computational neuroscience phd thesis

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The Computational Neuroscience specialization is a facet of the broader  Neuroscience Graduate Program at UC San Diego . The goal of the specialization is to train the next generation of neuroscientists with the analytical and computational skills that are essential to understand the organization and function of neural systems. The specialization is open to all students and may be of particular interest to students with backgrounds in physics, computer science, engineering, and mathematics.

The specialization allows Neuroscience students to concentrate on a program of rigorous course work on fundamental aspects of computational neuroscience. Students are encouraged to pursue thesis projects that include both an experimental and a computational component, possibly arranged 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.

The program is focused on these major themes relevant for computational neuroscience research:

• Cellular and Synaptic Dynamics - Anatomy, physiology, and electrical and chemical dynamics of individual neurons. Neuromorphic models.
• Biophysical Basis of Neuronal Computation - Collective properties and dynamics of neuronal systems, with emphasis on feedforward networks, associative networks, and networks of coupled oscillators.
• Algorithms for the Analysis of Neural Data - Characterization of spiking and continuous processes (ECoG, LFP, MEG, fMRI). Statistical aspects of genomics and neuroanatomy.
• Advanced Optical Tools in Quantitative Biology.
• Workshop in Electron Microscopy.
• Magnetic Resonance Imaging.

The program is currently led by David Kleinfeld (Physics and Neurobiology) and the primary teaching faculty also include Henry Abarbanel (Physics and Scripps Institution of Oceanography), Gert Cauwenberghs (Bioengineering), Eran Mukamel (Cognitive Science), Terrence Sejnowski (Salk Institute for Biological Studies and Neurobiology), Tatyana Sharpee (Salk Institute for Biological Studies and Physics) and Gabriel Silva (Ophthalmology and Bioengineering).

Application

All students admitted to the Neurosciences Graduate Program are eligible to pursue the CNS. Additionally, Ph.D. candidates in Physics and Bioengineering are also eligible to apply to the CNS. Upon completion of the CNS required coursework, a Neuroscience, Physics or Bioengineering student can apply for the specialization by completing the form below. 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 Computation Neuroscience Committee Chair, Dr. David Kleinfeld at [email protected] .

Upon achievement of degree requirements, students will receive a diploma indicating both their successful completion of their Ph.D. program as well as their specialization in Computational Neuroscience.

Thesis Research

All CNS students are expected to complete a Ph.D. dissertation connected with an issue in contemporary computational neuroscience. Either the student's primary advisor or close co-advisor (approved by the Computational Neuroscience Committee) must be a member of the Neuroscience Graduate Program faculty . 

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Emory University Laney Graduate School Neuroscience Graduate Program

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• Computational Neuroscience

Seeking a degree in Computational Neuroscience?

The graduate program in Neuroscience at Emory University is an interdisciplinary program, spanning many departments and priding itself on a collaborative atmosphere encouraging excellence. Our faculty and students have a broad scope of research interests within neuroscience, ranging from molecular to cellular to behavioral neuroscience.

Our program is one of eight Ph.D. programs that comprise the Emory Graduate Division of Biological and Biomedical Sciences (GDBBS). There are over 260 faculty members in the Division, and graduate students of any program in the Division face no departmental barriers. They can do laboratory rotations and research with any of the Division Faculty. The can also switch to one of the other programs, have an advisor from another program and take any course offered by the Division. This structure gives students tremendous flexibility in choosing coursework, advisors and research plans. There are currently 98 students enrolled in the program, and the average time to finish the degree is about 5.5 years.

Joint Degree Programs

• Neuroscience/Biomedical Engineering In 1997, Emory University and the Georgia Institute of Technology joined forces to create a joint Biomedical Engineering Department , which includes 15 Emory faculty members (4 from Neuroscience), and over 25 faculty members from Georgia Tech. The collaboration provides enormous opportunity and intellectual resources for students interested in neuroengineering, neuronal modeling, computational neuroscience and other cutting-edge challenges. Emory has eleven faculty that participate in this joint effort. The Chairman of the new department, Dr. Don Giddens, headed the Johns Hopkins College of Engineering before coming to Emory.
• MD/PhD Approximately 10-15% of Neuroscience graduate students are working towards an M.D. degree or already have one. MD/PhD students are admitted through the MD/PhD Program .

Financial Aid

In addition, all students must participate in weekly seminars for the first 2 years of the program. These seminars are informal venues where students present either relevant papers or their own research to their peers and a small group of faculty members. From this experience, students learn presentation and communication skills essential to a career in science.

Related Terms

• Behavioral Neuroscience
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• Molecular Neuroscience
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• Neuropharmacology Research
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Information & Guides

• Prospective NS Students
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Neuroscience Institute

Ph.d. in neural computation courses and activities.

The program consists of the following core activities:

• Coursework in computational neuroscience, quantitative methodologies and experimental neuroscience
• Exposure to experimental approaches through rotations or thesis research
• Training in teaching, scientific presentations and responsible conduct of research
• Successful defense of a Ph.D. Thesis

Additional satellite activities through the CNBC will also foster students’ professional and scientific development.

Course requirements

The coursework is designed to ensure that students are well trained in neuroscience and that they also receive in-depth training in a set of quantitative approaches relevant to the field of neural computation.

A typical student will take 2-3 courses per term in their first year and complete all coursework by the end of their third year in the program. Because of differences in background and educational goals, course requirements for each student in the program will be adapted to their individual needs.

CNBC Core Course Requirement

Students complete the four-course requirement of the CNBC graduate training program , with specific program related guidelines for the computational component. Students will gain graduate level training through coursework in the following three areas: (i) cell and molecular neuroscience/neurophysiology, (ii) systems neuroscience, and (iii) cognitive neuroscience. Courses fulfilling this requirement include

• (i) 03-762 Advanced Cellular Neuroscience (CMU) or NROSCI 2100/2101 Cellular and Molecular Neurobiology (Pitt)
• (ii) 03-763 Systems Neuroscience (CMU) or NROSCI 2102 Systems Neuroscience (Pitt), and
• (iii) 85-765 Cognitive Neuroscience.

