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Upcoming seminars

The recycling of quantum correlations triggered by general quantum measurements

Pro.f Changliang Ren, Hunan Normal University

Today @ 12pm

How to write a successful research paper

Dr. Laura Sanchez Burgos, Springer NatureDr. Engi Hassaan, Springer Nature (Germany)

Jun 30 @ 8am

PINTO: Physics-informed transformer neural operator for learning generalized solutions of partial differential equations for any initial and boundary condition

Dr Deepak N. Subramani, Indian Institute of Science Bangalore

Jun 30 @ 12pm

How to write a successful research paper

Dr. Laura Sanchez Burgos, Springer NatureDr. Engi Hassaan, Springer Nature (Germany)

Jun 30 @ 4pm

Spaceport Location Planning in the Continental United States

Max Li, University of Michigan–Ann Arbor

Jul 1 @ 10am

Completion of the maternal continuity of care and associated factors among women who gave birth in the last 6 months in Kena District, southern Ethiopia: A community-based cross-sectional study, 2023

Addisalem Haile , Arba Minch University

Jul 1 @ 12pm

Recent seminars

Maximizing the impact of your expertise: using health system data to improve treatment retention for opioid use disorder

Alyssa Tilhou, Boston University

Jun 26

Arms race of physical defences: hooked trichomes of *Macaranga* ant-plants kill lycaenid caterpillars, but one specialist has a counter-defence

Mr Ritabrata Chowdhury, University of Cambridge

Jun 26

Innovative Environmental Solutions towards Green and Sustainable Artificial Intelligence

Prof. Sucharita Gopal, Boston University
Prof. Troy Tao, Harbin Institute of Technology (+3)

Jun 26

Nature Water Talks: Water pollution and advanced treatment processes

Professor Yang Yang, Clarkson University
Dr. Denise Mitrano, Nestle Research

10.52843/cassyni.1hlgcq

Applications of Nektar++ II

Junjie Ye, Imperial College London
Dr James Edgeley, United Kingdom Atomic Energy Authority (+1)

Jun 26

Meshing and Mesh Adaptation

Kaloyan Kirilov, Imperial College London and King's College London
Mr Jingtian Zhou, Imperial College London (+1)

Jun 26

Seminar highlights

Crocodylia
Bird
crown-group
Enantiornithes
Would dinosaurs
JURASSIC/CRETACEOUS
Ornithischa
Pterosauria
Sauropodomorpha
Archaeopteryx
have died out
Age (Ma)
Pygostylia
even without the
Paraves
Maniraptora
asteroid?
TRIASSIC
Aphanosauria
Lagerpetidae
Silesauridae
Theropoda
Dinosauria
Bird total-group
Benson (2018) Dinosaur macroevolution
and macroecology. Annu. Rev. Ecol. Evol.
Archosauria
Syst., 49, 378-408

$Types, Challenges & Qualitative Features of DDEs Some Types of DDEs (iii) Neutral Delay Differential Equations (NDDEs) take the form: /(t) = f(t,y(t),y(q(t,y(t)),y'((t,y(t) '(ß(t, where α(t, y(t)) < t, ß(t, y(t)) < t. (iv) DDEs with Ditributed Delay and Fractional Order Derivatives A general form IVP with fractional-order derivative: D⁷y(t) = = f(t,y(t),y(t -+),D¹y(t-), - ƒg(t,s,y(s))ds) Dγ = dty, dt? γ ε (0, 1]. Time-delay is incorporated to describe short-term memory of prior time, while fractional-order is included to explain long-term temporal memory. A V ⑈⑈ ⑈⑈ Fathalla Rihan (UAEU) DDEs with Glucose-Insulin Interactions NODYCON 2025 3 / 24$

$Presentation of the model An infinite plane Σ₀ := {xd = 0} is covered by a thin layer of thickness Eh of particles Pw of size Ero. Let Dw := Rᵈ⁻¹ X R⁺ \ For f E L² (D) with compact support, we consider UW solution of - - -k²uε = f = in D . Σ₀ models a multilayer object through a Robin bc with Re[γ] > 0 Uε + ikγuε = 0 on Σ₀. uw = 0 ondP®, Uw verifies the radiation condition Ero Σ₀ = for X ε Rᵈ⁻¹ X (L, +∞), Гᵏ the Green function for Helmholtz equation. 3/29$

