Dynamical study of a mean-field model of neural network activity driven by biophysical ion exchange mechanisms
Project denoscription. In neuroscience, the question of scales is central, ranging from the molecule to the whole brain. In theoretical and computational neuroscience, it is possible to model these different scales and to build the link between them ....
Main objectives. 1. To identify the different dynamical regimes of this model, in particular in a parameter configuration corresponding to a healthy state; 2. To determine the different timescales involved in this system; (and possibly) 3. To start a bifurcation study for identified biophysical parameters of interest.
AMU Faculty of Medicine, Marseille, France [ website ]
Follow: @theTuringMachine
Project denoscription. In neuroscience, the question of scales is central, ranging from the molecule to the whole brain. In theoretical and computational neuroscience, it is possible to model these different scales and to build the link between them ....
Main objectives. 1. To identify the different dynamical regimes of this model, in particular in a parameter configuration corresponding to a healthy state; 2. To determine the different timescales involved in this system; (and possibly) 3. To start a bifurcation study for identified biophysical parameters of interest.
AMU Faculty of Medicine, Marseille, France [ website ]
Follow: @theTuringMachine
Applications are invited for three PhD student positions at the University of Bern. The positions are funded by a grant from the Swiss National Science Foundation which is ennoscriptd “Why Spikes?”.
This project aims at answering an almost 100 year old question in Neuroscience: “What are spikes good for?”. Indeed, since the discovery of action potentials by Lord Adrian in 1926, it has remained largely unknown what the benefits of spiking neurons are, when compared to analog neurons. Traditionally, it has been argued that spikes are good for long-distance communication or for temporally precise computation. However, there is no systematic study that quantitatively compares the communication as well as the computational benefits of spiking neuron w.r.t analog neurons. The aim of the project is to systematically quantify the benefits of spiking at various levels.
The PhD students and post-doc will be supervised by Prof. Jean-Pascal Pfister (Theoretical Neuroscience Group, Department of Physiology, University of Bern).
The PhD candidates (resp. post-doc candidate) should hold a Master (resp. PhD) degree in Physics, Mathematics, Computer Science, Computational Neuroscience, Neuroscience or a related field. She/he should have keen interests in developing theories that can be tested experimentally. Preference will be given to candidates with strong mathematical and programming skills. Expertise in stochastic dynamical systems, point processes, control theory and nonlinear Bayesian filtering will be a plus.
The applicant should submit a CV (including contacts of two referees), a statement of research interests, marks obtained for the Master to Jean-Pascal Pfister (jeanpascal.pfister@unibe.ch).
ThThe position is offered for a period of three years and can be extended. Deadline for application is the 31st of January 2023 or until the position is filled. Salary scale is provided by the Swiss National Science Foundation. (http://www.snf.ch/SiteCollectionDocuments/allg_doktorierende_e.pdf).
More: @theTuringMachine
This project aims at answering an almost 100 year old question in Neuroscience: “What are spikes good for?”. Indeed, since the discovery of action potentials by Lord Adrian in 1926, it has remained largely unknown what the benefits of spiking neurons are, when compared to analog neurons. Traditionally, it has been argued that spikes are good for long-distance communication or for temporally precise computation. However, there is no systematic study that quantitatively compares the communication as well as the computational benefits of spiking neuron w.r.t analog neurons. The aim of the project is to systematically quantify the benefits of spiking at various levels.
The PhD students and post-doc will be supervised by Prof. Jean-Pascal Pfister (Theoretical Neuroscience Group, Department of Physiology, University of Bern).
The PhD candidates (resp. post-doc candidate) should hold a Master (resp. PhD) degree in Physics, Mathematics, Computer Science, Computational Neuroscience, Neuroscience or a related field. She/he should have keen interests in developing theories that can be tested experimentally. Preference will be given to candidates with strong mathematical and programming skills. Expertise in stochastic dynamical systems, point processes, control theory and nonlinear Bayesian filtering will be a plus.
The applicant should submit a CV (including contacts of two referees), a statement of research interests, marks obtained for the Master to Jean-Pascal Pfister (jeanpascal.pfister@unibe.ch).