Computational Neuroscience

Students are required to take at least three computational neuroscience courses, including mathematical, statistical and computational approaches.

To complete the computational requirement, students must take:

• 36-759 Statistical Models of the Brain (CMU)
• Two additional computational electives selected by the student.

Recommended courses fulfilling the computational elective requirement include:

• 10-733 Representation and Generation in Neuroscience and AI (CMU)
• 15-686 Neural Computation (CMU)
• 15-883 Computational Models of Neural Systems (CMU)
• 18-698/42-632 Neural Signals Processing (CMU)
• 85-719 Introduction to Parallel Distributed Processing (CMU)
• 86-631 Neural Data Analysis (CMU)
• 86-675 Computational Perception (CMU)
• BIOENG 2650 Mathematical Models of Biological Learning (Pitt)
• MATH 3370 Mathematical Neuroscience (Pitt)

Quantitative Methods

Students must take at least two graduate level courses in one quantitative subject (e.g. math, computer science or statistics) to ensure depth of knowledge in this area. Courses listed above under the Computational Neuroscience requirement are not eligible to fulfill this requirement. Under the quantitative methods requirement, we have identified two examples of focus areas:

Dynamical Systems focus

• MATH 2940 Applied Stochastic Methods (PITT)
• MATH 2950 Applied Math Methods (PITT)

Statistics and Machine Learning focus

• 10-701 or 10-715 Machine Learning (CMU)
• 36-705 Intermediate Statistics (CMU)
• 36-707 Regression Analysis (CMU)

Other foci, including “brain imaging and signal processing” have been discussed and may be added as recommended course sets, subject to approval by the program co-directors. Note that to be eligible to take certain of these course, students might first need to complete course pre-requisites. These pre-requisites would not count towards the two course depth requirement.

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Program Milestones

Progress in the program is tracked based in part on students’ successful completion of program milestones. A committee selected by the student and approved by the program director evaluates the performance on milestones. 

First year research requirement

By the end of the first calendar year in the program, all students are required to complete a computational project. This project will be evaluated by a committee consisting of at least three faculty, of whom at least two are PNC training faculty. The project requires the student to identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases this project may be a precursor to the student’s eventual thesis project.

Second year research requirement

In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer .   By the end of the second full year in the program all students are required to complete a deeper computational project. The student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication .

Ph.D. Thesis proposal

Required coursework should be completed by the end of the third year. During the fourth year a Ph.D. candidate should present a thesis proposal to his or her thesis committee and the community.  The thesis proposal should include: a succinct summary of the proposed research problem; the significance of the proposed research; a review of relevant literature relating to the problem; a review of the candidate’s work leading up to the thesis, including preliminary results; a clear statement of remaining research; and a tentative schedule for completing the work.

Ph.D. Thesis Defense

Normally, the dissertation is completed during the student’s fifth year. The thesis defense is your chance to present the culmination of all your work and research to the neuroscience community and field questions from your thesis committee.

Additional Requirements

• All students will complete Responsible Conduct of Research Training.
• In order to build skills in teaching, mentoring, communication and management, each student will be required to serve as a teaching assistant for two courses during their career as a graduate student in the program.
• Students will participate with CNBC graduate training students in the following co-curricular activities: CNBC colloquium series; Brain Bag research seminars; and CNBC retreat.

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• Review Article
• Published: 20 August 2018

Cognitive computational neuroscience

• Nikolaus Kriegeskorte 1 &
• Pamela K. Douglas 2  

Nature Neuroscience volume  21 ,  pages 1148–1160 ( 2018 ) Cite this article

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• Cognitive neuroscience
• Computational neuroscience

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.

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Acknowledgements

This paper benefited from discussions in the context of the new conference Cognitive Computational Neuroscience, which had its inaugural meeting in New York City in September 2017 120 . We are grateful in particular to T. Naselaris, K. Kay, K. Kording, D. Shohamy, R. Poldrack, J. Diedrichsen, M. Bethge, R. Mok, T. Kietzmann, K. Storrs, M. Mur, T. Golan, M. Lengyel, M. Shadlen, D. Wolpert, A. Oliva, D. Yamins, J. Cohen, J. DiCarlo, T. Konkle, J. McDermott, N. Kanwisher, S. Gershman and J. Tenenbaum for inspiring discussions.

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Kriegeskorte, N., Douglas, P.K. Cognitive computational neuroscience. Nat Neurosci 21 , 1148–1160 (2018). https://doi.org/10.1038/s41593-018-0210-5

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Welcome to the Graduate Program

Think of the Neuroscience Training Program at Johns Hopkins as an expedition, where you will search the frontiers of science for discoveries that explain the inner workings of the nervous system.

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The Neuroscience Training Program and the Neuroscience Department were among the first neuroscience-focused academic centers established in the United States, dating back to 1980. Our faculty have trained over 250 PhD and MD/PhD students and 500 postdoctoral fellows in just the past ten years, partnerships that have led to fundamental discoveries in the organization of the cerebral cortex, neurotransmitter signaling, neuronal and glial cell development, and circuit function. 

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There has never been a more exciting time in the field of neuroscience. We hope you will join us in this journey of discovery.

Gatsby Computational Neuroscience Unit MPhil/PhD

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The Gatsby Unit PhD programme was the first to combine theoretical neuroscience and machine learning within the same programme. Our mathematical approach for developing novel algorithms and tools to understand learning, perception and action in brain and machines is unique.  Applications to this programme must be submitted directly to the Gatsby Unit via its online portal.

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Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.

The English language level for this programme is: Level 3

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

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Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

The Gatsby Unit is a world-class research centre for Computational and Theoretical Neuroscience and Machine Learning. Our research seeks to understand the principles of learning, perception and action in brains and machines by developing mathematical algorithms. We provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with other UCL research groups, in particular the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC) as well as the Centre for Computational Statistics and Machine Learning (CSML). Teaching is supplemented with regular research talks, journal clubs and reading groups, external seminar series and participation in relevant conferences and workshops. PhD students will be supported by all academic staff, not just their immediate supervisors.

