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Title | Laboratory for Neural |
Description | Research Back To Top Research Group Leader Team Papers Welcome to the Laboratory for Neural Statistics at the Center for Neural Science (CNS) at NYU and the Center |
Keywords | N/A |
WebSite | neurostatslab.org |
Host IP | 185.199.111.153 |
Location | - |
Site | Rank |
US$1,130,388
Last updated: 2023-05-17 07:10:39
neurostatslab.org has Semrush global rank of 9,363,433. neurostatslab.org has an estimated worth of US$ 1,130,388, based on its estimated Ads revenue. neurostatslab.org receives approximately 130,430 unique visitors each day. Its web server is located in -, with IP address 185.199.111.153. According to SiteAdvisor, neurostatslab.org is safe to visit. |
Purchase/Sale Value | US$1,130,388 |
Daily Ads Revenue | US$1,044 |
Monthly Ads Revenue | US$31,304 |
Yearly Ads Revenue | US$375,637 |
Daily Unique Visitors | 8,696 |
Note: All traffic and earnings values are estimates. |
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neurostatslab.org. | A | 3599 | IP: 185.199.111.153 |
neurostatslab.org. | A | 3599 | IP: 185.199.109.153 |
neurostatslab.org. | A | 3599 | IP: 185.199.110.153 |
neurostatslab.org. | A | 3599 | IP: 185.199.108.153 |
neurostatslab.org. | NS | 21600 | NS Record: ns-cloud-e4.googledomains.com. |
neurostatslab.org. | NS | 21600 | NS Record: ns-cloud-e3.googledomains.com. |
neurostatslab.org. | NS | 21600 | NS Record: ns-cloud-e1.googledomains.com. |
neurostatslab.org. | NS | 21600 | NS Record: ns-cloud-e2.googledomains.com. |
Back To Top Research Group Leader Team Papers Welcome to the Laboratory for Neural Statistics at the Center for Neural Science (CNS) at NYU and the Center for Computational Neuroscience (CCN) at the Flatiron Institute. The lab opened recently in January 2022 and is led by Alex Williams . ↓↓ Scroll down to read about our research , our publications , and current lab members ↓↓ Research Overview We develop statistical models and open-source computational tools to extract insights from neural data. We are particularly interested in characterizing flexibility and variability in neural circuits—e.g., how do the dynamics of large neural ensembles change over the course of learning a new skill, during periods of high attention or task engagement, or during development and aging. Summarizing these processes even on an descriptive level is a difficult and unsolved challenge. Past projects have explored the use of tensor decomposition as a model of trial-by-trial gain modulation, time warping |
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