## 1. COURSE IDENTIFICATION BLOCK **Course Title:** Introduction to Computational Neuroscience **Suggested Course Prefix/Number:** NEUR 4300 / CSCI 4300 / MATH 4300; graduate dual-listing NEUR 6300 **Departmental Home:** Neurobiology; cross-listed with Computer Science and Applied Mathematics **Credit Hours:** 4 credits **Contact Hours:** 2 × 75-minute lectures weekly = 2.5 hours/week; 1 × 3-hour computational lab weekly = 3 hours/week; total contact = 5.5 hours/week **Term/Year:** Fall [Year] **Meeting Pattern:** * Lecture: [Days], [Time], [Building/Room] * Computational Lab: [Day], [Time], [Building/Computer Lab/Room] **Modality:** In-person with digital lab notebook and course LMS support **Class Size:** 35–45 students **Design Basis:** Instructor-provided course brief. **Prerequisites:** * BIOL/NEUR 3000: Cellular Neuroscience or equivalent * MATH 2200: Calculus II or equivalent * MATH 2500: Linear Algebra or equivalent * CSCI 1500: Introduction to Programming in Python or equivalent **Corequisites:** None **Recommended Preparation:** Differential equations, probability/statistics, Git/GitHub basics, and comfort reading primary scientific literature. **Instructor:** [Instructor Name], Ph.D. **Office:** [Building/Room] **Office Hours:** * In person: [Days/Times] * Virtual: [Days/Times] via [Zoom/Teams/Institutional Platform] * Additional appointments available by request **Preferred Contact Method:** LMS message for course logistics; university email for confidential matters. **Email Response Policy:** The instructor will normally respond within 24–48 business hours. Messages sent after 5:00 p.m. Friday may receive a response the following business day. --- ## 2. COURSE DESCRIPTION ### Catalog Description This course introduces mathematical and computational approaches for modeling neural systems across levels of organization, from membrane dynamics and single-neuron excitability to synaptic plasticity and network behavior. Students implement models in Python using Jupyter, Brian2, NEURON, and version-controlled workflows while connecting biological mechanisms to quantitative analysis, simulation, and reproducible research practices. ### Expanded Course Description Computational neuroscience provides a formal bridge between biological observations and mechanistic explanations. In this course, you will translate neural phenomena into equations, simulations, visualizations, and interpretable claims. You will begin with dynamical systems concepts that describe membrane voltage, ion-channel conductances, and spike generation, then progress to integrate-and-fire neurons, Hodgkin-Huxley dynamics, synaptic transmission, plasticity, population coding, recurrent networks, and biologically grounded simulations. The course emphasizes both theory and implementation. Weekly computational labs develop your ability to build, test, document, and revise models using Python, NumPy, SciPy, Matplotlib, Jupyter, Git, Brian2, and NEURON. You will also evaluate modeling assumptions, compare model families, reproduce published or archived simulations, and communicate results in a format consistent with contemporary scientific computing practice. Brian2 is widely used for spiking neural network simulation and supports flexible model equations, while NEURON is a major environment for empirically grounded cell and network models. ([eLife][1]) This course is designed for advanced undergraduates and beginning graduate students in neuroscience, biology, computer science, mathematics, biomedical engineering, cognitive science, data science, or related fields. A productive mindset includes curiosity about biological mechanism, willingness to debug iteratively, openness to mathematical abstraction, and commitment to reproducible scientific practice. --- ## 3. COURSE LEARNING OUTCOMES Upon successful completion of this course, students will be able to: 1. **Represent** membrane, synaptic, and network-level neural phenomena using differential equations, state variables, parameters, and computational diagrams that match biological assumptions and units. * **Bloom Level:** Apply * **Program Learning Outcomes:** PLO 1: Biological mechanism; PLO 2: Quantitative reasoning * **ABET-Adjacent Alignment:** ABET 1: Solve complex problems using principles of science and mathematics 2. **Implement** single-neuron and synaptic models in Python, Brian2, and NEURON using documented, executable, and