Who this course is for

Applicants should have a strong analytical and mathematical background, a keen interest in neuroscience and/or machine learning, and a first degree in relevant fields such as Computer science, Engineering, Physics, Mathematics, Statistics, Neuroscience, or Cognitive Psychology. Many applicants to our programme will have a Master’s degree, but this is not essential. Each application is assessed on its individual merits. Candidates offered a place on our programme will be required to meet UCL's standard admissions requirements (including the English language proficiency requirements for international applicants).

What this course will give you

Students at the Gatsby Computational Neuroscience Unit study toward a PhD in Theoretical Neuroscience and Machine Learning. The Gatsby Unit is part of the Centre for Computational Statistics and Machine Learning, together with UCL Computer Science and Statistical Science. Approximately 90% of alumni have secured academic or industry positions. Many of the most successful leaders in the fields of computational neuroscience and machine learning have studied or worked in the unit, and the unit has a reputation for offering world-class training.

Our programme offers first-year teaching in Systems and Theoretical Neuroscience (jointly with SWC), Probabilistic and Unsupervised Learning, Approximate Inference and Learning in Probabilistic Models, Theoretical Neuroscience, and Advanced Methods in Machine Learning. In your second year, you will carry out a 2-month rotation project in a lab of your choice, either at or external to UCL. The rotation project should be on a research area not related to your PhD topic. Our course also offers a variety of career and skills development training. 

We are based in a purpose-designed building in the heart of London and UCL. The building also houses the SWC, with which we interact closely. Our building offers two large seminar rooms, common areas for social interaction or quiet study, high-performance computing facilities, kitchen facilities, and a brasserie offering breakfast, lunch, coffee and snacks. There are also two large outdoor areas and an on-site bicycle rack. The surrounding area is vibrant with a variety of shops, restaurants, cafes and bars, and museums.

The foundation of your career

Students receive world-class training through our intense, rigorous and diverse research programme. As a result, the majority are highly employable. Students will develop interdisciplinarity and will be expected to collaborate widely. You will develop your communication skills by giving regular internal talks as well as writing scientific publications. Critical thinking is further developed through weekly journal clubs and lab meetings. You will be expected to teach during the second year of the programme and act as a mentor to junior PhDs. Participation in outreach and public engagement activities is also encouraged.

Employability

Most of our graduates have continued in the fields of computational neuroscience and/or machine learning. Alumni have secured academic positions in prestigious institutions such as Cambridge, Oxford, Edinburgh, Columbia, Princeton, Caltech, École Normale Supérieure, the Max Planck Institute for Intelligent Systems and Janelia Research Campus, or have gone on to work in companies such as Google DeepMind, Amazon, Facebook, Samsung and Babylon Health.

There are many networking opportunities, both within and external to the Gatsby Unit. Within the unit, there are daily tea hours where members take turns to present their research or a 10-minute talk on an interesting topic to the unit. Our external seminar series provides an opportunity to meet and interact with eminent speakers and external attendees. 

Based in the same building as the SWC, students will have the opportunity to network extensively with experimentalists and to attend SWC seminars, lab meetings and joint social events. Through CSML you will network with peers in Computer Science and Statistics. All students are encouraged to attend relevant conferences and workshops.

Teaching and learning

The four-year PhD programme includes in its first year intensive courses that provide a comprehensive introduction to theoretical and systems neuroscience and machine learning:

• Systems Neuroscience and Theoretical Neuroscience
• Probabilistic and Unsupervised Learning (COMP0086)
• Approximate Inference and Learning in Probabilistic Models (COMP0085)
• Advanced Topics in Machine Learning (COMP0083)
• Reinforcement Learning (COMP0089)

Students will also be required to carry out a 2-month rotation project in a field different from the field of their PhD thesis.

We offer a supportive and interdisciplinary environment with close links to the SWC and the ELLIS Unit at UCL. For more details see https://www.ucl.ac.uk/gatsby/study-and-work/phd-programme/programme-structure.

Students are required to complete coursework for all first-year courses. There are three examinations in the first year of study.

Typical contact hours are 9am - 5pm.

Research areas and structure

• Analysis of neural data
• Neural dynamics
• Neural plasticity
• Perceptual processing of auditory and visual input
• Neural population coding
• Kernel methods
• Bayesian statistics
• Reinforcement learning
• Statistical machine learning
• Unsupervised learning
• Network and relational data

Research environment

The Gatsby Computational Neuroscience Unit was established by the Gatsby Charitable Foundation at UCL in 1998 to provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with other UCL research groups in neuroscience, machine learning and related areas. We are one of the first centres in the world to bring together the fields of theoretical neuroscience and machine learning, and our investigators have pioneered  research  into the mathematical underpinnings of learning, perception and action in natural and artificial systems.

In 2016 we moved from our original home in Queen Square to Fitzrovia to create a new collaborative partnership with the  Sainsbury Wellcome Centre for Neural Circuits and Behaviour  (SWC). The SWC and the Gatsby Unit work together closely, with joint research appointments, parallel PhD programmes, common day-to-day activities, and joint research projects that bring together theoretical and experimental neuroscience. We are also part of the cross-faculty Centre for Computational Statistics and Machine Learning (CSML) and work with other UCL research groups in neuroscience  and machine learning . 

There are many networking opportunities both within and external to the Gatsby Unit. Within the unit, there are regular talks where members take turns to give research talks to the rest of the unit. The unit also holds regular seminars to which eminent speakers are invited; these are open to other members of the university, and there is an opportunity to meet speakers and attendees after the talk. Students also run journal clubs and their own reading groups, and all students have the opportunity to attend relevant conferences and workshops.

The PhD programme lasts for four years, including first-year teaching  in techniques and research in theoretical and systems neuroscience and machine learning. We only admit new students starting in September.

Courses in the first year, taught with colleagues from the SWC and CSML, provide a comprehensive introduction to theoretical and systems neuroscience and to machine learning; with multidisciplinary training in other areas of neuroscience also available. Students are encouraged to work and interact closely with peers and faculty in the SWC and CSML throughout their PhD to take advantage of this uniquely multidisciplinary research environment.

