Mastodawn

El Duvelle Neuro Sep 11, 2023

4- More recently: thread on our review of #SplitterCells (a subset of #PlaceCells) where we try to summarize and make sense of many studies, asking if they might support decision-making, or which type of #ComputationalModel might support their firing:
https://elduvelle.github.io/ElDuvelle/status/1556036545223745538/

The associated paper:
Temporal context and latent state inference in the hippocampal splitter signal

It seems like a combination of two types of models - Latent State models and Temporal Context models - might best suit the hippocampal Splitter Signal.. but (IMO) it doesn't look like Splitter cells are really doing anything for actual, immediate decision-making.

4/x

El Duvelle 🌍 on Twitter (archived)

The rodent hippocampus is amazing: place cells, time cells, reward cells...Did you know about the puzzling #SplitterCells?We sought to understand them in an experimental & computational review on temporal context & latent state models!Preprint: https://psyarxiv.com/9z4wr/ 1/25

Phys.org Jun 5, 2023

Referenced link: https://techxplore.com/news/2023-06-mimics-humans-ability-emotions.html
Discuss on https://discu.eu/q/https://techxplore.com/news/2023-06-mimics-humans-ability-emotions.html

Originally posted by Phys.org / @physorg_com: http://nitter.platypush.tech/TechXplore_com/status/1665734326699577346#m

RT by @physorg_com: #Computationalmodel mimics humans' ability to predict emotions @MIT https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0047 https://techxplore.com/news/2023-06-mimics-humans-ability-emotions.html

Computational model mimics humans' ability to predict emotions

When interacting with another person, you likely spend part of your time trying to anticipate how they will feel about what you're saying or doing. This task requires a cognitive skill called theory of mind, which helps us to infer other people's beliefs, desires, intentions, and emotions.

Tech Xplore

Dominic Boutet May 30, 2023

#arxivfeed

"Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics"
https://arxiv.org/abs/2305.18046

#MolecularDynamics #ComputationalModel #SurrogateModel #TimeScale #Simulation #DiffusionModel

Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics

Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereas convergence of some moments, e.g. binding free energy or rates, might rely on sampling processes on time-scales as long as $10^{-1}\, \mathrm{s}$, and these simulations must be repeated for every molecular system independently. Here, we present Implict Transfer Operator (ITO) Learning, a framework to learn surrogates of the simulation process with multiple time-resolutions. We implement ITO with denoising diffusion probabilistic models with a new SE(3) equivariant architecture and show the resulting models can generate self-consistent stochastic dynamics across multiple time-scales, even when the system is only partially observed. Finally, we present a coarse-grained CG-SE3-ITO model which can quantitatively model all-atom molecular dynamics using only coarse molecular representations. As such, ITO provides an important step towards multiple time- and space-resolution acceleration of MD. Code is available at \href{https://github.com/olsson-group/ito}{https://github.com/olsson-group/ito}.

arXiv.org

El Duvelle Apr 8, 2023

#NeuroPreprint #Replay #Hippocampus #ComputationalModel
Constructing future behaviour in the hippocampal formation through composition and replays

Xenia Schmalz Mar 7, 2023

New #preprint: "Rules and statistics: What if it’s both? A basic computational
model of statistical learning in reading acquisition", describing my first attempt at a #ComputationalModel: https://osf.io/5b76z. The model aims to explain how #StatisticalRegularities can be an integral part of orthographic systems, while little evidence points to a relationship between #StatisticalLearning ability and #reading performance. Feedback welcome! :D

Daniel Heck Nov 6, 2022

It might be fun to develop an agent-based #ComputationalModel of social #network dynamics to #model the #TwitterMigration

The parameters of the model could then be updated on the fly via #Bayesian inference 😎

Or maybe, the current situation could be used to test critical predictions derived from existing models...