10:30 to 10:40 |
Rajesh Gopakumar (ICTS, India) |
Welcome Remarks |
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10:40 to 11:20 |
Kazuyuki Aihara (University of Tokyo, Japan) |
DNB(Dynamical Network Biomarkers) Analysis and its Applications to Neuroscience In this talk, first I will review the method of our DNB(Dynamical Network Biomarkers) analysis that detects early warning signals of critical transitions in complex systems. Then, I explain possible applications of the DNB analysis to Neuroscience.
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11:20 to 12:00 |
Sarthak Chandra (Massachusetts Institute of Technology (MIT), USA) |
Reservoir computing in noisy real-world systems: network inference and dynamical skeletons A reservoir computer (RC) is a machine learning (ML) architecture that uses high-dimensional internal dynamics of the ML device to be able to perform time-series analysis of dynamical systems. One particular exciting application of reservoir computing has been in the inference of network structure underlying dynamical systems. However, previous work towards this effort (using RCs and otherwise) has largely only considered relatively simplistic problems, where most inference techniques result in a clear separability between true and false network edges. Here I will talk about the challenges associated with application of RCs towards neural data obtained from C. Elegans. In particular, we show a novel surrogate data construction method that allows for accurate link assessment even when the data does not lead to clear indications of connectivity.
When working with such real-world data, an important issue that arises is the presence of dynamical noise, i.e., continual stochastic perturbations to system dynamics. I will discuss how RCs can be used as effective tools to filter out dynamical noise, allowing for the reconstruction of a "dynamical skeleton", i.e., the underlying deterministic system that governs the dynamics of the data. We demonstrate that RCs
can perform effective filtering without any access to the clean signal and by training solely on the stochastically perturbed dynamical trajectories, even when the dynamical noise causes significant distortions to the system attractor.
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14:40 to 15:20 |
Klaus Lehnertz (University of Bonn, Germany) |
From nonlinear dynamics to complex networks: improving understanding of epilepsy Epilepsy is the most common chronic brain disease that affects approximately 50 million people worldwide. Epileptic seizures are the cardinal symptom of this multi-facetted disease and are usually characterized by an overly synchronized firing of neurons. Seizures cannot be controlled by any available therapy in about 25% of individuals. Although epilepsy is probably the oldest disease known to mankind, knowledge about mechanisms underlying generation, spread, and termination of the extreme event seizure in humans is still fragmentary. Over the last decades, an improved characterization of the spatial-temporal dynamics of the epileptic process could be achieved with concepts and methods from nonlinear dynamics, statistical physics, synchronization and network theory. In the first part of my talk, I shall provide a brief overview of the progress that has been made in the field: from preliminary descriptions of pre-seizure phenomena to implantable seizure prediction and prevention systems. The second part of my talk is devoted to more recent developments that promise an advanced characterization of complex brain dynamics, and we shall discuss necessary extensions to further advance the field.
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16:00 to 16:40 |
Bernardo Gabriel Mindlin (University of Buenos Aires, Argentina) |
Birdsong as a model for learned, complex behavior Birdsong is an exquisite, complex behavior which for approximately 40% of the known bird species, requires some degree of learning. The neural architecture needed for generating the instructions involved in birdsong generation is relatively well known, although the actual dynamics emerging from it is still being investigated. In this talk I will review experimental results as well as the main theoretical models being currently discussed. I will focus on the interaction between the nervous system and the rich and complex nonlinear biomechanics involved in birdsong production.
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17:00 to 18:00 |
Bernardo Gabriel Mindlin |
Breakout |
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