In Year 1, students take core courses in theoretical neuroscience (TN), systems neuroscience (taught with SWC) and machine learning (ML), after which they generally choose to concentrate on either TN or ML. Students sit exams for TN and ML during the second (third) term. 

In Year 2, students are expected to work on their thesis project. Students also do a 2-month rotation in a field that is not related to their PhD thesis. At the end of the second year, students are expected to write an MPhil/PhD upgrade report on their progress and future plans and schedule to give a presentation on this report to the rest of the unit. Having passed all the required assessments, students then transfer from MPhil to PhD status and devote their remaining tenure to research. 

Throughout the PhD programme, students are immersed in a highly stimulating educational environment, comprising regular talks, research reports, journal clubs and ad hoc reading groups; seminar series in the unit and other UCL departments (including  SWC ;  Institute of Cognitive Neuroscience ;  Institute of Neurology , Department of Psychology ;  Neuroscience, Physiology and Pharmacology ;  the ELLIS Unit at UCL ; and  Department of Statistical Science ), and participation in international conferences such as COSYNE, ICML and NeurIPS.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

There are no programme-specific additional costs.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

A number of fully-funded studentships are available each year. Each studentship covers payment of full fees, provides a generous tax-free living stipend and a travel budget for conference/workshop attendance. Studentships are available to students of any nationality.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

All applications must be made directly to the Gatsby Unit by the deadline stated on the Gatsby Unit website. Please refer to this web page on the Gatsby Unit website for how to apply to this programme. Late applications will be held until all applications received by the deadline have been assessed. It is very likely that all available positions will be filled by then.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

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PhD in Neuroscience (Program in Neuroscience)

The Graduate School of Arts & Sciences (GRS) no longer admits students to the PhD offered by the Program in Neuroscience (PIN). Students are now admitted into the Graduate Program for Neuroscience (GPN) , a University-wide effort administered by the Division of Graduate Medical Sciences (GMS). The following is provided as reference only for continuing GRS Neuroscience PhD students in PIN.

The goal for the majority of students will be to complete the requirements for elective study and the prescribed milestones for moving toward submission of the written thesis and the oral defense. This includes successful completion of the qualifying exam and biannual meetings of the dissertation advisory committee (DAC). Oversight of PIN students is now being conducted by the Graduate Education Committee of GPN. Please contact the GPN office for further information or guidelines.

For the post–bachelor’s PhD degree, a total of 64 course credits is required. Of these, at least 32 of the credits must come from lecture, methods, or seminar courses.

For the post–master’s PhD, 32 credits of coursework are required. The required courses are the same as for the post–bachelor’s PhD. However, depending on the student’s background, one or more of the required courses may be waived via petition to the BU Graduate Program for Neuroscience Graduate Education Committee (GEC).

Core Courses

An essential feature of the program is a set of core courses: these are taken by all students in GPN during their first year and are aimed at developing a community of thinkers who move through the training program together, building relationships that cross departmental and campus barriers, and foster cross-disciplinary collaborations.

Students complete 10 credits of core neuroscience coursework that provides a strong foundation in this diverse field of graduate study. There are two team-taught lecture/discussion courses that are given sequentially over the first year. Each course has two directors, each from a different campus. Courses are cross-listed to represent the cosponsorship of departments from both campuses (Charles River Campus and Medical Campus). GPN students register in the following:

• GRS BI 755 Cellular and Systems Neuroscience or GMS AN 810 Systems Neurobiology (4 cr)
• GRS BI 756/GRS PS 738 Systems and Behavioral Neuroscience or GMS AN 811 Cognitive Neuroscience (4 cr)

Additional core neuroscience requirements (2 cr) include: a 7-week intensive introductory course in data analysis and mathematical models for students who do not have a strong background in computation. This introductory course combines lectures and hands-on computer time to treat real laboratory data like case studies and motivates students to use the mathematical approach as a means to better understand their own research via statistical data analysis and modeling.

• GRS MA 665 Introduction to Mathematical Models and Data Analysis in Neuroscience (2 cr)

Students pursuing the PhD in Computational Neuroscience (or who have taken an undergraduate course in the area) can substitute a more advanced elective for this requirement. Likewise, students who have taken the required course and would like more exposure to the area can continue on in the class to take the next module that is offered sequentially (4 cr instead of 2 cr).

Additional Required Curriculum

In addition to the core curriculum, students take the following seminar coursework during their first year and enroll in laboratory rotations:

• GRS NE 500/501 Frontiers of Neuroscience (2 cr per semester)

All students attend a unique class on Friday afternoons that is managed by the Director or Associate Director of GPN. During the first semester, students are assigned key papers from a particular BU faculty member’s laboratory and supporting manuscripts in the field. During the session, student presenters review and critique experimental findings and approaches, building their skills in critical thinking and developing the basic tools for successful oral presentations. They also get to share their scientific ideas and interests with the leaders of neuroscience at Boston University, an activity that enriches the neuroscience community by building new relationships between faculty and students. Research from monthly GPN distinguished lecturers from across the world are integrated into the training experience to provide a balanced exposure for students to all areas of neuroscience and to give them firsthand interactions with exceptional individuals who are defining the field of the future. During the second semester, students focus on the development of professional skills, such as advanced techniques in oral presentations and the beginnings of scientific writing.

Laboratory Rotations

Providing an enriching set of laboratory research experiences directed by GPN faculty for students during their first year is a central feature of the graduate training program at Boston University. The multitude of highly talented mentors who have funded research projects provides the student with a large number of potential laboratories from which to choose their thesis research mentor that will complement their current interests, and through laboratory rotations, expand their horizons into different areas of investigation that they may grow toward in the future.

