for the conventional tickler chain beam trawl and the pulse trawl. Predictions were validated by results obtained during sea trials. As such, this study attempts to provide the basis for future predictions of physical impacts of beam trawling and its technical advances on a larger spatial scale.
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
ICSU World Data System for scientific researchSSA KPI
AACIMP 2010 Summer School lecture by Kostiantyn Yefremov (WDC for Geoinformatics and Sustainable Development).
More info at http://summerschool.ssa.org.ua
Whispers of Speckles ( Part I: Building Computational Imaging Frameworks for ...Debdoot Sheet
Whispers of Speckles
( Part I: Building Computational Imaging Frameworks for Acoustic and Optical Speckle Imaging)
(Part II: Enlightenment from Shallow to Complex Reasoning with Deep Learning)
Presented at the Workshop on Machine Learning for Medical Image Analysis (WMLMIA), IIT Mandi, 25 June 2015.
- Prepared a 2D stick model of the bridge in SAP2000 using the properties mentioned in the FHWA Bridge document
- Designed the bridge for linear and non-linear structural models to conduct analyses
- Performed different analyses on the bridge – multimode analysis, pushover analysis, time history analysis and capacity spectrum analysis
- Compared the shear force, bending moment, axial force and displacement values for each abutment and pier from all analyses and critically assessed the bridge performance
Biohybrid Robotic Jellyfish for Future Applications in Ocean Monitoringinside-BigData.com
In this deck from the Stanford HPC Conference, Nicole Xu from Stanford University describes how she transformed a common jellyfish into a bionic creature that is part animal and part machine.
"Animal locomotion and bioinspiration have the potential to expand the performance capabilities of robots, but current implementations are limited. Mechanical soft robots leverage engineered materials and are highly controllable, but these biomimetic robots consume more power than corresponding animal counterparts. Biological soft robots from a bottom-up approach offer advantages such as speed and controllability but are limited to survival in cell media. Instead, biohybrid robots that comprise live animals and self- contained microelectronic systems leverage the animals’ own metabolism to reduce power constraints and body as an natural scaffold with damage tolerance. We demonstrate that by integrating onboard microelectronics into live jellyfish, we can enhance propulsion up to threefold, using only 10 mW of external power input to the microelectronics and at only a twofold increase in cost of transport to the animal. This robotic system uses 10 to 1000 times less external power per mass than existing swimming robots in literature and can be used in future applications for ocean monitoring to track environmental changes."
Watch the video: https://youtu.be/HrmJFyvInj8
Learn more: https://sanfrancisco.cbslocal.com/2020/02/05/stanford-research-project-common-jellyfish-bionic-sea-creatures/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Ultrasound color Doppler imaging has been routinely used for the diagnosis of cardiovascular diseases, enabling real-time flow visualization through the Doppler effect. Yet, its inability to provide true flow velocity vectors due to its one-dimensional detection limits its efficacy. To overcome this limitation, various VFI schemes, including multi-angle beams, speckle tracking, and transverse oscillation, have been explored, with some already available commercially. However, many of these methods still rely on autocorrelation, which poses inherent issues such as underestimation, aliasing, and the need for large ensemble sizes. Conversely, speckle-tracking-based VFI enables lateral velocity estimation but suffers from significantly lower accuracy compared to axial velocity measurements.
To address these challenges, we have presented a speckle-tracking-based VFI approach utilizing multi-angle ultrafast plane wave imaging. Our approach involves estimating axial velocity components projected onto individual steered plane waves, which are then combined to derive the velocity vector. Additionally, we've introduced a VFI visualization technique with high spatial and temporal resolutions capable of tracking flow particle trajectories.
Simulation and flow phantom experiments demonstrate that the proposed VFI method outperforms both speckle-tracking-based VFI and autocorrelation VFI counterparts by at least a factor of three. Furthermore, in vivo measurements on carotid arteries using the Prodigy ultrasound scanner demonstrate the effectiveness of our approach compared to existing methods, providing a more robust imaging tool for hemodynamic studies.
Learning objectives:
- Understand fundamental limitations of color Doppler imaging.
- Understand principles behind advanced vector flow imaging techniques.
- Familiarize with the ultrasound speckle tracking technique and its implications in flow imaging.
- Explore experiments conducted using multi-angle plane wave ultrafast imaging, specifically utilizing the pulse-sequence mode on a 128-channel ultrasound research platform.
