Brandeis Innovation Announces 2021 Sprout Program

Brandeis Innovation logoBrandeis Innovation is pleased to announce the kickoff of its annual Sprout program that supports bench research with grants up to $25,000, funded by the Office of the Provost and Office of Technology Licensing. Teams and individuals working on innovative projects and research within the Division of Science are eligible to apply.

Sprout helps bring scientific research and entrepreneurial ambitions to life by providing seed funding. As an added bonus, recipients will also have the option to choose to participate in our spring cohort of the NSF I-Corps fellowship, which provides training in technology commercialization and a stipend up to $750 for related expenses.

Interested applicants can complete this pre-application form, due on February 19th.

Ruth Charney to become AMS President on Feb. 1

Ruth CharneyRuth Charney, the Berenson Professor of Mathematics, will become the President of the American Mathematical Society on February 1.  The Notices of the American Mathematical Society recently published an interview with Charney. In the interview, Charney was asked about COVID’s impact on her own teaching, AMS’s response to COVID and the impact of the crisis on job prospects for new PhDs and postdocs.

Charney, whose research focus is geometric group theory, discussed how she first became involved with AMS and AWM (Association for Women in Mathematics) at a time when there were very few women in the field. One of Charney’s priorities as AMS president will be to increase diversity and inclusion into the study of mathematics.

Charney finished the interview by discussing the importance of professional societies like AMS to the mathematics community.

 

Grants for undergraduate research in computational neuroscience

The Division of Science is pleased once again to announce the availability of Traineeships for Undergraduates in Computational Neuroscience through a grant from the National Institute on Drug Abuse. Traineeships will commence in summer 2021 and run through the academic year 2021-22.

From former trainee Dahlia Kushinksy’s first-author paper published in Journal of Experimental Biology, “In vivo effects of temperature on the heart and pyloric rhythms in the crab, Cancer borealis”

Please apply to the program by March 2, 2021 at 6 pm to be considered.

 

Traineeships in Computational Neuroscience are intended to provide intensive undergraduate training in computational neuroscience for students interested in eventually pursuing graduate research. The traineeships will provide approximately $5000 in stipend to support research in the summer, and $3000 each for fall and spring semesters during the academic year. Current Brandeis sophomores and juniors (classes of ’22, ’23) may apply. To be eligible to compete for this program, you must

  • have a GPA > 3.0 in Div. of Science courses
  • have a commitment from a professor to advise you on a research project related to computational neuroscience
  • have a course work plan to complete requirements for a major in the Division of Science
  • complete some additional requirements
  • intend to apply to grad school in a related field.

Interested students should apply online (Brandeis login required). Questions may be addressed to Steven Karel <divsci at brandeis.edu> or to Prof. Paul Miller.

Summer Undergraduate Research Fellowship Applications for 2021

In spite of all the uncertainty about the summer to come, it is time for Brandeis Science undergraduates doing research to think about applying for summer fellowships. The Division of Science, through a variety of sources, will likely have enough money to support roughly the same number of students as in recent years. For the most part, funding comes in the form of $5000 stipends that are paid directly to students to support them while working in labs in the summer.

There are also full-year Computational Neuroscience traineeships to support students. We will be looking for 6 students to appoint for 2021-22.

While there is a variety of funding mechanisms, students can apply via a single unified application

To apply, students will need to have a commitment from a faculty mentor to supervise their research in Summer 2021. Applications will be due on March 2, 2021. Students will need a single letter of reference from their faculty mentor.

The SciComm Lab is hosting a workshop for Brandeis undergraduates who are interested in learning about application strategies for summer science research opportunities at Brandeis University.

Tijana Ivanovic selected to speak at 2021 Future of Biophysics Burroughs Wellcome Fund Symposium

Tijana IvanovicTijana Ivanovic, Assistant Professor of Biochemistry, has been selected as one of four young scientists to speak at the 2021 Future of Biophysics Burroughs Wellcome Fund Symposium on February 23, 2021. This symposium is part of the 65th Annual Meeting of the Biophysical Society and due to COVID concerns, will be held virtually.

The purpose of this symposium is to highlight the work of young researchers who are currently conducting research at the intersection of the physical and life sciences. Research in the Ivanovic Laboratory uses biophysical methods to uncover fundamental molecular mechanisms of virus translocation across biological membranes.

The other speakers selected for the 2021 Symposium are Elisabeth Fischer-Friedrich, TU Dresden, Germany; Abhishek Singharoy, Arizona State University, USA; and Chen Song, Peking University, China.

Turrigiano lab uncovers sources of neuronal heterogeneity

High activity neurons have greater instrinsic excitability and response to local inputs, but no difference in total input type or amount

Mammalian cortex has long been one of the most widely studied systems in neuroscience, dating back to the pioneering work of Santiago Ramon y Cajal in the late 19th century. The cortex is much larger in primates than other mammals, and is thought to be responsible for the advanced cognitive abilities of humans. Today, models of cortical connections and computations form the basis for some of the most powerful deep learning paradigms. However, despite this success, there is still much that is unknown about how cortex functions. One feature of cortex that has recently been discovered is that neurons that appear to be similar to each other can have very different baseline activity levels: some neurons are 100x more active than their neighbors. We don’t know how neurons that are otherwise highly similar in shape and genetic makeup can maintain such different activity levels, or if the neurons with high and low activity levels have different functions in the brain. These neurons are otherwise so similar to each other that it is difficult to tell them apart without recording their activity directly, and current techniques for recording the activity of many neurons simultaneously in live animals do not allow us to later re-identify them for further study.

In a paper recently published in Neuron, the Turrigiano lab, led by postdoctoral researcher Nick Trojanowski, reported a new approach for permanently labeling high and low activity neurons in live animals, and then determining what makes them different. To do this they used a fluorescent protein called CaMPARI2 that changes from green to red as activity increases, but only when exposed to UV light. By shining UV light into the brain, they caused neurons with high activity to turn red, while neurons with low activity remained green. This procedure allowed them to run a series of tests on high and low activity neurons to identify differences between them. They found that high activity neurons would intrinsically generate more activity than low activity neurons when presented with the same stimulus. These high activity neurons also receive more excitatory input specifically from nearby neurons of the same type. Surprisingly, however, they found that the total amount of excitatory and inhibitory input that high and low activity neurons received from other neurons was not a major factor in determining their activity levels. Together, these results tell us that the differences in activity between neurons are due to intrinsic differences, as well as their pattern of connectivity to their nearby partners. This has deep implications for how the networks that underlie cortical computations are built and maintained.

With these tools in hand, it is now possible to further explore the differences between high and low activity neurons. Do these neurons serve different functions? Are the baseline activity levels specified from birth? How do these activity levels affect the mechanisms of plasticity that are responsible for learning and memory? The recently published results represent just the tip of the iceberg of information that can be learned with this new technique, in the mammalian cortex as well as other brain regions.

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