The majority of students pursuing the PhD in Neuroscience take three to four rotations, with at least one rotation in an area outside of their initial research interests; students pursuing the PhD in Computational Neuroscience take a minimum of two rotations, with at least one in an experimental laboratory. Students can also engage in additional rotations should they not find a mentor, or if they would like more exposure to other methodologies used in neuroscience. Credit for rotations is contingent upon receipt of a short (5-page) laboratory report for each experience that is reviewed by the rotation mentor.

Directed Study/Thesis

Additional program credits come from directed study during thesis research to make up the 64-credit PhD requirement. Students are also encouraged to take an additional course in probability and statistics that is appropriate to their area of thesis research and take required workshops in neuroscience ethics and responsible conduct of research. Students also attend the required bimonthly student seminar series, a graduate student forum where they can work on improving their oral communication skills throughout their graduate career in GPN. Finally, to be in good standing in the program, they must attend all GPN distinguished lectures and program events, workshops in professional development, and participate in at least one teaching or outreach activity. Optional paid teaching fellowships are available to students who want greater experience in the educational mission.

Students can also substitute additional coursework for directed study to make up the credit requirement for the degree, especially as needed based upon their choice of thesis research or to supplement a lack of certain background during undergraduate study.

Hands-On Laboratory Experience

During the summer before their first year of study, incoming students participate in the Tools of the Trade laboratory experience that is run by GPN faculty to provide students with the essential hands-on experience necessary to make their laboratory rotations in the fall meaningful for their graduate-level training. In Tools of the Trade, students learn some of the basic techniques necessary for conducting laboratory research in the field of neuroscience, independent of their current research interests.

Group activities might be organized around detection of an important neuronal RNA via real-time PCR, the identification of a single nucleotide polymorphism in a DNA sample from a patient with a neurodegenerative disease, identification of protein in brain slices using immunohistochemistry and fluorescence microscopy, electrophysiological measurements or calcium imaging of living neurons, interaction of transcription factors with DNA regulatory elements that control expression of neural-specific genes, neuroimaging of the brain to detect the activation of particular brain structures, and running of a behavioral task with animals to address questions of addiction or learning and memory. Projects vary with the expertise and interests of the participating GPN faculty. Tools of the Trade is coordinated with the university-wide Neuroscience Retreat.

Elective Study

The rest of the formal credits toward the PhD in Neuroscience come from a minimum of 12 credits of elective study that includes at least one course with clinical relevancy. For example, students with a background in molecular neuroscience (or equivalent biochemical/biological study) can take the following electives:

• GMS MS 783 Molecular Basis of Neurological Disease (2 cr)
• GMS PM 701 Molecular Neurobiology and Pharmacology (2 cr)

These advanced courses provide the focus for an understanding of how endogenous substances act in the brain, the challenges faced in the development of effective therapies that target the nervous system, and what molecules can tell us about disease etiology and the potential for future treatment.

Taking advantage of the translational research and history of clinical training at the Medical Campus, all students are required to participate in clinical rounds that are offered through the Boston VA hospital as well as the Alzheimer’s Disease Center. Options for clinically relevant courses specific to students with interests outside of molecular neuroscience cover topics ranging from neuropsychology to human imaging and neural engineering, as well as the rehabilitative sciences offered at BU’s Sargent College of Health & Rehabilitation Sciences.

Please see specific details for the PhD in Computational Neuroscience on the website as there is additional required coursework for this degree specialization.

Suggestions for elective study are provided by faculty curriculum committees in subdisciplines of neuroscience as a means to help guide students toward reaching their scholastic goals.

CURRENT OPTIONS FOR ELECTIVE STUDY

As a member of GPN, students will acquire their more advanced training from coursework offered in departments around the University in order to fulfill the credit requirements for the PhD degree. The following is a list of potential electives organized by topic area as a guide to help students choose their curriculum and to give them flexibility in the design of coursework that spans more than one area of interest. Suggestions on foundation coursework specific to a research area are updated by faculty curriculum committees and can be obtained by emailing [email protected] .

Relevant to Molecular, Cellular & Systems (see also Computational)

• CAS BI 520 Sensory Neurobiology (4 cr)
• CAS BI 545 Neurobiology of Motivated Behavior (4 cr)
• CAS BI 599 Neurobiology of Synapses (4 cr)
• CAS PS 530 Neural Models of Memory Function (4 cr)
• GMS AN 702 *Neurobiology of Learning and Memory (2 cr)
• GMS AN 709 *Neural Development and Plasticity (2 cr)
• GMS AN 804 *Methods in Neuroscience (4 cr)
• GMS AN 807 *Neurobiology of the Visual System (2 cr)
• GMS BN 798 *Functional Neuroanatomy in Neuropsychology (4 cr)
• GRS BI 644 Neuroethology (4 cr)
• GRS BI 645 Cellular and Molecular Neurophysiology (4 cr)
• GRS BI 655 Developmental Neurobiology (4 cr)
• GRS BI 681 Molecular Biology of the Neuron (4 cr)
• SAR HS 550 Neural Systems (4 cr)
• SAR HS 755 Readings in Neuroscience (4 cr)

Relevant to Biomedical & Translational

• CAS BI 554 Neuroendocrinology (4 cr)
• GMS AN 707 *Neurobiology of Aging (2 cr)
• GMS AN 713 *Autism: Clinical and Neuroscience Perspectives (2 cr)
• GMS AN 808 *Neuroanatomical Basis of Neurological Disorders (2 cr)
• GMS BN 782 *Forensic Neuropsychology (4 cr)
• GMS BN 793 *Adult Communication Disorders (4 cr)
• GMS BN 796 *Neuropsychological Assessment I (4 cr)
• GMS BN 797 *Neuropsychological Assessment II (4 cr)
• GMS BN 821 *Neuroimaging Seminar (2 cr)
• GMS BN 891 & 892 *Case Studies in Neuropsychology (three different clinical rounds, sections A1, B1, and C1) (2 credits each section)
• GMS BN 893 *Child Clinical Neuropsychology (4 cr)
• GMS IM 690 *Imaging of Neurologic Disease (2 cr)
• GMS PM 801 *Systems Pharmacology and Experimental Therapeutics I (2 cr)
• GMS PM 802 *Systems Pharmacology and Experimental Therapeutics II (2 cr)
• GMS PM 820 *Behavioral Pharmacology (2 cr)
• GMS PM 840 *Neuroendocrine Pharmacology (2 cr)
• GMS PM 843 *Pharmacologic Intervention in Inflammatory Responses (2 cr)
• GMS PM 850 *Biochemical Neuropharmacology (2 cr)
• GMS PM 881 *Drug Discovery and Development (2 cr)

