Thread by @MPagkalos

I am super excited to share my first 🚨#tweeprint🚨 for my first @biorxivpreprint with @spiros1776 & @YiotaPoirazi on how to efficiently add dendrites to SNNs using Dendrify and @briansimulator:
biorxiv.org/content/10.110…
A thread 🧵 bellow👇 (1/11)

The problem🤔:
Decades of experimental and theoretical research show that neuronal dendrites do not just receive input from other neurons, but also perform complex functions semi-independently from the soma. (2/11)

Although dendritic operations greatly affect single-cell computations, their role in network functions (with a few notable exceptions @tyrell_turing @NeuroNaud) remains unexplored. (3/11)

This happens partly because neuron models with realistic dendritic properties are usually too complex and inefficient for large spiking network simulations 🫤. (4/11)

The solution🙏:
Develop simplified neuron models that reproduce essential dendritic mechanisms without sacrificing performance. And here is where Dendrify comes into play. (5/11)

Dendrify is a free, open-source #Python 🐍package that helps create simple neuron models that account for essential dendritic properties. It was born from our efforts to bridge the gap between detailed biophysical and integrate & fire models. (6/11)

Dendrify automatically generates and handles all the equations, parameters and custom events required by Brian to describe reduced compartmental models that reproduce many dendritic functions using only a few lines of code💻. (7/11)

Importantly, dendritic spikes are modelled in an event-driven fashion, without using the Hodgkin & Huxley equations, which makes the neuron models significantly more efficient and mathematically tractable. (8/11)

Using Dendrify, one can even create more complex models of specific cell types that accurately reproduce numerous experimentally observed neuronal and dendritic properties. (9/11)

All Dendrify's source code along with detailed tutorials and interactive Python notebooks will be available soon in the @dendritesgr GitHub page github.com/Poirazi-Lab, so stay tuned❗️❗️ (10/11)

I would like to thank:
- My amazing supervisors @spiros1776 and @YiotaPoirazi
- @MarcelStimberg and @neuralreckoning for their constructive feedback during the early stages of the project.
- The entire @briansimulator team for their awesome work.

I forgot to include (11/11) in the last tweet🤦‍♂️. (12/11)