version-controlled workflows that produce reproducible outputs. * **Bloom Level:** Apply/Create * **Program Learning Outcomes:** PLO 3: Computational practice; PLO 5: Research readiness * **ABET-Adjacent Alignment:** ABET 1, ABET 5: Function effectively on a team using tools and planning 3. **Analyze** model behavior by interpreting phase-plane structure, parameter sensitivity, numerical stability, spike timing, firing-rate curves, and simulation outputs using appropriate quantitative methods. * **Bloom Level:** Analyze * **Program Learning Outcomes:** PLO 2: Quantitative reasoning; PLO 4: Data interpretation * **ABET-Adjacent Alignment:** ABET 6: Develop and interpret experimental or computational data 4. **Compare** major neuron and network model classes, including integrate-and-fire, Hodgkin-Huxley, Izhikevich-style, conductance-based, rate-based, and spiking network models, by identifying tradeoffs in biological realism, computational efficiency, interpretability, and intended use. * **Bloom Level:** Analyze * **Program Learning Outcomes:** PLO 1: Biological mechanism; PLO 4: Data interpretation * **ABET-Adjacent Alignment:** ABET 2: Apply design judgment with attention to constraints 5. **Evaluate** published or archived computational neuroscience models by critiquing assumptions, parameter choices, reproducibility, documentation quality, and alignment between model structure and biological claims. * **Bloom Level:** Evaluate * **Program Learning Outcomes:** PLO 5: Research readiness; PLO 6: Scientific communication * **ABET-Adjacent Alignment:** ABET 4: Recognize professional and ethical responsibilities; ABET 7: Acquire and apply new knowledge 6. **Design and communicate** an original computational modeling project that poses a biologically meaningful question, constructs an executable model, validates results against expected behavior or literature, and presents findings through a reproducible repository, digital lab notebook, written report, and oral presentation. * **Bloom Level:** Create * **Program Learning Outcomes:** PLO 3: Computational practice; PLO 5: Research readiness; PLO 6: Scientific communication * **ABET-Adjacent Alignment:** ABET 2, ABET 3: Communicate effectively, ABET 5, ABET 6 --- ## 4. REQUIRED & RECOMMENDED MATERIALS ### Required Textbooks 1. Dayan, P., & Abbott, L. F. (2001). *Theoretical neuroscience: Computational and mathematical modeling of neural systems*. MIT Press. ISBN: 978-0262541855. Approximate cost: $55–$75 used/print; often available through library reserve. MIT Press lists the book as part of its Computational Neuroscience series. ([MIT Press][2]) 2. Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). *Neuronal dynamics: From single neurons to networks and models of cognition*. Cambridge University Press. ISBN: 978-1107635197. Approximate cost: $55–$75 paperback; online version available at no cost from the authors/publisher site. ([neuronaldynamics.epfl.ch][3]) ### OER and No-Cost Alternatives * Gerstner et al., *Neuronal Dynamics* online text, assigned as a primary no-cost reading source. ([neuronaldynamics.epfl.ch][3]) * Brian2 documentation and tutorials, no cost. Brian is described by its developers as a free, open-source simulator for spiking neural networks. ([Zenodo][4]) * NEURON documentation and tutorials, no cost. NEURON supports modeling individual neurons and networks and includes Python-based workflows. ([nrn.readthedocs.io][5]) * ModelDB, no cost, for archived computational neuroscience models and reproducibility exploration. ModelDB guidance emphasizes citing both ModelDB and the original model papers when using archived models. ([ModelDB][6]) ### Required Articles and Chapters The instructor may adjust exact page ranges to match the final textbook edition and library access. 1. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. *The Journal of Physiology, 117*(4), 500–544. This is the foundational conductance-based model of action-potential generation. ([PubMed][7]) 2. Hodgkin, A. L., Huxley, A. F., & Katz, B. (1952). Measurement of current-voltage relations in the membrane of the giant axon of *Loligo*. *The Journal of Physiology, 116*, 424–448. 3. Lapicque, L. (1907/2007 translation). Quantitative investigations of electrical nerve excitation treated as polarization. *Biological Cybernetics*. 4. FitzHugh, R. (1961). Impulses and physiological states in theoretical models of nerve membrane. *Biophysical Journal*. 5. Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. *Biophysical Journal*. 6. Izhikevich, E. M. (2003). Simple model of spiking neurons. *IEEE Transactions on Neural Networks, 14*(6), 1569–1572. The model was proposed to combine biological plausibility with computational efficiency. ([izhikevich.org][8]) 7. Abbott, L. F., & van Vreeswijk, C. (1993). Asynchronous states in networks of pulse-coupled oscillators. *Physical Review E*. 8. Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. *Journal of Computational Neuroscience*. 9. Dayan, P., & Abbott, L. F. (2001). Selected chapters from *Theoretical neuroscience*. 10. Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Selected chapters from *Neuronal dynamics*. 11. Stimberg, M., Brette, R., & Goodman, D. F. M. (2019). Brian 2, an intuitive and efficient neural simulator. *eLife, 8*, e47314. ([eLife][1]) 12. Hines, M. L., Morse, T., Migliore, M., Carnevale, N. T., & Shepherd, G. M. (2004). ModelDB: A database to support computational neuroscience. *Journal of Computational Neuroscience, 17*, 7–11. ([PMC][9]) 13. Samuel, S., et al. (2024). Computational reproducibility of Jupyter notebooks from biomedical publications. *GigaScience*. This study examined reproducibility of thousands of notebooks associated with biomedical publications. ([PMC][10]) 14. Masson-Trottier, M., et al. (2025). Toward the future of scientific publishing through reproducible research artefacts enabled by Neurodesk. *Aperture Neuro*. ([apertureneuro.org][11]) 15. Selected recent instructor-posted article from [Journal of Computational Neuroscience / eLife / Nature Neuroscience / PLOS Computational Biology], chosen to connect current research with final project topics. ### Recommended Supplementary Materials * Izhikevich, E. M. (2007). *Dynamical systems in neuroscience: The geometry of excitability and bursting*. MIT Press. * Ermentrout, B., & Terman, D. (2010). *Mathematical foundations of neuroscience*. Springer. * Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011). *Principles of computational modelling in neuroscience*. Cambridge University Press. * Journals: *Journal of Computational Neuroscience*, *PLOS Computational Biology*, *eLife*, *Nature Computational Science*, *Neural Computation*. * Podcasts and seminars: Allen Institute seminars, Simons Foundation neuroscience lectures, Neuromatch Academy materials. * Datasets and model repositories: ModelDB, DANDI Archive, Allen Brain Atlas, CRCNS. ### Required Technology and Software | Item | Requirement | Approximate Cost | Free/Accessible Alternative | | ------------------------------------------------ | ----------------: | ----------------------: | ------------------------------------------------- | | Laptop capable of running Python | Required for labs | Student-owned or loaner | University loaner laptop program | | Python 3.11+ | Required | Free | Anaconda, Miniforge, or department image | | JupyterLab / Jupyter Notebook | Required | Free | JupyterHub if available | | Git and GitHub/GitLab | Required | Free | University-hosted GitLab | | NumPy, SciPy, Matplotlib, Pandas | Required | Free | Installed through conda/pip | | Brian2 | Required | Free | Department container image | | NEURON with Python | Required | Free | Department container image or JupyterHub | | Screen-reader-compatible digital lab notebook | Required | Free | Markdown notebook template supplied by instructor | | External mouse/keyboard or adaptive input device | Optional | Varies | Disability Services loaner/adaptive equipment | ### Textbook Affordability and Library Reserve The instructor will place required print textbooks on library reserve where possible and will provide no-cost alternatives for core readings when legally available. Students should not purchase optional materials until they have reviewed the library, reserve, and OER options. --- ## 5. WEEKLY SCHEDULE — TOPICS, READINGS & ACTIVITIES Dates, readings, and due dates may be adjusted with advance notice. “DA” refers to Dayan & Abbott; “ND” refers to Gerstner et al., *Neuronal Dynamics*. | Week | Dates | Topic | Subtopics | Required Readings | In-Class and Lab Activities | Out-of-Class Work | CLOs | Assessment Due | | ---: | -------------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------- | ---------- | ----------------------------------- | | 1 | Week of [Date] | What counts as a neural model? | Levels