This paper explores how the adaptability of Caenorhabditis elegans locomotion behavior can be assessed through a movement-based assay. This assay is set up with a series of sinusoidal microchannels, featuring a fixed wavelength and modulating amplitude. These channels are comparable to the body diameter of the organism, and worms are allowed to travel from the input port to the output port. In regions that closely fit the worms' natural undulations, progress is quick and steady. As the channel amplitude changes along the device, the worm struggles to generate propulsive force, slows down, and eventually is unable to move forward. An array of locomotion parameters (average forward velocity, number and duration of pauses, range of contact angle, and cut-off region) are generated from the recorded videos to measure how the worm moves in the modulated sinusoidal channels. The device is tested on wild-type (N2) and two mutant (lev-8 and unc-38) C. elegans. We suggest that this passive, movement-based assay can be used to differentiate between nematodes with distinct locomotion phenotypes.
The ICES Symposium “Effects of fishing on benthic fauna, habitat and ecosystem function” took place in Tromsø, Norway from 16-19th June 2014.
Abstract:
Beam trawling causes physical disruption to the seafloor through physical contact of the gear components on the sediment and the resuspension of sediment into the water column in the turbulent wake of the gear. Recently Dutch beam trawlers have replaced tickler chains by electrodes as alternative stimulation for catching flatfish. It is claimed that benthic impacts are reduced. Here we report on trials in a medium sand fishing ground to compare the physical impact of a conventional 4m commercial tickler chain beam trawl with that of the new commercial “Delmeco” pulse trawl. We use a Kongsberg EM2040 multibeam echo sounder (MBES) to measure the extent to which the beam trawls modify the topography of the substrate and a particle size analyser (LISST 100X) to measure the concentration and particle size distribution of the sediment mobilized into the water column. MBES measurements reveal that the disturbed sediment in the trawl track was on average located at a centimetre deeper after trawling of the conventional beam trawl than after pulse trawling. Particle size distributions of the sediment plumes were measured at 25m, 45m and 65m behind the gear and did not reveal any differences in concentrations between the two trawls. Whereas the empirical data serve comparative purposes, their lack of predictive capacity limits extrapolation to fleet level. Finite element (FE) models have shown to overcome this for otter trawls by predicting the penetration depth and sediment displacement associated with each gear component in different sediment types. In this study, FE models were developed for the conventional tickler chain beam trawl and the pulse trawl. Predictions were validated by results obtained during sea trials. As such, this study attempts to provide the basis for future predictions of physical impacts of beam trawling and its technical advances on a larger spatial scale.
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
ICSU World Data System for scientific researchSSA KPI
AACIMP 2010 Summer School lecture by Kostiantyn Yefremov (WDC for Geoinformatics and Sustainable Development).
More info at http://summerschool.ssa.org.ua
Whispers of Speckles ( Part I: Building Computational Imaging Frameworks for ...Debdoot Sheet
Whispers of Speckles
( Part I: Building Computational Imaging Frameworks for Acoustic and Optical Speckle Imaging)
(Part II: Enlightenment from Shallow to Complex Reasoning with Deep Learning)
Presented at the Workshop on Machine Learning for Medical Image Analysis (WMLMIA), IIT Mandi, 25 June 2015.
- Prepared a 2D stick model of the bridge in SAP2000 using the properties mentioned in the FHWA Bridge document
- Designed the bridge for linear and non-linear structural models to conduct analyses
- Performed different analyses on the bridge – multimode analysis, pushover analysis, time history analysis and capacity spectrum analysis
- Compared the shear force, bending moment, axial force and displacement values for each abutment and pier from all analyses and critically assessed the bridge performance
1. Stochastic modelling of zebrafish locomotion :
collective motion from the bottom-up
Adam Zienkiewicz1 • David A. W. Barton1 • Maurizio Porfiri2 • Mario di Bernardo1,3
1Department of Engineering Mathematics, University of Bristol, UK
2Department of Mechanical and Aerospace Engineering,
New York University Polytechnic School of Engineering, USA
3Department of Information and Computer Engineering,
University of Naples Federico II, Italy
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
2. Overview
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(1 / 25)
Background and motivation Why study animal motion? Why (zebra)fish?
Individual model
…motion of (isolated) zebrafish
Multi-agent modeling
…modeling schools and collective motion
Emergence of leadership in groups
…towards methods of control
• requirements of a new model
• experiments, observations
• modeling and results
• observations and experiments
• model extension & results
• macro-level dynamics
Data-driven stochastic modeling of
zebrafish locomotion.
Zienkiewicz et al. , J. Math. Biology (2014)
3. Introduction
• Recent stochastic models of fish locomotion
- “Persistent Turning Walker” (PTW) model : Kulia mugil
displacement described in terms of turning speed and its autocorrelation
- later extended to multiple fish, modelling collective behaviour (same authors)
...motivation and background
Gautrais et al. (2009) J. Math. Biol.
