Diogo Santos-Pata Profile picture
Apr 26, 2021 10 tweets 6 min read Read on X
#tweeprint
Who tells the #hippocampus what and when to learn? Our latest article together with @adriamilcar @IvanRaikov7 @rennocosta @annamurav @SolteszLab @PaulVerschure, is out in @TrendsCognSci. cell.com/trends/cogniti…
Link to open access:
authors.elsevier.com/c/1cype4sIRvHk…
We describe the entorhinal-hippocampal circuit (EHC) components enabling self-supervised learning. Cortical projections enter the hippocampus via the entorhinal cortex and loop over DG and CA fields, functioning as a comparator to reconstruct its inputs(see Lörincz & Buzsáki 2000 Image
How can it approximate raw and reconstructed inputs? GABAergic neurons activation relate to the learning stage and their projections mostly counter-current to the perforant pathway, suggesting they are part of a circuit implementing backpropagation of the error within the EHC. Image
What are the advantages? These ingredients allow the hippocampus to autonomously build and update conjunctive representations of the environment (episodes).
Thus, this simple rule might be sufficient to trigger many of the physiological benchmarks found experimentally. Image
In an accompanying article, we implemented the simplest self-supervised model of entorhinal rate reconstruction and measured the cells tuning to spatial locations and responses to environmental modifications.
cell.com/iscience/fullt…
We observed that this single reconstruction function was sufficient to trigger rate remapping, adaptation to environmental morphing, place-field expansion, novelty detection, among others. Image
Surprisingly, when testing the model to reconstruct entorhinal inputs during environmental modifications, we observed that predicted grid cells' firing fields tend to expand (see Barry et al. 2012) Image
We then simulated an elongation of the arena (O’Keefe & Burgess 1996) as an interpolated expansion of the LEC rate maps and periodic extension of the MEC rate maps. This elongation generated an increase in both the number and size of place fields, across all layers. Image
Finally, the error magnitude revealed to be a good novelty signal driving learning. Indeed, re-learning of either familiar or novel environments plateaus faster than for moderately modified ones. Image
We have set a GitLab repository sharing the model implementation and response analysis:
gitlab.com/diogo.santos.p…
Please try it out.

• • •

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

Keep Current with Diogo Santos-Pata

Diogo Santos-Pata 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!

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!

:(