SimFin is a database with financial data such as Income Statements, Balance Sheets and Cash Flow Statements, along with a simple Python API for downloading and using the data. These tutorials show how to use the SimFin API and data.
Link: SimFin
Github Repo: GitHub

#DataScience #DataAnalysis
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Computational neuroscience resources
On this page is a list of resources for learning computational neuroscience that are freely available online.
#resources
#computational_neuroscience
@scientific_programming

ScienceAlert - Latest
Scientists Just Created Artificial Red Blood Cells That May Be Even Better Than The Real Thing

➖ @sciencetoall ➖

The Medical Imaging with Deep Learning #MIDL conference will be held free this year.

Registration is still required though.

Registration link: https://2020.midl.io/registration.html

🆔 @ApplyClub

Another Cohen playlist, this time for Statics and Machine learning.
Link: Cohen

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Four ways to quantify synchrony between time series data

Link: TowardsDataScience
#DataAnalysis
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Related Codes

Neurons are cells — small bodies of mostly water, ions, amino acids and proteins with remarkable electrochemical properties. They are the primary functional units of the brain. Our mental experiences — our perceptions, memories, and thoughts — are the result of the ebb and flow of salts across neural bi-lipid membranes and the synaptic transmissions between neurons. Understanding neurons and neural computation can help illuminate how our rich mental experiences are constructed and represented, the underlying principles of our behavior and decision making, as well as provide biological inspiration for new ways to process information and for artificial intelligence.

Link: Website
#Neuroscience
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Modern Research Data Management for Neuroscience

Upload your data to private repositories.
Synchronise across devices.
Securely access your data from anywhere.

Link: G-Node
#Neuroscience #Programming
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https://libguides.usc.edu/writingguide/CARS

#tips
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The geometry of abstraction in artificial and biological neural networks

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. We characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility

Link: Crowdcast
#Neuroscience #events
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Speed trick
Link: YouTube
#spare_time
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The repository of the course in GitHub.

There's a recent @Radiolab mini-series that I highly recommend, especially to scientists (regardless of your area of study). The series is called "G" and focuses on intelligence research. I'll preview the episodes then explain why I think you (scientists) should listen.

Tweets from: NeilLewisJr

The 1st episode is noscriptd "The Miseducation of Larry P" and chronicles the history of IQ testing in education & how poorly normed tests ended up...well...screwing over minority and low SES students. If you care about equity, this one's a tearjerker.

Link: wnystudios
#spare_time
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https://pedramardakani.wordpress.com/android-ninja-1

@ashoftegah #blog #tips

I am an applied mathematician in the Department of Mathematics at the Vrije Universiteit Amsterdam, and a member of Inria's MathNeuro Team.
I work on spatio-temporal patterns in biological and physical models, which I study using numerical and analytical methods.
My research interests include: numerical bifurcation analysis, mathematical neuroscience, multi-scale dynamics, numerical methods, localised states, coherent structures, nonlinear media, reaction-diffusion systems, and nonlocal models.

Page: Daniele Avitabile

#Scientists
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