58(3) and (2012) PLoS Comp. Biol. 8(9)
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(2 / 25)
Berdahl et al. (2013). Science, 339(6119)
• Speed regulation as a primary response mechanism
- decomposition of interaction forces within groups of small, shoaling fish (golden shiners)
Katz et al. and Herbert-Read et al. (2011) PNAS 108(46)
- emergent sensing (distributed / decentralised) of environment via speed regulation
Berdahl et al. (2013) Science, 339(6119)
Katz et al. (2011) PNAS 108(46)
5. Introduction ...objectives
Using data collected from video tracking of individual trajectories,
develop a data-driven model framework describing the motion
characteristics of individual zebrafish
• Adapt Gautrais’ PTW (constant speed) model of fish locomotion to capture
salient swimming features of small, shoaling fish with burst-and-coast
swimming mode
- augment stochastic differential model with additional dynamic speed process
- identify sufficient key metrics required to describe fish locomotion
- allow for and characterise interactions with environment (wall-avoidance)
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(4 / 25)
• Model framework features to provide foundation for extension to multiple
fish dynamics : biologically realistic models of (zebrafish) collective motion
- speed regulation : dynamic speed interaction models
vs. (canonical) constant speed
6. Experiments
Zebrafish trajectory data -
...data capture : automated tracking
• 10 isolated individuals observed for 5 min. Each
- shallow (10 cm), square tank (120 x 120 x20 cm)
• overhead video capture - 30 fps 5 fps (Hz)
• automated visual tracking of centroid
(point) position of fish in tank
(Kalman filter)
(rate of change of heading angle of vt )
VIDEO: automated position
tracking of live zebrafish
Automated fish (particle) tracking
• compute velocity
• speed
• turning speed
Dynamical Systems Laboratory (NYU)
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(5 / 25)
7. Observations ...zebrafish trajectories
Raw trajectory data: 10 zebrafish, 5 min observations Speed (ut)
Individual zebrafish exhibit a variety of locomotory patterns:
- smooth, fluid turning
- erratic (stop / start) with sharp or spiralling turning
- wall following (thigmotaxis)
BL.s-1
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(6 / 25)
8. Observations ...segment trajectories
Swimming segment trajectories (coloured by parent fish ID F1...F10)
‘Swimming’ data isolated from individual zebrafish : 28 segments of equal duration
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(7 / 25)
9. Trajectory analysis ...primary characteristics
Time series, distribution and autocorrelation (segment S9)
Time lag (s)
ut and ωt characterised by their distributions and autocorrelation (ACF)
- approximate with normal distributions and
Typically sharper
than Gaussian
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(8 / 25)
10. Trajectory analysis ...primary characteristics
Speed / Turning speed cross correlation (log-frequency : all segments)
Strong correlation within ut and ωt joint distribution across all segments
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(9 / 25)
11. Individual Model ...stochastic differential model
Capture salient characteristics of trajectory data using mean-reverting
stochastic processes : coupled stochastic differential equations
Wiener processes: dWt and dZt
Speed:
Ornstein-Uhlenbeck (type) processes:
Couple equations
with function:
Wall interactions
via bias function:
• mean speed: , mean turning speed:
• exp. decaying ACF with rates:
• process volatilities: and
6 variable parameters :
estimated from each
swimming segment
Turning-speed:
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(10 / 25)
12. Individual Model (2) ...wall avoidance and SDE coupling
Boundary effect on turning speed (S27)Quantify effect of wall interactions
on trajectories:
- induced fwd. acceleration : inconclusive
- induced turning : trajectories bent away from
wall dependent on projected collision angle
Define a coupling function to restrict the volatility of Ωt process
as a function of Ut
• fc → σ0 as Ut → 0 (upper bounded)
• fc → 0 as Ut → ∞ (lower bounded)
• fc → σω /2 as Ut → μu (σω estimated from data)
estimate constants (A,B σ0 )
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(11 / 25)
13. Results ...segment-wise calibration and simulation
Qualitative comparisons : S13 vs. RW13
Speed Turning speed
Dist.ACF
Calibrate SDE parameters [μ,σ,ϑ]u,ω from experimental speed / turning speed data
max. likelihood est.
