LMPrida

@LMPrida@fosstodon.org

Neuroscientist migrating here from 🐦 A node in a network. My lab: http://hippo-circuitlab.es/

This profile is from a federated server and may be incomplete. Browse more on the original instance.

LMPrida, to random Spanish

Our new 📝 in #PLoSCompBiology. We describe the #StructureIndex, a graph-based metric to quantify how feature values are distributed over a point cloud in any dim-space (genes, neural manifolds, signals, pixels..) https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011768

LMPrida, to random Spanish

🔔Our new 📝 is out!! In a nutshell: Hippocampal ripples are variable events that reflect reactivation of cell assemblies driven by different inputs. Spectral methods alone fall short in addressing their complexity. We transformed ripple analysis into a topological problem. We show that ripple waveforms can be represented in a low-dimensional space, which conveys information about layer-specific synaptic inputs. Read it here 👉🏼 https://www.nature.com/articles/s41593-023-01471-9

LMPrida, to random Spanish

How to merge the physiological and the emergent view on hippocampal representations? From cell types to population dynamics: Making hippocampal manifolds interpretable. Our perspective on this question is now out 👉🏼 https://www.sciencedirect.com/science/article/pii/S0959438823001253

LMPrida, to random Spanish

Looking for outstanding postdocs with computational and/or physics background to lead a #MSCA COFUND project under the #AIHub #csic strategy. Starting in 2024. Contact me directly. Ok

LMPrida, to random Spanish

On my way to #InhibitionGRC at Les Diablerets 🚂!! One of my favorite GRC conferences !! Great talks and posters ahead! https://www.grc.org/inhibition-in-the-cns-conference/2023/

LMPrida, to random Spanish

Our new @biorxivpreprint preprint 📝 is out!! https://www.biorxiv.org/content/10.1101/2023.07.02.547382v1 by Andrea Navas-Olive and Adrian Rubio. 👉🏼An opensource ML toolbox for sharp-wave ripple detection https://github.com/PridaLab/rippl-AI Trained with 🐭 data and applied to 🙊 in collaboration with @karihoffman and Sam Abbaspoor #DeepCode

LMPrida,

@karihoffman @elduvelle @cogneurophys Exactly, experts are at 0.7 F1 so the models work pretty well. In our eLife paper we actually showed how F1 of the CNN improves if we use the consolidated GT (union of two experts’ GTs). This means that some FP of the model are actually in the GT of some expert, calling for community tagging to improve performance. As for the models, here is a comparison for Precision and Recall (the diagonal is blanked).

  • All
  • Subscribed
  • Moderated
  • Favorites
  • JUstTest
  • InstantRegret
  • mdbf
  • ethstaker
  • magazineikmin
  • cubers
  • rosin
  • thenastyranch
  • Youngstown
  • osvaldo12
  • slotface
  • khanakhh
  • kavyap
  • DreamBathrooms
  • provamag3
  • Durango
  • everett
  • tacticalgear
  • modclub
  • anitta
  • cisconetworking
  • tester
  • ngwrru68w68
  • GTA5RPClips
  • normalnudes
  • megavids
  • Leos
  • lostlight
  • All magazines