Forwarded from Shervin Safavi
Hi everyone, following up on my previous message, now I have an opening for a computational neuroscience PhD position in my lab. You can find more details in the CMC lab website (which is still under construction, but probably have some helpful information):
https://shervinsafavi.github.io/cmclab/join/bne_phd_202310/
If you are interested in applying for this position, please follow the instructions in the official call from the university, i.e. upload a cover letter, a brief denoscription of your research interests, and your CV in the university portal.
If you have any questions, please let me know. If you are interested, but cannot meet the deadline, please let me know as soon as possible and we’ll see if we can figure out something.
https://shervinsafavi.github.io/cmclab/join/bne_phd_202310/
If you are interested in applying for this position, please follow the instructions in the official call from the university, i.e. upload a cover letter, a brief denoscription of your research interests, and your CV in the university portal.
If you have any questions, please let me know. If you are interested, but cannot meet the deadline, please let me know as soon as possible and we’ll see if we can figure out something.
shervinsafavi.github.io
PhD project on neural events
Computational Machinery of Cognition. The overarching research theme of our lab is understanding the computational machinery of cognitive processes.
Calculus Made Easy is a free book on calculus originally published in 1910 by Silvanus P. Thompson, considered a classic and elegant introduction to the subject.
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More: @theTuringMachine
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More: @theTuringMachine
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Calculus and Applications
Free online book on calculus by Vahid Shahrezaei
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More: @theTuringMachine
Free online book on calculus by Vahid Shahrezaei
[ link ]
More: @theTuringMachine
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Forwarded from Brain science journal club (Mojtaba Madadi Asl)
Neuronal Modeling Workshop
Part I: Simplified point neurons
This in-person workshop aims to provide participants with an introduction to various topics related to simulating a network of point neurons. The workshop is designed to be interactive, allowing participants to work on their own computers while receiving guidance from experienced instructors.
Lecturers:
• Alireza Valizadeh
• Mojtaba Madadi Asl
• Mozhgan Khanjanianpak
• Saeed Taghavi
Schedule: February 21-22, 2024
Venue: Pasargad Institute for Advanced Innovative Solutions (PIAIS), Khatam University, Tehran, Iran
Registration deadline: February 10, 2024
Registration form: www.b2n.ir/NMW-form
Website: www.b2n.ir/NMW-web
For details, please inquire mojtabamadadi7@gmail.com
For technical support, contact saeed.taghavi.v@gmail.com
Part I: Simplified point neurons
This in-person workshop aims to provide participants with an introduction to various topics related to simulating a network of point neurons. The workshop is designed to be interactive, allowing participants to work on their own computers while receiving guidance from experienced instructors.
Lecturers:
• Alireza Valizadeh
• Mojtaba Madadi Asl
• Mozhgan Khanjanianpak
• Saeed Taghavi
Schedule: February 21-22, 2024
Venue: Pasargad Institute for Advanced Innovative Solutions (PIAIS), Khatam University, Tehran, Iran
Registration deadline: February 10, 2024
Registration form: www.b2n.ir/NMW-form
Website: www.b2n.ir/NMW-web
For details, please inquire mojtabamadadi7@gmail.com
For technical support, contact saeed.taghavi.v@gmail.com
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Forwarded from Complex Systems Studies
"Ambitions for theory in the physics of life" (by William Bialek): https://arxiv.org/abs/2401.15538
[note: Lectures at the 2023 Les Houches Summer School, Theoretical Biophysics]
Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic denoscriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each case, optimization makes detailed, largely parameter-free predictions that connect quantitatively with experiment
[note: Lectures at the 2023 Les Houches Summer School, Theoretical Biophysics]
Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic denoscriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each case, optimization makes detailed, largely parameter-free predictions that connect quantitatively with experiment
arXiv.org
Ambitions for theory in the physics of life
Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic denoscriptions of living systems, however, things get complicated....
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Forwarded from Complex Systems Studies
#Coxeter Lecture Series will be delivered by 2022 Fields Medallist Hugo Duminil-Copin.
Do NOT miss an opportunity to hear his talks in-person or online!
Register: http://www.fields.utoronto.ca/activities/23-24/Duminil-Copin
Do NOT miss an opportunity to hear his talks in-person or online!
