El Duvelle Neuro<p><a href="https://neuromatch.social/tags/NeuroESC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NeuroESC</span></a> <a href="https://neuromatch.social/tags/JournalClub" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>JournalClub</span></a> <br>Reading <a href="https://www.biorxiv.org/content/10.1101/2025.02.03.636313v1.full" rel="nofollow noopener noreferrer" target="_blank">Mental exploration of future choices during immobility theta oscillations</a></p><p>If you've read it, will you let me know what you think?</p><p>The authors look at <a href="https://neuromatch.social/tags/ThetaSequences" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ThetaSequences</span></a> in a working memory task in a radial arm maze. They find theta during immobility (makes sense, e.g. we saw that in our <a href="https://elduvelle.github.io/ElDuvelle/status/1385701872376455172/" rel="nofollow noopener noreferrer" target="_blank">two-goals task</a>). They also find that theta sequences might preferentially represent the next goal (also makes sense, e.g. <a href="https://www.nature.com/articles/nn.3909" rel="nofollow noopener noreferrer" target="_blank">Hippocampal theta sequences reflect current goals</a>)! </p><p>I have only done a quick reading so far, but am confused by a few points:</p><ul><li>the decoding is done on all cells (pyramidals and interneurons), shouldn't it be done on pyramidal or <a href="https://neuromatch.social/tags/PlaceCells" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PlaceCells</span></a> only?</li><li>the cell counts are quite low (often less than 40 pyrs) when I would have thought at least 50 place cells would be needed for this kind of maze. I guess that shows that <a href="https://neuromatch.social/tags/Neuropixels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuropixels</span></a> are not the best to record from dCA1!</li><li><p>the decoded algorithm itself includes a ' position transition matrix' which seems like it would bias decoding towards realistic trajectories that the rat is about to do??? (but I probably missed something)</p></li><li><p>also, this study is <em>very</em> related to this other paper, which is not discussed or even cited (😕 ):<br><a href="https://www.cell.com/neuron/fulltext/S0896-6273(18)31006-7" rel="nofollow noopener noreferrer" target="_blank">Assembly Responses of Hippocampal CA1 Place Cells Predict Learned Behavior in Goal-Directed Spatial Tasks on the Radial Eight-Arm Maze</a> <a href="https://neuromatch.social/tags/CsisvariLab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CsisvariLab</span></a></p></li></ul><p>Let me know what you think!</p><p><a href="https://neuromatch.social/tags/LeutgebLab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LeutgebLab</span></a> <a href="https://neuromatch.social/tags/NeuroRat" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NeuroRat</span></a> <a href="https://neuromatch.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a> <a href="https://neuromatch.social/tags/SpatialCognition" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SpatialCognition</span></a></p>