Behavioral & Cognitive Neuroscience

• CAS PS 520 Research Methods in Perception and Cognition (4 cr)
• CAS PS 525 Cognitive Science (4 cr)
• CAS PS 528 Human Brain Mapping (4 cr)
• CAS PS 544 Developmental Neuropsychology (4 cr)
• CAS PS 737 Memory Systems of the Brain (4 cr)
• CAS PS 821 Learning (4 cr)
• ENG BE 715 Functional Neuroimaging (4 cr)
• GMS AN 716 *Developmental Cognitive Neuroscience (4 cr)
• GMS BN 795 *Neuropsychology of Perception and Memory (4 cr)
• GRS PS 721 General Experimental (4 cr)
• GRS PS 734 Psychopharmacology for the Behavioral Scientist (4 cr)
• GRS PS 738 Techniques in Systems & Behavioral Neuroscience (4 cr)
• GRS PS 822 Visual Perception (4 cr)
• GRS PS 824 Cognitive Psychology (4 cr)
• GRS PS 828 Seminar in Psycholinguistics (4 cr)
• GRS PS 829 Principles in Neuropsychology (4 cr)
• GRS PS 831 Seminar in Neuropsychology (4 cr)
• GRS PS 835 Attention (4 cr)

Theoretical & Computational Neuroscience

• CAS CN 500 Computational Methods in Cognitive and Neural Systems (4 cr)
• CAS CN 510 Principles and Methods of Cognitive and Neural Modeling I (4 cr)
• CAS CN 520 Principles and Methods of Cognitive and Neural Modeling II (4 cr)
• CAS CN 530 Neural and Computational Models of Vision (4 cr)
• CAS CN 540 Neural and Computational Models of Adaptive Movement and Planning Control (4 cr)
• CAS CN 550 Neural and Computational Models of Recognition, Memory, and Attention (4 cr)
• CAS CN 560 (colisted as BE 509) Neural and Computational Models of Speech and Hearing (4 cr)
• CAS CN 570 Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm (4 cr)
• CAS CN 580 Introduction to Computational Neuroscience (4 cr)
• ENG BE 509 (colisted as CN 560) Quantitative Physiology of the Auditory System (4 cr)
• ENG BE 570 Introduction to Computational Vision (4 cr)
• ENG BE 701 Auditory Signal Processing: Peripheral (4 cr)
• ENG BE 702 Auditory Signal Processing: Central (4 cr)
• ENG BE 707 Quantitative Studies of Excitable Membranes (4 cr)
• ENG BE 710 Neural Plasticity and Perceptual Learning (4 cr)
• GRS CN 700 Computational and Mathematical Methods in Neural Modeling (4 cr)
• GRS CN 710 Advanced Topics in Neural Modeling: Comparative Analysis of Learning Systems (4 cr)
• GRS CN 720 Neural and Computational Models of Planning and Temporal Structure in Behavior (4 cr)
• GRS CN 730 Models of Visual Perception (4 cr)
• GRS CN 740 Topics in Sensory Motor Control (4 cr)
• GRS CN 760 Topics in Speech Perception and Recognition (4 cr)
• GRS CN 780 Topics in Computational Neuroscience (4 cr)
• GRS CS 640 Artificial Intelligence (4 cr)

Coursework in Related Disciplines

• CAS BI 551 Biology of Stem Cells (4 cr)
• CAS BI 552/553 Molecular Biology (4 cr, 4 cr)
• CAS BI 555 Techniques in Cell Biology (4 cr)
• CAS BI 556 Membrane Biochemistry and Cell Signaling (4 cr)
• CAS MA 565 Math Models in the Life Sciences (4 cr)
• CAS MA 573 Qualitative Theory of Ordinary Differential Equations (4 cr)
• CAS MA 581 Probability (4 cr)
• CAS MA 582 Mathematical Statistics (4 cr)
• CAS MA 583 Introduction to Stochastic Processes (4 cr)
• CAS MA 584 Multivariate Statistical Analysis (4 cr)
• CAS MA 585 Time Series and Forecasting (4 cr)
• CAS MA 684 Applied Multiple Regression and Multivariable Method (4 cr)
• ENG BE 515 Introduction to Medical Imaging (4 cr)
• ENG BE 540 Bioelectrical Signals: Analysis and Interpretation (4 cr)
• ENG BE 550 Bioelectromechanics (4 cr)
• ENG BE 560 Biomolecular Architecture (4 cr)
• ENG BE 561 DNA and Protein Sequence Analysis (4 cr)
• ENG BE 700 Advanced Topics in Biomedical Engineering (4 cr)
• ENG BE 740 Parameter Estimation and Systems Identification (4 cr)
• ENG BE 747 Advanced Signals and Systems Analysis for Biomedical Engineering (4 cr)
• GMS BI 782 *Molecular Biology (4 cr)
• GMS BI 789 *Physical Biochemistry (2 cr)
• GMS BI 776 *Gene Targeting in Transgenic Mice (2 cr)
• GMS BI 786 *Biochemical Mechanisms of Aging (2 cr)
• GMS BI 797 *Molecular Mechanisms of Growth and Development (2 cr)
• GMS BL 755/756 *Biochemistry (4 cr, 4 cr)
• GMS MI 713 *Comprehensive Immunology (4 cr)
• GMS MM 701 *Genetics and Epidemiology of Human Disease (2 cr)
• GMS MM 703 *Cancer Biology and Genetics (2 cr)
• GMS MM 710 *Molecules to Molecular Therapeutics: The Translation of Molecular Observations to Clinical Implementation (4 cr)
• GMS PM 832 *Pharmacogenomics (2 cr)
• GMS PM 880 *Gene Regulation and Pharmacology (2 cr)
• GRS BI 621/622 Biochemistry (4 cr, 4 cr)
• GRS BI 735 Advanced Cell Biology (4 cr)
• GRS MB 721 Graduate Biochemistry (4 cr)
• GRS MB 722 Advanced Biochemistry (4 cr)
• MET AD 893 Technology Commercialization: From Lab to Market (4 cr)

