This is lecure 3 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=dtpgJLRt90M
YouTube Link: https://youtu.be/Ia0FSogTRaw
** Full Stack Web Development Training: https://www.edureka.co/masters-program/full-stack-developer-training **
This Edureka PPT on What is JavaScript explains all the fundamentals of JavaScript with examples. It also explains various features and applications of JavaScript in the following sequence:
Origin of JavaScript
What is JavaScript?
What can JavaScript do?
JavaScript Frameworks
HTML vs CSS vs JavaScript
Benefits of JavaScript
JavaScript Fundamentals
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(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
EM propulsion drive technology road map. Matter in motion exhibits internal Lorentz-contracted moving standing waves (de Broglie matter waves). The inverse effect of self-induced motion of matter may be potentially realized by utilizing synthesized red- and blue-shifted Lorentz-Doppler waves in a phase conjugate four-wave mixing process modulating a standing wave signal to generate a matter wave producing self-induced motion of a wave system without expulsion of reaction mass. A simplified impulse drive may be constructed with a standing wave cavity resonator excited by two-counter-propagating traveling waves with independent phase and frequency control.
YouTube Link: https://youtu.be/Ia0FSogTRaw
** Full Stack Web Development Training: https://www.edureka.co/masters-program/full-stack-developer-training **
This Edureka PPT on What is JavaScript explains all the fundamentals of JavaScript with examples. It also explains various features and applications of JavaScript in the following sequence:
Origin of JavaScript
What is JavaScript?
What can JavaScript do?
JavaScript Frameworks
HTML vs CSS vs JavaScript
Benefits of JavaScript
JavaScript Fundamentals
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
EM propulsion drive technology road map. Matter in motion exhibits internal Lorentz-contracted moving standing waves (de Broglie matter waves). The inverse effect of self-induced motion of matter may be potentially realized by utilizing synthesized red- and blue-shifted Lorentz-Doppler waves in a phase conjugate four-wave mixing process modulating a standing wave signal to generate a matter wave producing self-induced motion of a wave system without expulsion of reaction mass. A simplified impulse drive may be constructed with a standing wave cavity resonator excited by two-counter-propagating traveling waves with independent phase and frequency control.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
In this system we will make extensive use of files system in C++.
We will have a login id system initially. In this system we will be having separate functions for
• Getting the information
• Getting customer information who are lodged in
• Allocating a room to the customer
• Checking the availability
• Displaying the features of the rooms.
• Preparing a billing function for the customer according to his room no.
In the software developed separate functions will be there for each of the above points so that there is ample scope for adding more features in the near future.
Download From Here : https://drive.google.com/folderview?id=0B5y_t4zL91BZaWRkY1VPeElJNVE&usp=sharing
Computational Motor Control: Optimal Control for Deterministic Systems (JAIST...hirokazutanaka
This is lecure 2 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=lNH1q4y1m-U
Computational Motor Control: Kinematics & Dynamics (JAIST summer course)hirokazutanaka
Computational Motor Control: Kinematics & Dynamics (JAIST summer course)
This is lecture 1 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=8nk4DlpAaS8
Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...hirokazutanaka
This is lecure 4 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=2-VRBIg5m0w
Computational Motor Control: Reinforcement Learning (JAIST summer course) hirokazutanaka
This is lecure 6 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=GHMcx5F0_j8
Computational Motor Control: Optimal Control for Stochastic Systems (JAIST su...hirokazutanaka
This is lecure 5 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=XS7MDRMPQfU
Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electrical and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. First, we will present models of slow dynamics emerging on large cortical scales controlled by both subcortical networks and neurovascular coupling. The focus is on modeling migraine, though this approach is nested within the wider interest in modeling slow and large-scale dynamics in the brain. The aim is not only to better understand pain conditions and fluctuations in the resting state that causes these conditions but also to identify new opportunities to intervene with medical devices and implantable neuroprostheses. To this end, we then present the relevant state of the art of neuromodulation in migraine and approaches in fusion of both developments towards a translational computational neuroscience.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
In this system we will make extensive use of files system in C++.
We will have a login id system initially. In this system we will be having separate functions for
• Getting the information
• Getting customer information who are lodged in
• Allocating a room to the customer
• Checking the availability
• Displaying the features of the rooms.
