Computational Neuroscience

The Computational Neuroscience Specialization (CNS) is a facet of the broad Neurosciences Graduate training environment at UCSD. The goal of the specialization is to train the next generation of neuroscientists with the broad range of computational and analytical skills that are essential to understand the organization and function of complex neural systems. The specialization is particularly intended for students with backgrounds in the physical sciences, engineering, data science, and mathematics that are pursuing doctoral research in neurosciences.

The specialization allows students to augment their parent PhD program requirements with focused course work in the theoretical, analytical, and experimental aspects of computational neuroscience. Students are encouraged to pursue thesis research that includes both an experimental and a computational component, possibly arranged by the student as a collaboration between two research groups. Upon achievement of degree requirements, students will receive a diploma indicating both their successful completion of the broader Neuroscience Program as well as their specialization in Computational Neuroscience.

CNS Requirements

Recommended Mathematical Background

Potential CNS students should have a working background in calculus, ordinary differential equations, linear algebra, and probability. Knowledge of MATLAB or Python is suggested. It is recommended that students review this material the academic year before enrolling in CNS classes. "Tools for experimental data analysis" (NEUG/PSYC 231) when offered, provides a pedagogical review.

General CNS Requirements

All CNS students must complete the core courses required for by their departmental PhD program. CNS students should plan a curriculum of four approved CNS classes (see link to Approved CNS Classes), chosen in consultation with their thesis advisors and/or senior CNS faculty, from the list of 16 approved classes below. Typically, these classes are taken in year two and three. The minimum is four, and classes off the approved list may be substituted by petition. Note that CNS classes may also count in part or full as required electives in a given department.

As a guide, a suggested CNS curriculum includes the "classic canon" in theoretical neuroscience plus modern applied methodology is:

Neurodynamics (BGGN 260), offered in Fall
Neurophysics of brain circuits and networks (PHYS 278), offered in Winter
Probability and statistics for data science (DSC 212), offered all quarters
An additional class from the approved list

In addition to formal classes, CNS students are expected to actively participate and to present regularly in a weekly neuro-theory journal club (https://neurotheory.ucsd.edu/journal-club). This journal club brings together the broad theoretical and computational neuroscience community at UCSD, the Salk Institute, and TSRI. On an episodic basis, visiting academic leaders in computational neuroscience will give extended "chalk talks" at the journal club. CNS students are encouraged to include in their presentations a pedagogical review of background material needed to understand the primary literature in focus. Inclusion of pedagogical material helps junior students in the CNS gain independence as scientists and helps the senior CNS students deepen their expertise and hone their presentation skills.

For Neuroscience Graduate Program (NGP) Students

As a specific example, the core courses required for NGP students encompass Basic Neurosciences (NEU 200A/B/C) and Neuroanatomy (NEUG 257). Note that Neurosciences NEUG 200C contains three sessions on computational issues to solicit interest in the CNS! A fifth NGP requirement in statistics (BGGB 216, BGGN 240, BGGN 249A, PSYC 201A/B/C, COGS 209) is waived for CNS students. Thus, matriculation for NGP students involves eight classes total.

Questions:

The faculty contact for CNS is Prof. David Kleinfeld (https://neurophysics.ucsd.edu/) dk@physics.ucsd.edu

Application Details

All students admitted to the Neurosciences Graduate Program, as well as Ph.D. candidates in Bioengineering, and Physics, are currently eligible to apply to the CNS.

A PhD student in any eligable department can apply to the CNS program at any time, but this is best done before the start of their second year. After a preliminary review, our team will request a copy of their C.V., undergraduate transcripts, graduate transcripts, and a short description of their research interests. This application will be approved by the CNS Committee Chair, in consultation with the student's advisor and other faculty as needed

Upon achievement of doctoral degree requirements, students will receive a diploma stating "Neurosciences/Bioengineering/Physics with a Specialization in Computational Neuroscience"

Where Are They Now? Trajectories of past CNS Graduates

Faculty and Principal Investigators

Yonatan Aljadeff (2014) Assistant Professor, UC San Diego

Kevin L. Briggman (2005) MPI Director, Bonn

Flavio Frohlich (2007) Associate Professor, UNC Chapel Hill

Karunesh Ganguly (2004) Professor, UCSF

Aleena Garner (2012) Assistant Professor, Harvard

Margaret Henderson (2021) Assistant Professor, Carnegie Mellon University

Shantanu Jadhav (2008) Associate Professor, Brandeis University

James Jeanne (2012) Assistant Professor, Yale University

Alfred Kaye (2013), MD (2015) Assistant Professor, Yale University

Jyoti Mishra (2008) Assistant Professor, UC San Diego

Jeffrey Moore (2021) Leon Thal Prize, Assistant Professor, USC

Stephanie Nelli (2019) Assistant Professor, Occidental College

Tanya Nguyen (2015) Assistant Professor, UC San Diego

Andrew J Peters (2016) Assistant Professor, Oxford University

Nuttida Rungratsameetaweemana (2020) Assistant Professor, Columbia University

Thomas Sprague (2016) Leon Thal Prize – Assistant Professor, UC Santa Barbara

Corinne Teeter (2012) Member of Technical Staff, Sandia National Laboratories

Diane Whitmer (2008) Adjunct Assistant Professor, University of Texas at Austin

Business and Professional

Emily Andersen (2012) Industry

Adam Calhoun (2015) Scientist, Meta Reality Labs

Zack Cecere (2021) Scientist, Athena Ventures

Scott Cole (2018) Data Scientist, Square

Justin Elstrott (2009) Scientist, Genentech

Kyle Fischer (2019) Industry

Kate Gaudry (2006), JD (2010) Kilpatrick Townsend & Stockton LLP

Daniel Hill (2009) Senior Data Scientist, Meta, NYC

Preston Holmes (2001) Head of IoT Solutions, Google Cloud Platform

Justin Kiggins (2016) Product Manager, Chan Zuckerberg Initiative

Adam Koerner (2013) Co-founder, Drop Fake

Anastacia Kurnikova (2018) AI Engineer, Thermo Fisher Scientific

Stephen Larson (2012) Founder/CEO, Metacell

Philip Low (2007) Founder and CEO, NeuroVigil

Samar Mehta (2007), MD (2011) Beth Israel Deaconess Medical Center

David W. Matthews (2013) Boston Consulting Group

Philip Meier (2011) Co-Founder/Chief Product Officer, CleverPet

Akinori Mitani (2018) Software Engineer, Google

Micah Richert (2008) Senior Scientist, Brain Corporation

Jon Shlens (2008) Principal Scientist and Research Director, Google DeepMind

Marvin Thielk (2019) Data Scientist, Elsevier

Vy Vo (2019) Research Scientist, Intel

Postdocs and Fellows

Bassam V. Atallah (2010) Leon Thal Prize, Fellow, Champalimaud Foundation

Anupam Garg (2019), MD (2021) Johns Hopkins

Javier How (2020) Johns Hopkins University

Landon Klein (2018) Science Fellow, California Council on Science and Technology

Ethan McBride (2019) Scientist, Allen Institute

Bethanny Danskin (2020) Scientist, Allen Institute

Kathleen Quach (2020) Chalasani Laboratory, Salk Institute

Kimberly Reinhold (2015) Leon Thal Prize, Sabatini Laboratory, Harvard