{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-review-of-network-inference-techniques-for","title":"A Review of Network Inference Techniques for Neural Activation Time Series","arxiv_id":"1806.08212","date":"2018-06-20","proceeding":null,"authors":["George Panagopoulos"],"abstract":"Studying neural connectivity is considered one of the most promising and\nchallenging areas of modern neuroscience. The underpinnings of cognition are\nhidden in the way neurons interact with each other. However, our experimental\nmethods of studying real neural connections at a microscopic level are still\narduous and costly. An efficient alternative is to infer connectivity based on\nthe neuronal activations using computational methods. A reliable method for\nnetwork inference, would not only facilitate research of neural circuits\nwithout the need of laborious experiments but also reveal insights on the\nunderlying mechanisms of the brain. In this work, we perform a review of\nmethods for neural circuit inference given the activation time series of the\nneural population. Approaching it from machine learning perspective, we divide\nthe methodologies into unsupervised and supervised learning. The methods are\nbased on correlation metrics, probabilistic point processes, and neural\nnetworks. Furthermore, we add a data mining methodology inspired by influence\nestimation in social networks as a new supervised learning approach. For\ncomparison, we use the small version of the Chalearn Connectomics competition,\nthat is accompanied with ground truth connections between neurons. The\nexperiments indicate that unsupervised learning methods perform better,\nhowever, supervised methods could surpass them given enough data and resources.","url_abs":"http://arxiv.org/abs/1806.08212v1","url_pdf":"http://arxiv.org/pdf/1806.08212v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-review-of-network-inference-techniques-for","repo_url":"https://github.com/GiorgosPanagopoulos/Network-Inference-From-Neural-Activations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}