• Preparing a billing function for the customer according to his room no.
In the software developed separate functions will be there for each of the above points so that there is ample scope for adding more features in the near future.
Download From Here : https://drive.google.com/folderview?id=0B5y_t4zL91BZaWRkY1VPeElJNVE&usp=sharing
Computational Motor Control: Optimal Control for Deterministic Systems (JAIST...hirokazutanaka
This is lecure 2 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=lNH1q4y1m-U
Computational Motor Control: Kinematics & Dynamics (JAIST summer course)hirokazutanaka
Computational Motor Control: Kinematics & Dynamics (JAIST summer course)
This is lecture 1 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=8nk4DlpAaS8
Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...hirokazutanaka
This is lecure 4 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=2-VRBIg5m0w
Computational Motor Control: Reinforcement Learning (JAIST summer course) hirokazutanaka
This is lecure 6 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=GHMcx5F0_j8
Computational Motor Control: Optimal Control for Stochastic Systems (JAIST su...hirokazutanaka
This is lecure 5 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=XS7MDRMPQfU
Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electrical and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. First, we will present models of slow dynamics emerging on large cortical scales controlled by both subcortical networks and neurovascular coupling. The focus is on modeling migraine, though this approach is nested within the wider interest in modeling slow and large-scale dynamics in the brain. The aim is not only to better understand pain conditions and fluctuations in the resting state that causes these conditions but also to identify new opportunities to intervene with medical devices and implantable neuroprostheses. To this end, we then present the relevant state of the art of neuromodulation in migraine and approaches in fusion of both developments towards a translational computational neuroscience.
Computational Motor Control: Introduction (JAIST summer course)hirokazutanaka
This is a course introduction to JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). https://www.youtube.com/user/ht2022columbia
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Thedynamicbehaviourofastructureiscloselyrelatedtoitsnaturalfrequenciesand
correspondingmodeshapes. Awellknownphenomenonisthatwhenastructureissubjectedto
asinusoidalforceandtheforcingfrequencyapproachesoneofthenaturalfrequenciesofthe
structure,theresponseofthestructurewillbecomedynamicallyamplifiedi.e.resonanceoccurs.
Naturalfrequenciesandtheircorrespondingmodeshapesarerelateddirectlytothestructure’s
massandstiffnessdistribution(foranundampedsystem).
Aneigenvalueproblemallowsthecalculationofthe(undamped)naturalfrequenciesandmode
shapesofastructure. Aconcerninthedesignofstructuressubjecttodynamicloadingistoavoid
orcopewiththeeffectsofresonance.
Anotherimportantaspectofaneigenvaluesolutionisinitsmathematicalsignificance-thatis,it
formsthebasisofthetechniqueofmodesuperposition(aneffectivesolutionstrategytodecouple
acoupleddynamicmatrixequationsystem). Themodeshapematrixisusedasatransformation
matrixtoconverttheproblemfromaphysicalcoordinatesystemtoageneralizedcoordinate
system( modes pace).
In general for an FE model, there can be any number of natural frequencies and corresponding
mode shapes. In practice only a few of the lowest frequencies and mode shapes may be required.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Passenger seat is main part of vehicle which has direct effect on her/his convenience. Seat suspension can remove unwanted and harmful vibration if right parameters were selected. Each of human body organs has specific natural frequency. When vehicle vibration reaches to this natural frequency, resonance will occur, and this phenomenon is harmful in long term. Usually lumped models used to predict human body response to vibration. In this paper, via Kitazaki biodynamic model, the seat to head vibration transmissibility was minimized by artificial neural network method. By this method, the optimum spring constant, damper coefficient and mass values were found.
ANALYSIS OF A CHAOTIC SPIKING NEURAL MODEL : THE NDS NEURONcsandit
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are performed to further understand the underlying dynamics of the model and enhance it as well.
Chaos provides many interesting properties that can be exploited to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and
synchronization. Chaos might play an important role in information processing tasks in human brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are followed to push the boundaries of the NDS model in order to enhance it. One way is to study the effects of scaling the parameters of the chaotic equations of the NDS model and study the
resulted dynamics. Another way is to study the method that is used in discretization of the original R¨ossler that the NDS model is based on. These approaches have revealed some facts about the NDS attractor and suggest why such a model can be stabilized to large number of
unstable periodic orbits (UPOs) which might correspond to memories in phase space.