Qualifying Examination

Students must take their Qualifying Examination at the end of their second year of study to remain in good standing in the program. The Qualifying Examination tests the ability of the student to think experimentally by generating testable hypotheses based on a foundation of knowledge that can be communicated in a written document and defended orally in front of a committee of GPN members. It is recommended that at least one faculty member of the Qualifying Examination Committee be from the GPN faculty of a different campus than where the student is conducting their dissertation research. For instance, a student working in a laboratory at the Charles River Campus would also have a GPN faculty member on the examining committee from the Medical Campus. A minimum of three faculty members will be on the examining committee, with membership approved by the GEC (see above).

The exam will be structured around a written proposal that is in the form of an individual NIH training fellowship application within the student’s proposed area of thesis research as well as a written abstract and Specific Aims in an additional area to test the student’s ability to address topics outside of their comfort zone. Students will be given a small workshop run by faculty so that they first learn the basic structure of an NRSA-style fellowship application before beginning their writing exercise for the exam. During the oral exam, the examining committee evaluates the student’s core knowledge base in these two areas as well as basic neuroscience. Students must be able to propose and defend experimental and/or computational hypotheses with realistic approaches to answer key questions relevant to their Specific Aims. A successful completion of both the written and oral parts of the exam is necessary before advancing to doctoral candidacy and registering for the third year in the program.

Dissertation

Dissertation advisory committee (dac).

At the end of the second year, after successful completion of the Qualifying Exam, each student and the research mentor will put together a dissertation advisory committee (DAC) of five members that contains at least two faculty members from GPN and at least one member from outside of BU to serve as an outside reader. Composition of the DAC must be approved by the Program Director/Associate Director after review with the GEC. The thesis research mentor serves as the first reader. All students make a presentation to their DAC once a semester to get feedback on their progress, although not all members of the committee need to be present (a quorum of three is acceptable). Distant DAC members may participate by teleconference with the approval of the DAC chair. Evaluation of student progress by the DAC is recorded on a tracking sheet by the DAC chair (a GPN faculty member who is not the mentor) and is placed in the student’s file for future review.

Dissertation Prospectus and Progress Report Seminar

Students generate a written document of no more than 20 pages double-spaced (Dissertation Prospectus) that describes the Aims of their thesis research, its background and significance to the field, experimental design and methods, and a bibliography. They then make a formal oral presentation (Progress Report Seminar) to the neuroscience community that is followed by a meeting of the DAC, and approval to pursue the research direction is sent to the GEC for further administrative processing. This formal progress report occurs at least one year prior to the defense.

Predefense and Defense

A predefense meeting of the DAC occurs at least two to three weeks prior to the defense to make sure that the quality of the dissertation document is close to being acceptable for the degree and to review necessary paperwork. At this time, the committee reviews the abstract and title. The defense cannot be scheduled before the abstract is approved and copies sent to the GMS at least three weeks before the defense. At the time of their defense, students give a 50-minute oral presentation, followed by 10 minutes of questions, that is open to all members of the University. This public forum is followed by a closed session of the DAC, at which the student is asked to respond to questions put forth by the committee to test their ability to defend the work presented in the dissertation document.

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Computational neurosciences

Parent category, graduate degree programs in computational neurosciences, doctor of philosophy in neuroscience (phd).

The Graduate Program in Neuroscience strives to educate and support graduate students as they expand the breadth and depth of their knowledge about the brain through enriching research experiences. The program embraces principles of equity, diversity and inclusion and recognizes and accommodates...

Master of Science in Neuroscience (MSc)

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Computer Science > Computational Complexity

Title: the computational lens: from quantum physics to neuroscience.

Abstract: Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numerically simulate their models and analyze massive datasets. This technological breakthrough led to the emergence of many sub-disciplines bearing the prefix "computational" in their names. Currently, we are in the midst of the second wave, marked by the remarkable advancements in artificial intelligence. From predicting protein structures to classifying galaxies, the scope of its applications is vast, and there can only be more awaiting us on the horizon. While these two waves influence scientific methodology at the instrumental level, in this dissertation, I will present the computational lens in science, aiming at the conceptual level. Specifically, the central thesis posits that computation serves as a convenient and mechanistic language for understanding and analyzing information processing systems, offering the advantages of composability and modularity. This dissertation begins with an illustration of the blueprint of the computational lens, supported by a review of relevant previous work. Subsequently, I will present my own works in quantum physics and neuroscience as concrete examples. In the concluding chapter, I will contemplate the potential of applying the computational lens across various scientific fields, in a way that can provide significant domain insights, and discuss potential future directions.

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Degree Programme Table: Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology (PhD) (Full-time) (PRPHDINFMT1F)

Year 1 academic year: 2024/25, starting in: september.

There are no compulsory courses in this year of this programme.

PhD thesis in cognitive neuroscience (M/F)

Job Information

Offer description.

The successful candidate will join the Center de Recherche de Cerveau et Cognition (CerCo) in a dynamic research institute in Toulouse, a thriving city in the south of France. CerCo is a research center created by the National Center for Scientific Research (CNRS) and Paul Sabatier University, made up of more than 30 researchers and around 40 doctoral and post-doctoral students. The candidate will join the NeuroAI team, with a strong focus on computational neuroscience and artificial intelligence. The team has access to different experimental facilities, such as EEG, TMS and fMRI, as well as computing resources (clusters and local GPUs).

The project is part of an ERC starting grant investigating the role of oscillatory traveling waves in cognitive functions. Our primary goal is to study neural oscillations within and between cortical regions, as well as cortical-thalamic interactions. Our approach's novelty consists of framing the model in the light of Predictive Coding principles, using a computational approach based on spiking and deep neural networks. We'll compare the results of the simulations against experimental recordings of human participants (mostly EEG) to validate and assess the model's predictions.