assume standard
(Gaussian) O-U processes
dU, dΩ numerically integrated
(Euler-Maruyama)
generate random walks
Trajectories : S13 vs. RW
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(12 / 25)
14. Results ...process coupling
Coupling function fc recovers joint distribution of Ut and Ωt
Speed / Turning speed cross correlation (composite of all segments )
experimental data simulated random walkers
...upper volatility bound σ0 fixed, μu and σω vary between RW segments
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(13 / 25)
15. Results ...segment simulation
Example simulation (2x real-time)
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(14 / 25)
16. Results ...calibrating individual fish
Trajectory comparison (individual fish) insufficient swimming data for F4 & F8
Average segment parameters used for calibration of (8) individuals
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(15 / 25)
17. Modelling a shoal ... multi-agent models of collective behaviour
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(16 / 25)
Pairs of zebrafish swimming in circular shallow tank
(45 cm radius, 10cm water depth)
Experiments:
• 18 observations of unique zebrafish pairs
• 20 min observations (30Hz sample freq.)
• automated tracking + manual repair
• samples proximal (< 2 BL) to walls omitted
pair
What kind of behaviour (rules) can we infer from observations of fish swimming together?
Attraction? Repulsion? Alignment?
Analysis of trajectory data can reveal
pairwise interactions in terms of
‘social forces’ (accelerations)
Jun – Aug 2014
Hold up! …start with two fish
(6 hrs)
18. Inferring interaction behaviour ... social forces
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(17 / 25)
Katz et al. (2011) PNAS 108(46)Golden shiners (14 x 56 min @ 30Hz)
Zebrafish (2 x 20 min @ 30Hz)
5 cm
(juvenile)
3 cm
(adult)
Zienkiewicz et al. (incomplete data)
19. Inferring interaction behaviour ... social forces
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(18 / 25)
Golden shiners (14 x 56 min @ 30Hz)
Zebrafish (2 x 20 min @ 30Hz)
5 cm
(juvenile)
3 cm
(adult)
Golden shiners
Zebrafish
Alignment :
an emergent phenomena?
Data suggests turning is dependent
only on relative position
….not orientation
no explicit alignment ‘rule’
20. Interaction model ...toy model of speed / turning forces
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(19 / 25)
Interpolate forces in primary response direction: Model potential:
Angular forceTangential (speed) force
21. Interaction model (2) ...conclusions
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(20 / 25)
Speed:
Turning speed:
Modified SDEs:
Acceleration (force) due to pair-wise interactions:
(Similarly for angular acc. )
Interaction network adjacency matrix:
Voronoi neighbourhood
(Voronoi neighbourhood, radial proximity
networks, estimated visual networks)
22. Multi-agent simulations ...2-fish example
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(21 / 25)
23. Global observables ...2-fish example
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(22 / 25)
Polarisation (P)
Milling (M)
Cohesion (P)
Mean nearest-
neighbour
distance (MNND)
Live zebrafish Simulation
2 fish, 20 mins @ 30fps 2 agents, 20 mins @ 30 Hz
A set of measures / order parameters which describe the global, or macro-scale dynamics
Relative alignment
Rotation around a
common centre of mass
Rotation around a
common centre of mass
24. Multi-agent simulations ... 1000 fish example !!
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(23 / 25)
25. Leadership and collective decision making
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(24 / 25)
How can collective dynamics
be modulated by the
presence of a subset of
Informed individuals?
Emergent leadership in the
absence of explicit signals....
• zero turning (translation)
• constant turning (circular)
• ‘blind’ agent
• agent(s) with preferential
heading direction
Practical examples:
foraging, migration,
danger awareness
...artificial control ?
26. Conclusions
• direct calibration from experimental data, inc. boundary avoidance
- produce simulated trajectories with comparable curvature
- capture ‘passive’ wall following behaviour with ϑω /σω dependence
• describe zebrafish locomotion with an extended PTW model
- characterised by autoregressive, stochastic processes for
both speed and turning speed
- stochastic speed process more suitable for burst-and-coast
swimming mode of small, schooling fish
• model framework allows explicit inclusion of both speed and
turning speed modulation as responses to dynamic environment
- equilibrium bias of both speed and turning speed can be evolved
to simulate linear accelerations and torques (independently)
- infer interaction ‘forces’ to model group behaviour
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
(25 / 25)
27. Acknowledgements
My supervisors:
Mario di Bernardo
David Barton
Maurizio Porfiri
My sponsors (U.K.):
Dynamical Systems Laboratory:
(New York University Polytechnic School of Engineering)
Sachit Butail
Fabrizio Ladu
...thank you for listening
Adam Zienkiewicz Stochastic modelling of zebrafish locomotion
Complexity (BCCS) Seminar – 16 December 2014