Analysis of a chaotic spiking neural model the nds neuroncsandit
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,
viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are
performed to further understand the underlying dynamics of the model and enhance it as well.
Chaos provides many interesting properties that can be exploited to achieve computational
tasks. Such properties are sensitivity to initial conditions, space filling, control and
synchronization. Chaos might play an important role in information processing tasks in human
brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos
then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are
followed to push the boundaries of the NDS model in order to enhance it. One way is to study
the effects of scaling the parameters of the chaotic equations of the NDS model and study the
resulted dynamics. Another way is to study the method that is used in discretization of the
original R¨ossler that the NDS model is based on. These approaches have revealed some facts
about the NDS attractor and suggest why such a model can be stabilized to large number of
unstable periodic orbits (UPOs) which might correspond to memories in phase space.
ANALYSIS OF A CHAOTIC SPIKING NEURAL MODEL : THE NDS NEURONcscpconf
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are
performed to further understand the underlying dynamics of the model and enhance it as well.Chaos provides many interesting properties that can be exploited to achieve computational
tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Chaos might play an important role in information processing tasks in human
brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are followed to push the boundaries of the NDS model in order to enhance it. One way is to study
the effects of scaling the parameters of the chaotic equations of the NDS model and study the resulted dynamics. Another way is to study the method that is used in discretization of the
original R¨ossler that the NDS model is based on. These approaches have revealed some facts about the NDS attractor and suggest why such a model can be stabilized to large number of unstable periodic orbits (UPOs) which might correspond to memories in phase space.
Detection of jargon words in a text using semi supervised learningcsandit
Further analysis and experimentation is carried out in this paper for a chaotic dynamic model,
viz. the Nonlinear Dynamic State neuron (NDS). The analysis and experimentations are
performed to further understand the underlying dynamics of the model and enhance it as well.
Chaos provides many interesting properties that can be exploited to achieve computational
tasks. Such properties are sensitivity to initial conditions, space filling, control and
synchronization. Chaos might play an important role in information processing tasks in human
brain as suggested by biologists. If artificial neural networks (ANNs) is equipped with chaos
then it will enrich the dynamic behaviours of such networks. The NDS model has some
limitations and can be overcome in different ways. In this paper different approaches are
followed to push the boundaries of the NDS model in order to enhance it. One way is to study
the effects of scaling the parameters of the chaotic equations of the NDS model and study the
resulted dynamics. Another way is to study the method that is used in discretization of the
original R¨ossler that the NDS model is based on. These approaches have revealed some facts
about the NDS attractor and suggest why such a model can be stabilized to large number of
unstable periodic orbits (UPOs) which might correspond to memories in phase space.
Analysis of large scale spiking networks dynamics with spatio-temporal constr...Hassan Nasser
Recent experimental advances have made it possible to record up to several hundreds of neurons simultaneously in the cortex or in the retina. Analysing such data requires mathematical and numerical methods to describe the spatio-temporal correlations in population activity. This can be done thanks to Maximum Entropy method. Here, a crucial parameter is the product NxR where N is the number of neurons and R the memory depth of correlations (how far in the past does the spike activity affects the current state). Standard statistical mechanics methods are limited to spatial correlation structure with
R = 1 (e.g. Ising model) whereas methods based on transfer matrices, allowing the analysis of spatio-temporal correlations, are limited to NR = 20.
In the first part of the thesis we propose a modified version of the transfer matrix method, based on the parallel version of the Montecarlo algorithm, allowing us to go to NR = 100.
In the second part we present EnaS, a C++ library with a Graphical User Interface developed for neuroscientists. EnaS offers highly interactive tools that allow users to manage data, perform empirical statistics, modeling and visualizing results.
Finally, in a third part, we test our method on synthetic and real data sets. Real data set correspond to retina data provided by neuroscientists partners. Our non extensive analysis shows the advantages of considering spatio-temporal correlations for the analysis of retina spike trains, but it also outlines the limits of Maximum Entropy methods.
