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How to Get Reeled into NeuroDuino – a Mathematician’s Guide (Part II)

Neuroduino
A NeuroDuino, Backyard Brains’ latest prototype

Hi there, it’s Natalia Díaz again with an update to my neuromathematical (yes, such a thing exists!) project. If you don’t remember me, I’m a student of Mathematical Engineering at the University of Santiago de Chile and I’m doing my internship here in BackyardBrains.

Since the last time we met (you and I, that is), BYB co-founder Tim Marzullo sent me some cool stuff. Not that it’s an exclusive privilege of interns, mind you! Anyone can find them in the “Muscle SpikerShield Bundle” kit.

With this bundle, you can do several very entertaining experiments such as seeing on your smartphone the action potentials that are produced when you move your muscles. You can also use the Muscle SpikerShield to control video games, robotics, and musical instruments.

It took a while for my board to pass customs, but it managed to arrive and we got to work right away. What I was most excited about was the arrival of new prototype from Backyard Brains – their very own customized Arduino board – codenamed NeuroDuino. (See above how handsome it is!)

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How to Get Reeled in By Neuroscience – a Mathematician’s Guide (Part I)

how to get reeled in by neuroscience

What lies at the intersection of math and medicine? Why many things, of course. Certainly more than could possibly fit into a blog post! But today, I am going to talk about the connection between brain function and numbers.

My name is Natalia Díaz and I am a student of Mathematical Engineering at the University of Santiago de Chile. Ever since I can remember, I have been tantalized by mathematics and medicine (especially brain function). The opportunity to mix both subjects finally arose when I entered college. That is how Neuroscience popped into my life!

To get my degree, I must complete my internship and my thesis. That’s how I started working with my mentors Dr. Patricio Rojas (University of Santiago) and Dr. Patricio Orio (University of Valparaíso). We are investigating, through numerical simulations, the effect of the electrical synapse topology between inhibitory neurons.

For this, we use a neural mathematical model of a mixed network of inhibitory and excitatory neurons of the cerebral cortex, and we study different types of topology (“all with all” or lattice style) of connection between inhibitory neurons characterizing the patterns obtained.

For example, the figure below shows a significant difference in network synchronization using different topologies. In the first yellowy-whitish graph, there is no gap junction (electrical synapse). The second shows a gap junction with a lattice topology, and in the last one we apply a gap junction with an all-to-all topology. To plot this, we use different values for the mean synaptic strength between excitatory neurons (mGsynE) and for the mean synaptic strength between inhibitory neurons (mGsynI). Lots of abbreviations, I know. But I promise they are fun!

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