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 2019 and run through the academic year 2019-20.

From former trainee Dahlia Kushinksy’s first-author paper published this month 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 February 27, 2019 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 ’20, ’21) 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.

Leading Science: Magnifying the Mind

Brandeis Magnify the Mind

Written by Zosia Busé, B.A. ’20

Joshua Trachtenberg, graduated from Brandeis in 1990 and is a leader in studying the living brain in action using advanced imaging technology. After establishing his research laboratory at UCLA, he founded a company – Neurolabware – in order to build the sophisticated custom research microscopes that are needed to perform groundbreaking work in understanding how the brain develops and how diseases and injuries interrupt its normal functioning. His company is created by scientists and for scientists, and is unique in creating high quality microscopes that are easy to use but also have the flexibility to be used in creative ways in innovative experiments, and in combination with a variety of other devices.

Brandeis University is now seeking to acquire one of these advanced microscopes that can observe fundamental processes inside the living brains of animals engaged in advanced behaviors. The resonant scanning two-photon microscope from Neurolabware allows researchers to understand and image large networks of neurons in order to visualize which cells and networks are involved with specific memories or how these processes go awry in disease. “This approach is unparalleled. There is no other technique around that could possibly touch this,” Trachtenberg says.

Previous two-photon technologies permitted only very slow imaging, allowing scientists to take a picture about every two seconds, but the resonant two-photon technology is a major breakthrough that allows scientists to take pictures at about 30 frames per second. This speed increase is a major game changer. Not only can one observe activity in the brain at a higher speed, but it is possible to take pictures at a speed that is faster than the movement artifacts that must be accounted for, such as breathing or heart beats. Because one can see the movement, it can be corrected, allowing high resolution functional imaging of structures as small as single synaptic spines in the living brain. Further, advances in laser technology and fluorescent labels now allow scientists to see deeper into the brain than ever before, compounding the recent advantages of increased speed.

[Read more…]

Marder Lab wins the Ugly Sweater contest

 

A new feature was added to the 2018 Life Sciences Holiday Party – the Ugly Sweater Contest! Lab’s were encouraged to purchase, design, and bedazzle a sweater for their PIs to wear and show off at the party. Ballots for best sweater were cast at the event with the Marder lab submitting the winner. Eve’s sweater was decked out with crabs, lobsters, STG’s and neurons.  Congratulations!

How do batteries work?

How do batteries really work? A convincing simple yet quantitative answer to this question has remained elusive. Textbooks and on-line sources have provided only descriptions but not explanations of basic electrochemistry. All calculations in electrochemistry are based on measured voltages, not atomic or molecular properties. Made-up explanations of batteries in terms of different “electron affinities” of different metals are widely believed but easily disproved, e.g. by concentration cells using the same metal for both electrodes.

A paper in the Journal of Chemical Education by Klaus Schmidt-Rohr (Chemistry) explains how batteries store and release energy, in quite simple terms but based on quantitative data. In the classical Zn/Cu galvanic cell, it is the difference in the lattice cohesive energies of Zn and Cu metals, without and with d-electron bonding, respectively, that is released as electrical energy. Zinc is also the high-energy material in a 1.5-V alkaline household battery. In the lead–acid car battery, intriguingly the energy is stored in split water (two protons and an oxide ion). Atom transfer into or out of bulk metals or molecules plays as big a role as electron transfer in driving the processes in batteries.

How Batteries Store and Release Energy: Explaining Basic Electrochemistry, Klaus Schmidt-Rohr, Journal of Chemical Education, 2018, 95 (10), pp 1801–1810.

HMS Professor Stephen Harrison to Receive 48th Rosenstiel Award

Prof. Stephen C. Harrison will receive the 48th Rosenstiel Award for Distinguished Work in Basic Medical Research on March 25, 2019. He is being honored for his studies of protein structure using X-ray crystallography.  His work has ranged from the landmark elucidation of the structure of viruses, to understanding the recognition of DNA sequences by transcription factors, to the regulation of protein kinases implicated in cancer. The event will take place from 4:00 to 5:00 PM on Monday, March 25 in Gerstenzang 123.

Harrison is the Giovanni Armenise-Harvard Professor of Basic Medical Sciences and Director of the Center for Molecular and Cellular Dynamics at the Harvard Medical School.  He is also Head of the Laboratory of Molecular Medicine at Boston Children’s Hospital and an Investigator of the Howard Hughes Medical Institute.   He has been elected a member of the US National Academy of Sciences, the American Academy of Arts and Sciences,  the American Philosophical Society; he is a foreign member of the Royal Society and the European Molecular Biology Organization.

