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.
Forwarded from Scientific Programming (Ziaee (he/him))
A basic intro to stats for neuroscientists and all course materials are open here
Jupyter notebook slides & RISE with code to play around with to build an intuition for stats...
#neuroscience
#course
Jupyter notebook slides & RISE with code to play around with to build an intuition for stats...
#neuroscience
#course
GitHub
GitHub - BlohmLab/NSCI801-QuantNeuro: NSCI 801 (Queen's U) Quantitative Neuroscience course materials
NSCI 801 (Queen's U) Quantitative Neuroscience course materials - GitHub - BlohmLab/NSCI801-QuantNeuro: NSCI 801 (Queen's U) Quantitative Neuroscience course materials
Forwarded from Complex Systems Studies
Two open #PhD positions at EPFL:
❗️#PhD student position in computational neuroscience and modelling of salamander locomotor circuits
❗️#PhD student position in biorobotics and fluid dynamics
https://www.epfl.ch/labs/biorob/openings/
❗️#PhD student position in computational neuroscience and modelling of salamander locomotor circuits
❗️#PhD student position in biorobotics and fluid dynamics
https://www.epfl.ch/labs/biorob/openings/
EPFL
Open positions
-
Forwarded from Scientific Programming (Ziaee (he/him))
We have this awesome function called sublots_mosaic where you can pass us a layout id'ed on name
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
Connected Papers | Find and explore academic papers
A unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.
[ Link ]
Follow: @theTuringMachine
A unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work.
[ Link ]
Follow: @theTuringMachine
PhD Student position at the BCBL- Basque Center on Cognition Brain and Language (San Sebastián, Basque Country, Spain) www.bcbl.eu
• Position: PhD student
• Researcher Profile: First Stage Researcher (R1- up to the point of PhD)
• Number of vacancies: 1
• Project: Spanish Ministry of Economy and Competitiveness through the Plan Nacional RTI2018 093547 B I00 (LANGCONN)
• Location: Spain > Donostia-San Sebastian
• Research Field: Neuroscience > Cognition and Language
• Type of contract/Duration of Contract : Temporary > 4 years
• Job Status: Full-time
• Hours per week: 35
• Starting date: 01/07/2021 (flexible)
• Application deadline: 31/05/2021
• Information about the project: The Basque Center on Cognition Brain and Language – BCBL- (Donostia-San Sebastián, Basque Country, Spain)
#positions
Follow: @theTuringMachine
• Position: PhD student
• Researcher Profile: First Stage Researcher (R1- up to the point of PhD)
• Number of vacancies: 1
• Project: Spanish Ministry of Economy and Competitiveness through the Plan Nacional RTI2018 093547 B I00 (LANGCONN)
• Location: Spain > Donostia-San Sebastian
• Research Field: Neuroscience > Cognition and Language
• Type of contract/Duration of Contract : Temporary > 4 years
• Job Status: Full-time
• Hours per week: 35
• Starting date: 01/07/2021 (flexible)
• Application deadline: 31/05/2021
• Information about the project: The Basque Center on Cognition Brain and Language – BCBL- (Donostia-San Sebastián, Basque Country, Spain)
#positions
Follow: @theTuringMachine
Forwarded from Scientific Programming (Ziaee (he/him))
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
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
GitHub
GitHub - NeuromatchAcademy/course-content: NMA Computational Neuroscience course
NMA Computational Neuroscience course. Contribute to NeuromatchAcademy/course-content development by creating an account on GitHub.
Forwarded from Scientific Programming (Ziaee (he/him))
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
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
FUN MOOC
Machine learning in Python with scikit-learn
Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!