the Turing Machine
https://www.youtube.com/watch?v=gSCa78TIldg
YouTube
PySINDy: A Python Library for Model Discovery
https://github.com/dynamicslab/pysindy
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data
by Brian de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
Journal…
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data
by Brian de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
Journal…
Virtual Nature Conference on Technologies for Neuroengineering
[ link ]
[ link ]
Nature
Virtual Nature Conference on Technologies for Neuroengineering
Join me at this event!
Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections
The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.
[ source ]
#article
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@theTuringMachine
The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.
[ source ]
#article
Follow:
@theTuringMachine
Frontiers
Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections
The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are…
Forwarded from Vivek
I’m planning to enrol for a Machine Learning course on Coursera by Andrew Ng. If anyone is interested in joining simultaneously to discuss and code and maybe collaborate for a project, please let me know. You can also ping me at <vivek.sharma1510@gmail.com>
Forwarded from Scientific Programming (Ziaee (he/him))
This website provides one of the most lightweight introductions to #machine_learning I have seen.
If I were to start learning #ML all over again, the structure and concepts covered in this resource would provide a good start.
got from Omarsar0
If I were to start learning #ML all over again, the structure and concepts covered in this resource would provide a good start.
got from Omarsar0
NEST Desktop
An interactive desktop application for Nest Simulator
Link:
https://nest-desktop.github.io
Follow: @theTuringMachine
An interactive desktop application for Nest Simulator
Link:
https://nest-desktop.github.io
Follow: @theTuringMachine
https://ebrains.eu/service/nest-desktop
NEST Desktop is a web-based application which provides a graphical user interface for NEST Simulator. With this easy-to-use tool, users can interactively construct neuronal networks and explore network dynamics.
NEST Desktop is a web-based application which provides a graphical user interface for NEST Simulator. With this easy-to-use tool, users can interactively construct neuronal networks and explore network dynamics.
EBRAINS
NEST Desktop
Construct neuronal networks and explore network dynamics with the NEST Simulator GUI
Forwarded from Scientific Programming (Ziaee (he/him))
NetPyNE
NetPyNE (Networks using Python and NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
NetPyNE (Networks using Python and NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.