Dr. Harrison’s initial studies of virus structure provided an understanding of how viruses invade cells and how virus particles are assembled.  He has extended his work to reveal the structures of many viruses, including influenza, HIV, ebola and dengue.  Knowledge of these structures is guiding the development of new vaccines against these viruses.  Moreover, the methodology that he and his colleagues developed to visualize virus structure has made it possible to learn about the molecular architecture of other very large assemblies of proteins.

Harrison’s lab has also revealed the ways that proteins recognize specific DNA sequences to regulate gene expression.  More recently his lab has been exploring the complex structure of the many proteins that are assembled in the kinetochore, which anchors the centromeres of chromosomes to microtubules, to permit their proper segregation in mitosis.

“Steve Harrison has done much more than giving us astonishing pictures of proteins at the atomic level; he has used this structural information to show us how these proteins perform their precise functions,” said James E. Haber, Director of the Rosenstiel Center for Basic Medical Sciences.

The Rosenstiel Award has had a distinguished record of identifying and honoring pioneering scientists who subsequently have been honored with the Lasker and Nobel Prizes. Awards are given to scientists for recent discoveries of particular originality and importance to basic medical research.

View full list of awardees.

 

 

New Computational Neuroscience Textbook

Paul Miller bookComputational Neuroscience is an exciting branch of science, which is helping us understand how simple biophysical processes within cells such as neurons lead to complex and sometimes surprising neural responses, and how these neurons, when connected in circuits can give rise to the wide range of activity patterns underlying human thinking and behavior. To bridge the scales from molecules to mental activity, computer simulations of mathematical models are essential, as it is all too easy for us otherwise to produce descriptions of these complex interacting systems that are internally inconsistent. Simulations allow us to ask “given these ingredients, what is possible?”

Simulation showing how weaker input that is localized can produce spiking when stronger dispersed input does not.

The best way to study computational neuroscience is to write the computer codes that model a particular biological phenomenon, then see what the simulation does when you vary a parameter in the model. Therefore, the course I teach at Brandeis (NBIO 136B) is based around a large number of computer tutorials, in which students, some of whom have no computer-coding background, begin with codes of 5-10 lines that simulate charging of a capacitor, and end up completing codes that simulate the neural underpinnings of learning, pattern recognition, memory, and decision-making. It turns out that very few computational principles are needed to build such codes, making these simulation methods far more easily understood and completed than any mathematical analysis of the systems. However, in the absence of a suitable introductory textbook—most computational neuroscience textbooks are designed by Ph.D. physicists and mathematicians for Ph.D. physicists and mathematicians—it proved difficult for me to use the flipped classroom approach (see below). Therefore, my goal was to create a text that students could read and understand on their own.

Different behaviors of a three-unit circuit as connection-strengths are changed. (Multistable constant activity states, multiple oscillating states, chaotic activity, heteroclinic state sequence). Each color represents firing rate of a unit as a function of time.

In keeping with the goal of the course—to help students gain coding expertise and understand biological systems through manipulations of computer codes—I produced over 100 computer codes (in Matlab) for the book, the vast majority of which are freely available online. (All codes used to produce figures and some tutorial solutions are accessible, but I retained over half of the tutorial solutions in case instructors wish to assign tutorials without students being able to seek a solution elsewhere.)

Learn more at MIT Press.

From the Preface of the book:

I designed this book to help beginning students access the exciting and blossoming field of computational neuroscience and lead them to the point where they can understand, simulate, and analyze the quite complex behaviors of individual neurons and brain circuits. I was motivated to write the book when progressing to the “flipped” or “inverted” classroom approach to teaching, in which much of the time in the classroom is spent assisting students with the computer tutorials while the majority of information-delivery is via students reading the material outside of class. To facilitate this process, I assume less mathematical background of the reader than is required for many similar texts (I confine calculus-based proofs to appendices) and intersperse the text with computer tutorials that can be used in (or outside of) class. Many of the topics are discussed in more depth in the book “Theoretical Neuroscience” by Peter Dayan and Larry Abbott, the book I used to learn theoretical neuroscience and which I recommend for students with a strong mathematical background.

The majority of figures, as well as the tutorials, have associated computer codes available online, at github.com/primon23/Intro-Comp-Neuro, and at my website. I hope these codes may be a useful resource for anyone teaching or wishing to further their understanding of neural systems.

 

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