For more information about the software that I co-developed with my colleagues, please visit this page:
https://enas.inria.fr/
For more information about the publications, please visit this page:
https://scholar.google.fr/citations?user=L97ZODwAAAAJ
For the thesis, please visit this link:
https://www.theses.fr/178166669
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Comparative structure of adrenal gland in vertebrates
Computational Motor Control: State Space Models for Motor Adaptation (JAIST summer course)
1. Computational Motor
Control Summer School
03: State space models
for motor adaptation.
Hirokazu Tanaka
School of Information Science
Japan Institute of Science and Technology
2. State-space modeling of motor adaptation.
In this lecture, we will learn:
• Motor adaptation paradigms
• Continuous-time state-space models
• Discrete-time state-space models
• Controllability
• Observability
• State-space description for motor adaptation
• Multi-rate models
• Motor memory of errors
• Mirror reversal (non-error based learning)
3. Motor adaptation paradigms to dynamical perturbations: Force-field adaptation.
Shadmehr & Mussa-Ivaldi (1994) J Neurosci
Baseline (no field) Initial exposures
adaptation catch trials
4. Motor adaptation paradigms to kinematical perturbations: Visuomotor rotation.
Krakauer et al. (2000) J Neurosc; Krakauer (2009) Progress in Motor Control
5. Adaptation to prism displacements.
Martin et al. (1996) Brain ;Kitazawa et al. (1995) J Neurosci
6. Adaptation to prism displacements.
Kitazawa et al. (1995) J Neurosci
1 1n n ne e ke 1
1
1
n
in
i
e e k e
7. Continuous-time state-space models.
F ma mx
x v
Fv a
m
x
v
x
Newton’s equation of dynamics
0 1 0
0 0 1/
x x
F
v v m
x Ax Bu
x Ax Bu
State-space representation
A x B u
State-space vector
8. Discrete-time state-space models.
Discrete-time representation
k k t x x
1 ( 1)k
k k
k k k
k k
k t
t
t
t t
x x
x x
x Ax Bu
I A x B u
1
ˆ ˆ
k k k x Ax Bu
2
2ˆ
ˆ t
t
t t
te t
e
t
A
A
A I A
B BB
t
Δt 2Δt 3Δt (k-1)Δt kΔt (k+1)Δt0
k-1 k k+10 1 2 3
time (continuous)
time steps
(discrete)
9. Deterministic and stochastic state-space models.
1k k k
k k
x Ax Bu
z Cx
1
k
kk k
kk
k
w
v
x Ax Bu
z Cx
Deterministic Stochastic
XkXk-1 Xk+1
zk-1 zk zk+1
uk-1 uk uk+1
10. Linear time-variant and time-invariant state-space models.
1k k k
k k
k k
k
x x u
xCz
A B
Time-variant model
1k k k
k k
x x u
xCz
A B
Time-invariant model
Throughout these lectures, we will use linear time-invariant (LTI) models
for mathematical simplicity.
11. State-space models in an explicit component form.
1k k k x Ax Bu
k kz Cx
1, 1
2, 1
, 1
k
k
N k
x
x
x
1,
2,
,
k
k
N k
x
x
x
11 12 1
21 21
11 1
N
N
a a a
a a
a a
11
21
1
1
L
N NL
b b
b
b b
1
L
u
u
= +
1,
2,
,
k
k
N k
x
x
x
1
M
z
z
11 12 1
1 112
N
MM
c c c
c c c
=
Process equation
Measurement equation
N vector N×N matrix N vector N×L matrix L vector
M vector M×N matrix N vector
12. Controllability: the ability of driving a system into desired final state.
1k k k x Ax Bu
, , ,N L N N
k k
N L
x u A B
Controllability is the ability of external inputs {uk} to drive a state from any
initial condition to any final condition in a finite time. A state-space model
is controllable if the N×NL controllability matrix has full row rank:
2 1n
B AB A B A B
Sketch of proof:
0
1 1
2
2 2 1
1
1
0
2
N N N
N N
NN
N
N
N
x Ax Bu
A x ABu Bu
u
u
A x B AB A B
u
Kalman (1963) SIAM J Contr
13. Observability: determining hidden state from measurements.
k kz Cx
, ,N M N
k
M
k
x z C
Observability is the ability to determine a (latent) state from a sequence
of measurements {zk}. A state space model is called observable if the
MN×N observability has full rank N:
