The National Institutes of Health will fund more than 175 grants, totaling nearly $500 million, through the NIH’s Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, part of a large effort among federal and non-federal partners to use knowledge about how the brain works to develop more effective therapies for neurological disorders.
The BRAIN Initiative supports the development of a diverse portfolio of biomolecular tools and emphasizes their rapid and broad dissemination to the research community. Browse the Addgene collection of plasmids created with support from the BRAIN Initiative.
This is a template document to be used for agreements between device manufacturers and academic research institutions to form partnerships for submission of grant applications to the NIH for clinical research.
The Allen Institute today announced six new Next Generation Leaders, members of a unique neuroscience advisory panel made up of early-career researchers. Now in its seventh year, the Next Generation Leaders Council advises neuroscience research efforts at the Allen Institute, namely the Allen Institute for Brain Science and the MindScope Program.
When neuroscientist Sébastien Tremblay set out to manipulate monkeys’ brains with light, colleagues had sobering advice: “It’s more difficult than it sounds.” Tremblay, who works in neuroscientist Michael Platt’s lab at the University of Pennsylvania, uses light to activate or silence precise groups of neurons and probe their role in brain function.
Emery N. Brown, MD, PhD, will receive the 2020 Swartz Prize for Theoretical and Computational Neuroscience. The $30,000 prize, supported by the Swartz Foundation, honors an individual whose work has produced a significant cumulative contribution to theoretical models or computational methods in neuroscience or who has made a particularly noteworthy recent advance to the field. It will be presented during SfN’s Awards Announcement Week 2020.
Cold Spring Harbor Laboratory scientists dramatically improved the efficiency of automated methods for tracing neuronal connections. They taught a computer to recognize different parts of neurons, then used the math of topology to see how those neurons are likely to connect.