ThThe position is offered for a period of three years and can be extended. Deadline for application is the 31st of January 2023 or until the position is filled. Salary scale is provided by the Swiss National Science Foundation. (http://www.snf.ch/SiteCollectionDocuments/allg_doktorierende_e.pdf).
More: @theTuringMachine
Forwarded from Complex Systems Studies
Interested in scientific research in the field of #ComplexSystems?
IFISC announces the SURF@IFISC2023 summer research grants for undergraduates with the aim of introducing student fellows to cutting-edge research.
Deadline: March 26th
🔗 https://ifisc.uib-csic.es/en/about-ifisc/join-us/surf/surf-2023/
IFISC announces the SURF@IFISC2023 summer research grants for undergraduates with the aim of introducing student fellows to cutting-edge research.
Deadline: March 26th
🔗 https://ifisc.uib-csic.es/en/about-ifisc/join-us/surf/surf-2023/
the Turing Machine
Mathematical Methods in Computational Neuroscience Computational Neuroscience and Inference from data are disciplines that extensively use tools from Mathematics and Physics to understand the behavior of model neuronal networks and analyze data from real…
Mathematical Methods in Computational Neuroscience
Don't miss out this year's school.
The deadline for application is April 30th at 23:59 AoE. Results will be communicated to applicants by mid May.
[ more ]
Follow: @theTuringMachine
Don't miss out this year's school.
The deadline for application is April 30th at 23:59 AoE. Results will be communicated to applicants by mid May.
[ more ]
Follow: @theTuringMachine
Mathematical Methods
Mathematical Methods in Computational Neuroscience
Summer school in Eresfjord, Norway (July 8th - 26th, 2024)
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On the difficulty of learning chaotic dynamics with RNNs
NeurIPS poster session
Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradients backpropagated in time tend to saturate or diverge during training. This is known as the exploding and vanishing gradient problem. Previous solutions to this issue either built on rather complicated, purpose-engineered architectures with gated memory buffers, or - more recently - imposed constraints that ensure convergence to a fixed point or restrict (the eigenspectrum of) the recurrence matrix. Such constraints, however, convey severe limitations on the expressivity of the RNN. Essential intrinsic dynamics such as multistability or chaos are disabled. This is inherently at disaccord with the chaotic nature of many, if not most, time series encountered in nature and society..
[ link ][ Poster ][ Paper ]
More: @theTuringMachine
NeurIPS poster session
Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradients backpropagated in time tend to saturate or diverge during training. This is known as the exploding and vanishing gradient problem. Previous solutions to this issue either built on rather complicated, purpose-engineered architectures with gated memory buffers, or - more recently - imposed constraints that ensure convergence to a fixed point or restrict (the eigenspectrum of) the recurrence matrix. Such constraints, however, convey severe limitations on the expressivity of the RNN. Essential intrinsic dynamics such as multistability or chaos are disabled. This is inherently at disaccord with the chaotic nature of many, if not most, time series encountered in nature and society..
[ link ][ Poster ][ Paper ]
More: @theTuringMachine
2023 BRAINART COMPETITION
THE MULTIFACETED BRAIN: ADAPTATION AND DIVERSITY
This year we are holding a BrainArt Competition under the theme "The Multifaceted Brain: Adaptation and Diversity". We are now accepting submissions for the BrainArt Competition! Please use the following form to submit your art. If you do not have access to the form, you may send your submission to ohbm.brainart (at) gmail.com. [ link ]
Follow for more: @theTuringMachine
THE MULTIFACETED BRAIN: ADAPTATION AND DIVERSITY
This year we are holding a BrainArt Competition under the theme "The Multifaceted Brain: Adaptation and Diversity". We are now accepting submissions for the BrainArt Competition! Please use the following form to submit your art. If you do not have access to the form, you may send your submission to ohbm.brainart (at) gmail.com. [ link ]
Follow for more: @theTuringMachine
Geometric constraints on human brain function
The anatomy of the brain necessarily constrains its function, but precisely how remains unclear.
predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity, suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity... [ more ]
#article
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The anatomy of the brain necessarily constrains its function, but precisely how remains unclear.
predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity, suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity... [ more ]
#article
Follow: @theTuringMachine
Forwarded from Scientific Programming (Ziaee (he/him))
25_Awesome_Python_Scripts.pdf
171.4 KB
A Collection of 25 Awesome Python Scripts (mini projects)
#Python
#Python
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Forwarded from Scientific Programming (Ziaee (he/him))
COMPUTATIONAL PSYCHIATRY COURSE ZURICH
This course is organized by the Translational Neuromodeling Unit (TNU), University of Zurich & ETH Zurich and is designed to provide MSc and PhD students, scientists clinicians and anyone interested in Computational Psychiatry with the necessary toolkit to master challenges in computational psychiatry research.
Pre-requisites: Some background knowledge in neuroscience, neuroimaging, (Bayesian) statistics & probability theory, programming and machine learning is expected. If you lack this background, it is recommended that you prepare for this course.
https://www.translationalneuromodeling.org/cpcourse/
Preparation Resources
Lectures
Lecture Recordings
Tutorials
Reading List
This course is organized by the Translational Neuromodeling Unit (TNU), University of Zurich & ETH Zurich and is designed to provide MSc and PhD students, scientists clinicians and anyone interested in Computational Psychiatry with the necessary toolkit to master challenges in computational psychiatry research.
Pre-requisites: Some background knowledge in neuroscience, neuroimaging, (Bayesian) statistics & probability theory, programming and machine learning is expected. If you lack this background, it is recommended that you prepare for this course.
https://www.translationalneuromodeling.org/cpcourse/
Preparation Resources
Lectures
Lecture Recordings
Tutorials
Reading List
GitHub
GitHub - computational-psychiatry-course/precourse-preparation
Contribute to computational-psychiatry-course/precourse-preparation development by creating an account on GitHub.
A Brain-Wide Map of Neural Activity during Complex Behaviour
Abstract:
... Here, we report a comprehensive set of recordings from 115 mice in 11 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encodes key task variables....
[ link ]
More: @theTuringMachine
Abstract:
... Here, we report a comprehensive set of recordings from 115 mice in 11 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encodes key task variables....
[ link ]
More: @theTuringMachine
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Looking for job opportunities in Neuroscience?
check this out!
https://www.world-wide.org/jobs
More: @theTuringMachine
check this out!
https://www.world-wide.org/jobs
More: @theTuringMachine
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CNS-2023
A Guide to Reconstructing Dynamical Systems from Neural Measurements Using Recurrent Neural Networks.
[ link ]
more: @theTuringMachine
A Guide to Reconstructing Dynamical Systems from Neural Measurements Using Recurrent Neural Networks.
[ link ]
more: @theTuringMachine
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Forwarded from Scientific Programming (Ziaee (he/him))
Complete ML Refresher (1).pdf
1.3 MB
Machine Learning refresher.
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Open Datasets in Electrophysiology
This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data.
Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. Be sure to check the license and/or usage agreements for any datasets you access....
[ link ]
More: @theTuringMachine
This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data.
Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. Be sure to check the license and/or usage agreements for any datasets you access....
[ link ]
More: @theTuringMachine
GitHub
GitHub - openlists/ElectrophysiologyData: A list of openly available datasets in (mostly human) electrophysiology.
A list of openly available datasets in (mostly human) electrophysiology. - GitHub - openlists/ElectrophysiologyData: A list of openly available datasets in (mostly human) electrophysiology.
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the Turing Machine
Open Datasets in Electrophysiology This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration…
These resources include original and publicly available datasets that were standardised according to the CND data structure, as well as original analysis noscripts and links to publicly available toolboxes for the analysis of continuous-event neural data. Note that each dataset should be used according to its own license and should be referenced as indicated by the authors in their original submission.
[ link ]
Follow: @theTuringMachine
[ link ]
Follow: @theTuringMachine
cnspworkshop.net
CNSP Resources
POSTDOCTORAL FELLOW POSITION IN EPILEPSY RESEARCH
The EEG and Epilepsy Unit of the University Hospitals and Faculty of medicine of the University of Geneva are offering a
POSTDOCTORAL FELLOW POSITION IN EPILEPSY RESEARCH
Start date: March 1st 2024 or mutual agreement
Review starts on September 30th 2023 and continues until the position is filled.