Kalman (1963) SIAM J Contr
1N
C
CA
C A
14. State-space models for dynamic (force-field) motor adaptation.
Thoroughman & Shadmehr (2000) Nature; Donchin et al. (2003) J Neurosci
1n n n
n n n
x Ax Bu
z Cx Du
15. State-space models for dynamic (force-field) motor adaptation.
Thoroughman & Shadmehr (2000) Nature; Donchin et al. (2003) J Neurosci
1n n n
n n n
x Ax Bu
z Cx Du
16. State-space models for kinematic (visual rotation) motor adaptation.
Tanaka et al. (2006) J Neurophysiol
T
1k k k k
k k k
z
z
x Ax BH
H x
17. Trial-by-trial generalization width reflects directional tuning width.
1
1i i N
i
N
g
g
g
r r r r Rg
T
k k k rR g
1 1 1
T
k k k k k k k k R R g R g gr g
Suppose that, for target direction θ, the motor output is a weighted sum of
population activity {gi(θ)} multiplied with preferred directions {ri}:
A gradient descent learning rule specifies the change of preferred directions
according to the movement error Δrk and the population activity {g(θk)} :
This change affects the motor output at the next trial as:
18. Two-rate model of motor adaptation: fast and slow learners.
Smith et al. (2006) PLoS Biol
1n n nu x Ax B
f ff f
1
s ss s
1
0
0
n n
n
n n
x xa b
u
x xa b
n nz Cx
f
f s1
1 1s
1
1 1 n
n n n
n
x
z x x
x
f s s f
,a a b b
There are two learners in the brain; the fast learner (x(f)) learns
quickly but forgets quickly, while slow learner (x(s)) learns slowly
but maintains its memory longer.
Motor output is a sum of the fast
and slow learners.
State vector consists of fast
(x(f)) and slow learners (x(s)).
19. The model explains savings, spontaneous recovery.
Smith et al. (2006) PLoS Biol
Savings Spontaneous recovery
20. The prediction of spontaneous recovery is confirmed in humans.
Smith et al. (2006) PLoS Biol
21. The slow process contributes to motor memory consolidation.
Joiner & Smith (2008) J Neurophysiol
The slow process, but not the fast process, contributes to motor memory consolidation.
22. Explicit (strategic) and implicit (error-based) learning.
Mazzoni & Krakauer (2006) J Neurosci
Strategy (aiming the adjacent target) cancels the “error” without
any adaptation!
25. What is “motor error?”: Aiming error and target error.
Taylor & Ivry (2011) PLoS Comp Biol; Taylor & Ivry (2014) Prog Brain Res
26. State-space model for strategic and error-based learning.
Taylor & Ivry (2011) PLoS Comp Biol; Taylor & Ivry (2014) Prog Brain Res
yn: target direction
rn: rotation angle
xn: adaptation variable
sn: strategy variable
yn
sn
sn-rn+xn
aiming
n n n nn n ne s s r x r x
target
n n n n n n nn ne y s r x y sx r
aiming
netarget
ne
27. State-space model for strategic and error-based learning.
Taylor & Ivry (2011) PLoS Comp Biol; Taylor & Ivry (2014) Prog Brain Res
yn
sn
sn-rn+xn
aiming
n n n nn n ne s s r x r x
target
n n n n n n nn ne y s r x y sx r
aiming
netarget
ne
aiming
targ
1
e
1
t
n n n
nn n
x ax be
s cs de
a=0.99, b=0.015,
c=0.999, d=0.022
28. Steepest descent learning rule for optimization.
Lecture 6, in Neural Networks for Machine Learning, Geoff Hinton
E
( 1) ( )n n E
w w
w
optimum
E
w
E
w
Descent learning rule:
RPROP: Adjustment of learning rate.
E
w
gradient
learning rate
1 1
29. Motor memory of experienced errors.
Herzfeld et al. (2014) Science
( ) ( ) ( )
( 1) ( ) ( ) ( )
ˆ
ˆ ˆ
n n n
n n n n
e y y
x ax e
( )
( )
( )
( )
( )
: perturbation
ˆ : estimated perturbation ("belief")
: sensory consequence
ˆ : predicted sensory consequence
: control signal
n
n
n
n
n
x
x
y
y
u
State-space model: memory of environments
Population-coding model: memory of errors
( ) ( )n n
i i
i
w g e
2
2
exp
2
i
i
e e
g e
( 1)
( 1) ( 1) ( 1) ( )
T ( 1) ( 1)
sgn
n
n n n n
n n
e
e e
e e
g
w w
g g error
activity
w1 w2 w3 wn
η
30. Motor memory of experienced errors.
Herzfeld et al. (2014) Science
31. Displacement and left-right reversal: Why so different?
Martin et al. (1996) Brain; Sekiyama et al. (2000) Nature
Displacement prism
… takes only few dozen trials. … takes a few weeks.
