Closed laurentperrinet closed 8 months ago
Hi @brainhackorg/project-monitors my project is ready!
Thank you @laurentperrinet for submitting your project to BrainHack Marseille 2022! :tada::tada::tada: Your project have been added to our website.
Ruggero for the BHM team.
Title
Automatic detection of spiking motifs in neurobiological data
Leaders
Collaborators
Brainhack Global 2022 Event
Brainhack Marseille
Project Description
The study of spatio-temporal correlated activity patterns is very active in several fields related to neuroscience, like machine learning in vision (Muller Nat Rev Neurosci 2018) and neuronal representations and processing (Shahidi Nat Neurosci 2019). This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity. A diversity of formalizations and detection methods have been proposed and we will focus on several example measures for event/spike trains, to be compared on both synthetic and real data.
An implementation could be based on autodifferentiable networks as implemented in Python libraries like pytorch. This framework allows for the tuning of parameters with specific architectures like convolutional layers that can capture various timescales in spike patterns (e.g. latencies) in an automated fashion. Another recent tool based on the estimation of firing probability for a range of latencies has been proposed (Grimaldi ICIP 2022). This will be compared with existing approaches like Elephant’s SPADE or decoding techniques based on computed statistics computed on smoothed spike trains (adapted from time series processing, see (Lawrie, biorxiv).
One part concerns the generation of realistic synthetic data producing spike trains which include spiking motifs with specific latencies or comodulation of firing rate. The goal is to test how these different structures, which rely on specific assumptions about e.g. stationarity or independent firing probability across time, can be captured by different detection methods.
Bring you real data to analyze them! We will also provide data from electrophysiology.
Link to project repository/sources
Github repo
review on Precise spiking motifs in neurobiological and neuromorphic data
Grimaldi, Besnainou, Ladret, Perrinet (2022). Learning heterogeneous delays of spiking neurons for motion detection. Proceedings of ICIP 2022. https://laurentperrinet.github.io/publication/grimaldi-22-bc/grimaldi-22-bc.pdf
Polychronies grant
Goals for Brainhack Global
Good first issues
issue one: generate synthetic model for raster plots
issue two: design detection method knowing these motifs
issue three: supervised learning
issue four: unsupervised learning
Communication channels
https://mattermost.brainhack.org/brainhack/channels/bhg22-marseille-detecspikmotifs
Skills
Onboarding documentation
No response
What will participants learn?
Data to use
No response
Number of collaborators
4
Credit to collaborators
See the README file on the project's github repo.
Image
Type
method_development
Development status
1_basic structure
Topic
bayesian_approaches, deep_learning, information_theory, machine_learning, neural_decoding, neural_networks, statistical_modelling
Tools
Jupyter
Programming language
Python
Modalities
other
Git skills
1_commit_push
Anything else?
Come to us!
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!
Twitter-sized summary of your project pitch.: "This project aims to develop a method for the automated detection of repeating spiking motifs, possibly noisy, in ongoing activity."