We are looking for a postdoctoral fellow funded jointly by two projects of the Swiss National Science Foundation (SNSF): (1) “Network Analysis to Predict Eligibility for Epilepsy Surgery” and (2) “Precision mapping of electrical brain network dynamics with application to epilepsy”.
More: [ link ]
Follow: @theTuringMachine
The EEG and Epilepsy Unit of the University Hospitals and Faculty of medicine of the University of Geneva are offering a
POSTDOCTORAL FELLOW POSITION IN EPILEPSY RESEARCH
Start date: March 1st 2024 or mutual agreement
Review starts on September 30th 2023 and continues until the position is filled.
We are looking for a postdoctoral fellow funded jointly by two projects of the Swiss National Science Foundation (SNSF): (1) “Network Analysis to Predict Eligibility for Epilepsy Surgery” and (2) “Precision mapping of electrical brain network dynamics with application to epilepsy”.
More: [ link ]
Follow: @theTuringMachine
EURAXESS
POSTDOCTORAL FELLOW POSITION IN EPILEPSY RESEARCH | EURAXESS
The EEG and Epilepsy Unit of the University Hospitals and Faculty of medicine of the University of Geneva are offering a POSTDOCTORAL FELLOW POSITION IN EPILEPSY RESEARCH
Forwarded from Shervin Safavi
The Computational Machinery of Cognition (CMC) Lab (PI: Shervin Safavi), will be established at the Technische Universität Dresden in Fall 2023. We will be building the team in the next few months (starting with openings for Ph.D. students and master students).
Research theme:
We are interested in understanding the computational machinery of cognitive processes (in particular inference and decision processes). We do:
* normative and biophysical modeling of cognitive functions (starting with perceptual decisions);
* testing these models with neural and behavioral data (in collaboration with experimental labs)
* developing methods for multi- and cross-scale analysis of neural data to better capture the neural markers of cognitive processes.
If the research of our lab resonates with yours, and you like to get future announcements (e.g., call for PhD positions). please fill out this form (https://docs.google.com/forms/d/e/1FAIpQLSd8V5Mu8d-JwZXjs_Ck5toLl0IBg5pTpTrZs4A_QW-71pi13A/viewform?usp=sf_link)
Also, please feel free to get in touch (shervin.safavi@tuebingen.mpg.de) if you like to know more and/or to collaborate. It would also be great if pass it to your colleagues/friends who might be interested.
Research theme:
We are interested in understanding the computational machinery of cognitive processes (in particular inference and decision processes). We do:
* normative and biophysical modeling of cognitive functions (starting with perceptual decisions);
* testing these models with neural and behavioral data (in collaboration with experimental labs)
* developing methods for multi- and cross-scale analysis of neural data to better capture the neural markers of cognitive processes.
If the research of our lab resonates with yours, and you like to get future announcements (e.g., call for PhD positions). please fill out this form (https://docs.google.com/forms/d/e/1FAIpQLSd8V5Mu8d-JwZXjs_Ck5toLl0IBg5pTpTrZs4A_QW-71pi13A/viewform?usp=sf_link)
Also, please feel free to get in touch (shervin.safavi@tuebingen.mpg.de) if you like to know more and/or to collaborate. It would also be great if pass it to your colleagues/friends who might be interested.
Google Docs
CMC Lab: Interest mailing-list
The Computational Machinery of Cognition (CMC) Lab (PI: Shervin Safavi), will be established at the Dresden University of Technology (Technische Universität Dresden or TU Dresden) in Autumn 2023. We will be building the team in the next few months (starting…
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The International Brain Laboratory will release all data sets within 12 months of collection, or upon acceptance for publication of an associated manunoscript, whichever comes first.
[ link ]
More: @theTuringMachine
[ link ]
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A freely available short course on neuroscience for people with a machine learning background. Designed by Dan Goodman and Marcus Ghosh.
[ link ]
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[ link ]
More: @theTuringMachine
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