Left-right reversed prism
Day 3
Day 34
32. Mirror reversal: a distinct form of motor adaptation?
Taglen et al. (2014) J Neurosci; Lilicrap et al. (2013) Exp Brain Res
Movement number Movement number
Absoluteerror
Absoluteerror
Visual rotation Mirror reversal
33. Summary
• A state-space model consists of a process equation
(temporal transition) and an observation equation
(measurement).
• Humans are flexible to a novel environment, known as
motor adaptation, such as perturbations of force fields
and visual transformation.
• State-space modeling has been very successful in
describing trial-by-trial adaptation processes in humans.
34. References
• Thoroughman, K. A., & Shadmehr, R. (2000). Learning of action through adaptive combination of motor primitives. Nature,
407(6805), 742-747.
• Donchin, O., Francis, J. T., & Shadmehr, R. (2003). Quantifying generalization from trial-by-trial behavior of adaptive systems
that learn with basis functions: theory and experiments in human motor control. The Journal of Neuroscience, 23(27),
9032-9045.
• Tanaka, H., Sejnowski, T. J., & Krakauer, J. W. (2009). Adaptation to visuomotor rotation through interaction between
posterior parietal and motor cortical areas. Journal of Neurophysiology, 102(5), 2921-2932.
• Smith, M. A., Ghazizadeh, A., & Shadmehr, R. (2006). Interacting adaptive processes with different timescales underlie
short-term motor learning. PLoS Biol, 4(6), e179.
• Joiner, W. M., & Smith, M. A. (2008). Long-term retention explained by a model of short-term learning in the adaptive
control of reaching. Journal of Neurophysiology, 100(5), 2948-2955.
• Inoue, M., Uchimura, M., Karibe, A., O'Shea, J., Rossetti, Y., & Kitazawa, S. (2015). Three timescales in prism adaptation.
Journal of Neurophysiology, 113(1), 328-338.
• Mazzoni, P., & Krakauer, J. W. (2006). An implicit plan overrides an explicit strategy during visuomotor adaptation. The
Journal of Neuroscience, 26(14), 3642-3645.
• Taylor, J. A., & Ivry, R. B. (2011). Flexible cognitive strategies during motor learning. PLoS Comput Biol, 7(3), e1001096-
e1001096.
• Taylor, J. A., & Ivry, R. B. (2014). Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement
learning. Progress in Brain Research, 210, 217.
• Taylor, J. A., Krakauer, J. W., & Ivry, R. B. (2014). Explicit and implicit contributions to learning in a sensorimotor adaptation
task. The Journal of Neuroscience, 34(8), 3023-3032.
• Telgen, S., Parvin, D., & Diedrichsen, J. (2014). Telgen, S., Parvin, D., & Diedrichsen, J. (2014). Mirror reversal and visual
rotation are learned and consolidated via separate mechanisms: Recalibrating or learning de novo?. The Journal of
Neuroscience, 34(41), 13768-13779.?. The Journal of Neuroscience, 34(41), 13768-13779.
• Lillicrap, T. P., Moreno-Briseño, P., Diaz, R., Tweed, D. B., Troje, N. F., & Fernandez-Ruiz, J. (2013). Adapting to inversion of
the visual field: a new twist on an old problem. Experimental Brain Research, 228(3), 327-339.
• Ostry, D. J., Darainy, M., Mattar, A. A., Wong, J., & Gribble, P. L. (2010). Somatosensory plasticity and motor learning. The
Journal of Neuroscience, 30(15), 5384-5393.
35. Exercise
• Simulate the state-space model proposed by Taylor and
Ivry.
• Mirror reversal is different from most adaptation
paradigms in that learning from error worsens the
performance. Can we consider a state-space model for
mirror reversal?