NMA-Computational Neuroscience: July 5-23, 2021
Content: https://github.com/NeuromatchAcademy/course-content
• NMA-Deep Learning: Aug 2-20, 2021
Content: https://github.com/NeuromatchAcademy/course-content-dl

Applications for interactive students and teaching assistants (paid positions) are due May 7th 2021. Application Portal: https://portal.neuromatchacademy.org/

#Neuromatch

Machine learning in Python with scikit-learn

Ref. 41026
Duration: 8 weeks
Effort: 35 hours
Pace: ~4h15/week

Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!

#ML
#scikit_learn
#course

💰Doctoral student in Computational Neuroscience
#phd in School of Electrical Engineering and Computer Science at KTH

https://facultyvacancies.com/doctoral-position-in-computational-neuroscience,i19433.html

Neuromodulators are crucial for the brain function and while the effect of neuromodulation is relatively well studied, little is known about how neuromodulators affect the network activity dynamics. In this project we want to quantify the effects of neuromodulators on neuron/synapse properties and network structure and dynamics.

The doctoral student will be part of Dr. Kumar's research group. The research group uses computational and analytical methods to understand the dynamics and information processing in biological neuronal networks. The research in the Kumar lab is aimed at understading:

The role of oscillations and correlations in communication between different brain areas [e.g. see Hahn et al. Nature Rev. Neurosci. 2019]
Control of brain activity dynamics [e.g. see Vlachos et al. PloS Comp Bio 2016]
Interaction between neuron properties and network activity dynamics [Sahasranamam et al. Sci. Reports 2015, Spreizer et al. PloS Comp Bio 2010, Hahn et al. 2020]
Neural coding [e.g. Tauffer and Kumar 2020].

The research group is also developing mathematical models of brain diseases to understand the mechanisms underlying the emergence of disease related aberrant activity dynamics in brain diseases. More info.: https://www.kth.se/profile/arvindku/

Supervision: The doctoral student will be supervised by: Dr. Arvind Kumar

Vocal Tract Resonance

A "neutral" vowel is defined as a vowel produced by a vocal tract configuration that has uniform cross-sectional area along its entire length. Whilst no vowel articulation can actually meet this requirement accurately, the vowel in "heard" and some productions of schwa can approximate this configuration. For such vowels, and only for such vowels, the vocal tract can be treated mathematically as a single uniform tube closed at one end (the glottis) and open at the other (the lips) for the purposes of calculating the resonances of the vocal tract. See the topic "Standing Waves and Resonance" for further details.
For all other speech sounds the configuration of the vocal tract is much more complex. Figure 1 displays an x-ray derived medial section of a vocal tract during the production of a high central spread-lipped vowel.

[ Link ]

#speech
Follow: @theTuringMachine

What is this about?
Methodologies for neural signal processing in the case of natural scenes and sound perception.

How?
50% Lectures
25% Talks on case-studies
25% Hands-on tutorials


Where, when and how much?
• Online! 2-3 August.
• It is €20 for registration.


Participants?
• Researchers interested in studying natural speech or music perception with EEG/MEG/ECoG, but have no experience with ecologically-valid experiments.
• Researchers with experience in continuous sensory perception and tools such as the mTRF-Toolbox, who are interested in deepening their understanding and in expanding their set of tools.
Prerequisites?
• Some experience with neural signal processing (e.g., EEG, MEG, or ECoG).
• Some Matlab experience is required for the hands-on sessions.
• A practical interest in applying these notions.


[ link ]

#workshop
Follow: @theTuringMachine

The Canadian Computational Neuroscience Spotlight (CCNS) was created following the mass cancellations and postponements of traditional neuroscience conferences during the early stages of the COVID-19 pandemic, including two such meetings amongst the Canadian neuroscience community. The absence of these meetings presented an opportunity to create a brand-new, entirely virtual academic meeting that could take full advantage of the online setting. Given that traditionally-defined trainees and early-career researchers were arguably most impacted by the cancellation of the networking and learning opportunities that conferences present, CCNS was designed as a “trainee-focused” meeting, highlighted by tutorial talks beginning each session, panel discussions with both established and early-career scientists, and a spotlight on trainee presentations.

https://ccnsmeeting.ca/

#conference
Follow: @theTuringMachine

Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping or point-and-click typing with a computer cursor

[ Link ]

#paper
Follow:@theTuringMachine

A nice blog for topics in Neuroscience

[ link ]

Follow: @theTuringMachine

Analysis and interpretation of massively parallel electrophsiological data

This workshop showcases a few different approaches and analysis tools to exploit electrophysiological data.
Workshop at Neuroinformatics 2013 in Stockholm, Sweden
Workshop noscript: Analysis and Interpretation of Massively Parallel Electrophysiological Data
Probing the organization of interactions within and across neuronal populations is a promising approach to understanding the principles of brain processing. The rapidly advancing technical capabilities to record from hundreds of neurons in parallel open up new possibilities to disentangle the correlative structure within neuronal networks. However, the complexity of these massive data streams calls for novel, tractable analysis tools that exploit the parallel aspect of the data.

[ link ]

Follow: @theTuringMachine

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models

A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recur- rent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking....

#paper

Follow: @theTuringMachine

Have you ever wanted to quickly try some ideas popping up in your head using a Python Shell (REPL)? You might not want to open a new Jupyter Notebook to experiment with only a few lines of code.
But you might also be hesitant to use a classic Python shell since it doesn’t support auto-completion or docstring as Jupyter Notebook does. You also cannot fix the mistake in the code after hitting Enter.
What if you can turn your boring Python shell into a multi-functional shell like below?

Features:
🌱Syntax highlighting.
🌱Multiline editing (the up arrow works).
🌱Autocompletion
🌱Mouse support
🌱Support for color schemes.
🌱Support for bracketed paste
🌱Both Vi and Emacs key bindings.
🌱Support for double width (Chinese) characters.
🌱... and many other things.


Link
Open in an incognito tab!

Have you ever wondered how models of resting state fMRI really perform? Then this is the thread.

GitHub