What are the design principles of neural connectomes?

We show that a small # of biophysical features shape the structure and function of connecotmes in zebrafish, mice, & c. elegans.
lead by @_adam_haber, w/@RainerFriedri12 & @AdrianAWanner

doi.org/10.1101/2023.0…
1/n Image
The map of synaptic connectivity among neurons shapes the computations that neural circuits perform. Identifying the design principles of connectomes is fundamental for understanding brain development and architecture, neural computations, learning, and behavior... 2/n
We therefore learned probabilistic generative models for connectomes of the olfactory bulb of zebrafish, the mouse visual cortex, and of C. elegans .. 3/n Image
We find that models that rely on a surprisingly small number of simple biological and physical features are highly accurate in replicating the measured connectomes (features here shown for zebrafish).. 4/n Image
Specifically, in all species we accurately predict the existence of individual synapses, as well as their synaptic strength.. 5/n Image
We also accurately predict, in all cases, the distributions of synaptic indegree and outdegree of the neurons, the frequency of sub-network motifs (without any special features), and more.. 6/n Image
We then simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the computation that the real circuit performs in response to olfactory cues.. 7/n Image
We then show that specific failures of our models reflect missing design features that we uncover by adding latent features to the model.. 8/n Image
Our results reflect surprisingly simple design principles of real connectomes in 3 different species, offering a general computational framework for analyzing connectomes and linking structure & function in neural circuits.

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Elad Schneidman

Elad Schneidman Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @EladSchneidman

Jun 24, 2020
New from the lab w/@ben_brazowski:
Efficient Collective Learning by Ensembles of Altruistic Diversifying Neural Networks
1/4

arxiv.org/abs/2006.11671
Inspired by models of collective behavior in animals and artificial agents (pnas.org/content/114/22…), we studied co-learning by ensembles of interacting neural networks that aim to maximize their own performance but also their functional relations to other networks
2/4
Ensembles’ performance was optimal when coupling between networks increased diversity and degraded the performance of individual networks. Thus, even without a global goal for the ensemble, efficient collective behavior emerges from local interactions between networks
3/4
Read 4 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(