Encoding taste and place in the hippocampus

The ambience of a good meal can sometimes be as memorable as the taste of the food itself. A new study from Shantanu Jadhav and Donald Katz’s labs, published in the February 18 edition of The Journal of Neuroscience, may help explain why. This research identified a subset of neurons in the hippocampus of rats that respond to both places and tastes.

The hippocampus is a brain region that has long been implicated in learning and memory, especially in the spatial domain. Neurons in this area called “place cells” respond to specific locations as animals explore their environments. The hippocampus is also connected to the taste system and active during taste learning. However, little is known about taste processing in the hippocampus. Can place cells help demarcate the locations of food?

To test this hypothesis, Neuroscience PhD student Linnea Herzog, together with staff member Leila May Pascual and Brandeis undergraduates Seneca Scott and Elon Mathieson, recorded from neurons in the hippocampus of rats as the rats explored a chamber. At the same time, different tastes were delivered directly onto the rats’ tongues.

Analyzing how place cells responded to tastes delivered inside or outside of their place field

The researchers found that about 20% of hippocampal neurons responded to tastes, and could discriminate between tastes based on palatability. Of these taste-responsive neurons, place cells only responded to tastes that were consumed within that cell’s preferred location. These results suggest that taste responses are overlaid onto existing mental maps. These place- and taste-responsive cells form a cognitive “taste map” that may help animals remember the locations of food.

Eve Marder and Liqun Luo receive 2019 NAS awards

Eve Marder NAS award

Eve Marder, the Victor and Gwendolyn Beinfield Professor of Neuroscience, has received the 2019 National Academy of Sciences Award in the Neurosciences. The National Academy of Sciences is recognizing “Marder’s research of over more than 40 years that has provided transformative insight into the fundamental processes of animal and human brains.” NAS also called Marder “one of the most influential neuroscientists of her generation”.

Liqun Luo

In addition to her research, NAS acknowledged Marder’s impact upon young scientists working in her field. She has served as a mentor to “generations of neuroscientists”.  A book titled “Lessons from the Lobster: Eve Marder’s Work in Neuroscience” by Charlotte Nassim and was published in 2018.

The NAS Award in the Neurosciences is given only once every three years.

In addition to Marder, a Ph.D. alumnus is among the 18 scientists that are being recognized this year. Liqun Luo received the Pradel Research Award.  In the press release, NAS cited Luo’s “pioneering research into neural circuits of invertebrates and vertebrates.”

Luo earned his Ph.D. in Biology from Brandeis in 1992. He worked in Kalpana White’s lab. He is now a Professor and HHMI Investigator at Stanford University.

Read more at Brandeis Now.

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 reasonant 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!

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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