Gabriel PeyréHeat diffusion vs. wave equation on a surface. https://en.wikipedia.org/wiki/Laplace%E2%80%93Beltrami_operator
| Website | http://neural-reckoning.org/ |
Dan Goodman
- 17 Followers
- 195 Following
- 211 Posts
Computational neuroscientist at Imperial College. Co-founded Brian simulator, Neuromatch, SNUFA. Loves spiking neural networks and off-beat machine learning.
@jonny @jordan idea for neuromatch.social. How about experimenting with better thread views? I never liked Twitter much for this but to be honest masto is no better and maybe even worse. I'd like a block that shows the first message in the thread to remind me what it was, the number of messages and participants (and maybe list the most active participants), and a vertical scroll list by recency with message being replied to on left, replies on right. Or something like that. Feasible?
Joby Joseph
PsychoPyThis is the PsychoPy team's #introduction to Mastodon.
PsychoPy is an #opensource package to build and run experiments in #python and #js
PsychoPy is a widely used tool for the behavioural sciences like #psychology #linguistics #economics #neuroscience
This account will also tweet about other parts of the ecosystem like #pavlovia (it's like GitHub for behavioural science) and #PsychoJS
Looking forward to chatting with you here! 🥰😁
Alex Holcombe@bwyble @auditoryJoel @neuralreckoning @Samuelmoore @GunnarBlohm
BioRXiv does its part with that COVID19 un-reviewed preprints warning. The attached should be a common notice on journal websites.
BioRXiv does its part with that COVID19 un-reviewed preprints warning. The attached should be a common notice on journal websites.
Iannetti LabFaculty position in Computational Neuroscience open at the Italian Institute of Technology. Generous support for salary, start-up budget, and annual running costs. Feel free to reach out for informal inquires.
Details here: https://tinyurl.com/7ztdnkbn
Details here: https://tinyurl.com/7ztdnkbn
How do the #bayes #psychology people think about #multimodal common onset? I have the impression - probably ignorance - that multimodal evidence is usually thought of as a linear weighed sum of evidence across the separate modalities, but this can't work for common onset or other temporal correlations between modalities. I guess it doesn't have to be multimodal either, common onset within a modality would be the same issue. Maybe one for @bwyble ?
jonnyI need some #FediDev help:
So If we want to really kill birdsite, we need to do things that are impossible on corporate platforms
I have been working on one idea, #DIYAlgorithms, by adding an API endpoint to let masto lists be manually controlled lists of statuses (instead of lists of accounts), so an external algo can autopopulate them.
I have no idea what i'm doing tho, and we need to move quick before another centralized platform really takes off.
help plz?
https://github.com/sneakers-the-rat/mastodon/tree/feature/postlists
Patrick MineaultI love this MouseNet paper: lots of detailed work to build an ANN that's mouse-visual-cortex-like, with the right fan-in, fan-out, cortical layers, area-to-area connectivity. You'd think this detailed modelling work would make the model a better fit to mouse brain data, but no: an off-the-shelf VGGNet is better at explaining mouse cortex.
I think this means that because high capacity ANNs are universal approximators, the implicit prior from architectural choices ends up being quite weak; the dataset (e.g. ImageNet vs. Ecoset vs. mouse naturalistic data) and the task (e.g. object recognition , self-supervision, etc.) are probably more important.
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010427
MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex
Author summary Task-driven deep neural networks have shown great potential in predicting functional responses of biological neurons. Nevertheless, they are not precise biological analogues, raising questions about how they should be interpreted. Here, we build new deep neural network models of the mouse visual cortex (MouseNet) that are biologically constrained in detail, not only in terms of the basic structure of their connectivity, but also in terms of the count and hence density of neurons within each area, and the spatial extent of their projections. Equipped with the MouseNet model, we can address key questions about mesoscale brain architecture and its role in task learning and performance. We ask, and provide a first set of answers, to: What is the performance of a mouse brain-sized—and mouse brain-structured—model on benchmark image classification tasks? How does the training of a network on this task affect the functional properties of specified layers within the biologically constrained architecture—both overall, and in comparison with recorded function of mouse neurons? We anticipate much future work on allied questions, and the development of more sophisticated models in both mouse and other species, based on the freely available MouseNet model and code which we develop and provide here.