3. Geometric Control
3.2 Model-predictive control (MPC) on a fiber bundle
Receding horizon: robust and suitable for complicated tasks, yet needs nonlinear optimization in each step.
Geometric control: fast and accurate computation on Lie algebra, and suitable on-line.
Comparison: conventional MPC: (3n+20)³=38³, geometric MPC: (2n+16)³=28³, speeds up 2.5 times.
State
Control input
Past
Future
Adopt
Zero-order
Target
holder
Measured
Predicted
Reject
tₖ₊N
tₖ₊N
Schematic of MPC on a receding horizon
2025/5/23

NODYCON 2025
About the stochastic process
It is a process with randomness where the events can only be predicted in terms of probability.
Examples includes the network traffic, biological systems etc.
Fourth International Nonlinear Dynamics Conference
For a continuous time process it is denoted by {Xt}t≥0 and for a discrete time process
it us denoted by {Xₜ₀, Xₜ₁, Xₜ₂
which are the sequence of random variables. For
a particular time, a random variable is defined over all the events of a stochastic
process.
In a stochastic process when time is fixed over events it is a random variable and
when an event is fixed over time it is a sample path.
24/06/2025

Active particle
Entity that consume energy and convert it into persistent motion
Biomolecules
Bacteria
Janus particle
Sperm cells
Robots
Animals
Nanometres
Micrometres
Millimetres
Metres
Cichos et al. (Nat. Mac. Intell. 2020)
Cells
Bacteria
Robots
Walking droplets
00:02 (min:sec)
0.00
411 in Hrowman mation
David Rogers (1950s)
Turner et al. (2000)
Paramanick et al. (2024)
Valani et al. (2019)

Speaker	Prof Rafael Yuste, Columbia University
Abstract	The design of neural circuits, with large numbers of neurons interconnected in vast networks, strongly suggest that they are specifically build to generate emergent functional properties (1). To explore this hypothesis, we have developed two-photon holographic methods to selective image and manipulate the activity of neuronal populations in 3D in vivo (2). Using them we find that groups of synchronous neurons (neuronal ensembles) dominate the evoked and spontaneous activity of mouse primary visual cortex (3). Ensembles can be optogenetically imprinted for several days and some of their neurons trigger the entire ensemble (4). By activating these pattern completion cells in ensembles involved in visual discrimination paradigms, we can bi-directionally alter behavioural choices (5). Our results demonstrate that ensembles are necessary and sufficient for visual perception and are consistent with the possibility that neuronal ensembles are the functional building blocks of cortical circuits. R. Yuste, From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487-497 (2015). L. Carrillo-Reid, W. Yang, J. E. Kang Miller, D. S. Peterka, R. Yuste, Imaging and Optically Manipulating Neuronal Ensembles. Annu Rev Biophys, 46: 271-293 (2017). J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid, R. Yuste, Visual stimuli recruit intrinsically generated cortical ensembles. Proceedings of the National Academy of Sciences of the United States of America 111, E4053-4061 (2014). L. Carrillo-Reid, W. Yang, Y. Bando, D. S. Peterka, R. Yuste, Imprinting and recalling cortical ensembles. Science 353, 691-694 (2016). L. Carrillo-Reid, S. Han, W. Yang, A. Akrouh, R. Yuste, (2019). Controlling visually-guided behaviour by holographic recalling of cortical ensembles. Cell 178, 447-457. DOI:https://doi.org/10.1016/j.cell.2019.05.045.
Tags	cortexoptogeneticstwo-photon imagingneural ensembles
Series	IST Neuroscience IST Austria's Neuroscience Seminar Series covers anything dealing with brain development, biophysics, systems, theoretical and molecular neuroscience. The series is partly given by senior speakers (streamed talks) and students (internal only), taking place every Tuesday during the academic year at 13:30 (CET).
Host	please see here for hosts, speakers & dates