Requirements

Additional information.

Please provide a transcript of the marks for the two years of the master's degree with details of the attended classes, a copy of the research dissertation carried out as part of the master's degree, a detailed CV, and a cover letter.

Work Location(s)

Where to apply.

MEMP PhD Thesis Defense (1:30pm): Jennifer Dawkins

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MIT E25-119/121 45 Carleton Street, Cambridge, MA 02142 and Zoom (See below for full information)

Computational prediction of health status from the human gut microbiome and metabolome

A healthy gut microbiome is crucial to overall human well-being. Gut microbiome dysfunction, or dysbiosis, has been implicated in a broad range of diseases, including inflammatory bowel diseases (IBDs), cardiovascular diseases, kidney diseases, metabolic diseases, and gastrointestinal infections like  Clostridioides difficile  infection (CDI). Often, microbiome-linked illnesses arise after the microbiome is disrupted, such as by antibiotic treatment. However, because the microbiome is so diverse and individual-specific, very little is known about the specific microbial changes that may lead to it human disease. Thus, it is extremely difficult to predict whether a given disruption to the microbiome will result in disease. 

Of the diseases linked to gut microbial disfunction, dysbiosis is perhaps most prominently linked to CDI. As the most common health-care associate infection, CDI is thought to occur when an individual has had both exposure to the  C. difficile  pathogen and gut dysbiosis caused by a past perturbation, such as antibiotic treatment. Infection recurrence, with an estimated rate of 15.5%, is a particularly insidious problem, and there is currently no reliable method to predict which individuals will recur. There is a need for early prediction of CDI after a perturbation, as this can allow physicians to start or restart more effective treatments immediately and prevent further sickness and risk of death.

Current research into the microbiome and microbiome dysbiosis, including CDI, focuses heavily on identifying the microbial taxonomic composition using next generation sequencing. However, there is growing evidence that the gut metabolome may provide crucial information that cannot be gained from microbial composition alone, as metabolites provide the means by which host cells and microbe cells communicate with each-other. Predictive analysis is especially useful for uncovering links between metabolic or microbial composition features and host disease state as it models all input covariates simultaneously. However, current predictive methods often fall short when applied to the microbiome, as simpler methods lack the capabilities to model this complex system, whereas highly non-linear “black box” methods lack interpretability. When predicting from biological or medical data with the goals of clinical utility and advancement of scientific knowledge, a model that can explain its decisions is crucial for increasing physician trust and uncovering avenues for future investigation. There is a need for interpretable computational models that can learn non-linear relationships between host outcome and paired microbial composition and metabolomic profiles.

This thesis addresses these two challenges. First, we present the analysis of a novel longitudinal study of CDI recurrence in patients, including predictive analyses, which demonstrate that a small set of metabolites can accurately predict future recurrence. Our findings have clinical utility in the development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence. Secondly, we present a novel predictive model developed specifically for making interpretable predictions on paired microbial composition and untargeted metabolic profiles. We demonstrate our model’s ability to predict a variety of host disease states accurately while providing clear and biologically compelling explanations of its decisions, thereby demonstrating high clinical and scientific utility.

Thesis Supervisor: Georg K. Gerber, MD, PhD Associate Professor of Pathology, HMS; Member of the Faculty, Harvard-MIT Program in Health Sciences and Technology

Thesis Committee Chair: Emery Brown, MD, PhD Warren M. Zapol Professor of Anesthesia, HMS, MGH; Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience, MIT 

Thesis Readers: Eric Alm, PhD Professor of Biological Engineering, MIT

Emily Balskus, PhD Thomas Dudley Cabot Professor of Chemistry and Chemical Biology, HU; Howard Hughes Medical Institute Investigator ------------------------------------------------------------------------------------------------------

Zoom invitation –  Jennifer Dawkins is inviting you to a scheduled Zoom meeting.

Topic: Jennifer Dawkins MEMP PhD Thesis Defense Time: Tuesday, April 30, 2024, 1:30 PM Eastern Time (US and Canada)

Your participation is important to us: please notify  hst [at] mit.edu (hst[at]mit[dot]edu) , at least 3 business days in advance, if you require accommodations in order to access this event.

Join Zoom Meeting https://mit.zoom.us/j/99749759912

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Museum talks give UChicago graduate students’ research a new audience

April 25, 2024

By Benjamin Ransom/ UChicago News

When University of Chicago graduate student Mia Paletta recently delivered a gallery talk about her research at the Field Museum, she invited the audience to ponder: “What do birds dream about?”

A Ph.D. student studying computational neuroscience, Paletta is fascinated by the way that animal brains generate coordinated, complex movements like birdsong. Standing amid the Field’s eclectic collection of birds around the globe, Paletta explained that when songbirds are asleep, the neurons in their brains fire in the same patterns as those that are produced while the animal is awake and singing. She explained that songbirds may be mentally rehearsing and processing the complex motor actions required to sing their songs.  

Paletta’s presentation was part of the UChicagoGRAD’s new Research Speaks event series, which provides unique opportunities for graduate students to share their research with the University community and wider public. 

“As an early-career scientist that doesn’t get a lot of opportunities to talk about my research in front of a public audience, it was encouraging and fun to see that people were excited about what I was doing,” Paletta said. “I especially love talking to kids about science, and I want everyone to feel encouraged that they have a place in it if they want it.” 

For the program’s inaugural event, 12 doctoral students in the humanities, social sciences, biological sciences, and engineering were invited to guide visitors through exhibits and specimens at the Field Museum—from ancient Egyptian scrolls to skeletal models of early human ancestors.

“Research Speaks is not only an exciting opportunity for graduate students’ professional development,” said Brooke Carrell, assistant provost and executive director of UChicagoGRAD Experience, “it’s a forum for them to engage with dynamic cultural institutions and practice sharing their ideas—maximizing the impact of their research with the broader public.”