Register: http://www.fields.utoronto.ca/activities/23-24/Duminil-Copin
Summer School | Advanced tools for data analysis in neuroscience
Advanced tools for data analysis in neuroscience
Research discoveries are increasingly dependent on the development of new tools and technologies, as well as on the ability to process, manage and analyze the large amounts of data collected with these tools....
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More: @theTuringMachine
Advanced tools for data analysis in neuroscience
Research discoveries are increasingly dependent on the development of new tools and technologies, as well as on the ability to process, manage and analyze the large amounts of data collected with these tools....
[ link ]
More: @theTuringMachine
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IPython Interactive Computing and Visualization Cookbook, Second Edition (2018), by Cyrille Rossant, contains over 100 hands-on recipes on high-performance numerical computing and data science in the Jupyter Notebook.
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More: @theTuringMachine
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More: @theTuringMachine
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Forwarded from Scientific Programming (SCI_dev(he/him))
Datasets for machine learning typically contain a large number of features, but such high-dimensional feature spaces are not always helpful.
In general, all the features are not equally important and there are certain features that account for a large percentage of variance in the dataset. Dimensionality reduction algorithms aim to reduce the dimension of the feature space to a fraction of the original number of dimensions. In doing so, the features with high variance are still retained—but are in the transformed feature space. And principal component analysis (PCA) is one of the most popular dimensionality reduction algorithms.
Here's a simple example in Python demonstrating PCA for dimensionality reduction before training a scikit-learn classifier
Github
You may also need to read more about PCA here.
In general, all the features are not equally important and there are certain features that account for a large percentage of variance in the dataset. Dimensionality reduction algorithms aim to reduce the dimension of the feature space to a fraction of the original number of dimensions. In doing so, the features with high variance are still retained—but are in the transformed feature space. And principal component analysis (PCA) is one of the most popular dimensionality reduction algorithms.
Here's a simple example in Python demonstrating PCA for dimensionality reduction before training a scikit-learn classifier
Github
You may also need to read more about PCA here.
GitHub
workshop_ML/pca/classify_use_pca.ipynb at main · Ziaeemehr/workshop_ML
Machine learning tutorials and examples. Contribute to Ziaeemehr/workshop_ML development by creating an account on GitHub.
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
How can we tell whether two neural networks utilize the same internal processes for a particular computation?
[ Talk ] [ paper ][ git ]
#Cosyne2024
More: @theTuringMachine
How can we tell whether two neural networks utilize the same internal processes for a particular computation?
[ Talk ] [ paper ][ git ]
#Cosyne2024
More: @theTuringMachine
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Deep neural networks reveal context-sensitive speech encoding in single neurons of human cortex.
Shailee Jain, Matthew K. Leonard, Edward F. Chang
[ Talk ]
#Cosyne2024
More: @theTuringMachine
Shailee Jain, Matthew K. Leonard, Edward F. Chang
[ Talk ]
#Cosyne2024
More: @theTuringMachine
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Forwarded from Scientific Programming (SCI_dev(he/him))
Post-doctoral in Marseille.
Project Title: Higher-order interactions in human brain networks supporting causal learning
Project Title: Higher-order interactions in human brain networks supporting causal learning
Forwarded from the last neural cell (Aleksandr Kovalev)
Brain-To-Text Competition 2024
This is the most fascinating BCI competition yet, organized by Stanford. Everyone has one month to develop the world's best brain-to-speech decoder!
Task: Predict attempted speech from brain activity.
Deadline: June 2, 2024
Dataset: They've recorded 12,100 sentences from a patient who can no longer speak intelligibly due to amyotrophic lateral sclerosis (ALS).
Just letting you know we're jumping into this challenge!
Together with @Altime and @kovalev_alvi, we're going to create something interesting.
Like this post if you want to follow our updates❤️
This is the most fascinating BCI competition yet, organized by Stanford. Everyone has one month to develop the world's best brain-to-speech decoder!
Task: Predict attempted speech from brain activity.
Deadline: June 2, 2024
Dataset: They've recorded 12,100 sentences from a patient who can no longer speak intelligibly due to amyotrophic lateral sclerosis (ALS).
For each sentence, we provide the trannoscript of what the participant was attempting to say, along with the corresponding time series of neural spiking activity recorded from 256 microelectrodes in speech-related areas of cortex.
Just letting you know we're jumping into this challenge!
Together with @Altime and @kovalev_alvi, we're going to create something interesting.
Like this post if you want to follow our updates❤️
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