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#hippocampus

2 posts2 participants1 post today

From the lab of Helene Schmidt:

"Large-scale 3D EM connectomics dataset of mouse hippocampal area CA1", Corteze et al. 2025
biorxiv.org/content/10.1101/20

Spectacular.

bioRxiv · Large-scale 3D EM connectomics dataset of mouse hippocampal area CA1The hippocampal formation is thought to be crucial for memory and learning, with subarea cornu ammonis 1 (CA1) considered to play a major role in spatial and episodic memory formation, and for evaluating the match between retrieved memories and current sensory information. While enormous progress has been made in classifying CA1 neurons based on molecular, morphological, and functional properties, and in identifying their role in behavioral tasks, a clear understanding of the underlying circuits is still missing. Here, we present the first large-scale three dimensional (3D) electron microscopy dataset of mouse CA1 at nanometer-scale resolution. The dataset is available online and can be readily used for circuit reconstructions, as demonstrated for inputs to CA1 superficial layers. Example volume segmentations show that automated reconstruction detection is feasible. Using these data, we find evidence against the long-held assumption of a homogeneous pyramidal cell population. Furthermore we find substantial possibly long-range axonal innervation of stratum-lacunosum interneurons, suggested previously to originate in L2 of MEC. These first analyses illustrate the usability of this dataset for finally clarifying the connectomic properties of mouse CA1, a key structure in mammalian brains. ### Competing Interest Statement The authors have declared no competing interest.

I keep going back to this question about #TemporalCreditAssignment and #HippocampalReplay:
As an "agent" you want to learn the value of places and which places are likely to lead to reward;

-1) if a place leads to higher than expected reward, you'll want to propagate back the reward info from the reward throughout the places that led to the reward. If replay does that you should see an increase of replay at a new reward site and the replay sequences should start at the reward and reflect what you just did to reach it. Right?

-2) if a place leads to lower than expected reward, you'll also want to propagate that lowered value, pretty much in the same way, so if replay does that you should see a similar replay rate and content for increased OR decreased reward sites. Right?

-3) if a place has had unchanged reward for a while and you're just in exploitation mode (just going there again and again because you know that's the best place to go to in the environment) then you shouldn't need to update anything and replay rate should be quite low at that unchanged reward side. Right?

That's not at all what replay is doing IRL, so does that mean replay is not used for temporal credit assignment? Or did I (very likely) miss something?

@johnwidloski and I are co-organizing a symposium on #HippocampalReplay at the #EBBS25 in Bordeaux!

Our goal: From the state of the art in awake replay models & existing data, determine the next big questions and most informative future experiments to finally figure out what is awake replay for!

Our speakers:

  • John Widloski
  • Lisa Genzel
  • Jacob Bakermans
  • Elisa Massi

Meeting dates: 28 June to 1 July 2025 (our symposium is on the last day

Programme: ebbs2025.azuleon.org/programme

The abstract submission deadline for posters (and early bird registration) is on 31 March 2025 - I hope to see you there!

ebbs2025.azuleon.orgEBBS 2025 :: ProgrammeThe website of the 51st EBBS Meeting :: EBBS 2025