of explanation; biological abstraction; units; state variables; reproducible workflows | DA Ch. 1 selections; ND Intro; course lab notebook guide | Lecture: model-building as scientific argument. Lab: Python/Jupyter/Git setup; accessible notebook template; unit-checking exercise | Install software; complete Git diagnostic; read lab notebook standards | 1, 2 | Technology readiness check | | 2 | Week of [Date] | Mathematical foundations for neural dynamics | Differential equations; numerical integration; stability; dimensional analysis | ND Ch. 1; instructor notes on Euler and Runge-Kutta methods | Lecture: ODEs and state-space reasoning. Lab: simulate RC membrane equation; compare time steps | Problem set practice; notebook reflection on numerical error | 1, 3 | Lab Notebook 1 | | 3 | Week of [Date] | Passive membrane and leaky integrate-and-fire models | Membrane capacitance; resistance; threshold; reset; spike train metrics | ND Ch. 1–2 selections; Lapicque reading | Lecture: LIF model derivation. Lab: build LIF neuron from scratch; firing-rate curves | Coding drills; parameter sweep | 1, 2, 3 | Problem Set 1 | | 4 | Week of [Date] | Conductance-based modeling and Hodgkin-Huxley dynamics | Sodium/potassium currents; gating variables; voltage clamp; action-potential generation | Hodgkin & Huxley 1952; ND Ch. 2; DA Ch. 5 selections | Lecture: HH equations and biological interpretation. Lab: implement reduced HH simulation; plot gating variables | Annotate HH code; prepare model comparison notes | 1, 2, 3, 4 | Lab Notebook 2 | | 5 | Week of [Date] | Phase planes, excitability, and bifurcation intuition | Nullclines; fixed points; limit cycles; excitability classes; FitzHugh-Nagumo | FitzHugh 1961; ND Ch. 4 selections | Lecture: qualitative dynamics. Lab: phase-plane visualization; perturbation experiments | Short analytical memo comparing models | 3, 4 | Problem Set 2 | | 6 | Week of [Date] | Synapses and short-term dynamics | Chemical/electrical synapses; conductance vs. current-based synapses; AMPA/NMDA/GABA; short-term plasticity | DA Ch. 5–6 selections; ND Ch. 3 selections | Lecture: synaptic mechanisms as model components. Lab: build synaptic conductance model in Brian2 | Prepare for midterm; update Git repository | 1, 2, 3 | Lab Notebook 3 | | 7 | Week of [Date] | Midterm review and model critique | Review of model families; quantitative reasoning; assumptions; interpretation | Review packet; selected ModelDB example | Lecture: structured review and exam preparation. Lab: guided critique of archived model documentation | Study guide; practice exam; model critique worksheet | 1, 3, 4, 5 | Midterm Exam | | 8 | Week of [Date] | Spiking network simulation with Brian2 | Recurrent networks; E/I balance; sparse connectivity; population activity | Stimberg et al. 2019; Brunel 2000 selections; Brian2 tutorials | Lecture: network dynamics and simulation constraints. Lab: Brian2 E/I network; raster plots and firing-rate analysis | Repository milestone; project topic brainstorming | 2, 3, 4 | Project Proposal | | 9 | Week of [Date] | NEURON and morphology-aware modeling | Compartments; cable equation intuition; morphology; ion-channel insertion; ModelDB | NEURON documentation selections; Hines et al. 2004; selected ModelDB model | Lecture: when morphology matters. Lab: run and modify a NEURON model; document dependencies | Model replication plan; notebook reproducibility checklist | 2, 3, 5 | Lab Notebook 4 | | 10 | Week of [Date] | Neural coding and decoding | Rate coding; temporal coding; tuning curves; population vectors; basic decoding | DA Ch. 3 selections; recent article posted on LMS | Lecture: linking models to data. Lab: simulate tuning curves; decode stimulus variable | Data analysis exercise; project methods draft | 1, 3, 4 | Problem Set 3 | | 11 | Week of [Date] | Learning and plasticity | Hebbian learning; STDP; reward-modulated learning; model limitations | ND Ch. 19 selections; instructor-posted STDP article | Lecture: plasticity rules as algorithms and hypotheses. Lab: implement STDP in Brian2 | Project coding sprint; peer feedback | 2, 3, 4, 6 | Lab Notebook 5 | | 12 | Week of [Date] | Reproducible computational neuroscience | Git workflows; environment files; notebooks as scientific records; containers; FAIR principles | Samuel et al. 2024; Masson-Trottier et al. 2025; ModelDB citation guidance | Lecture: reproducibility and research ethics. Lab: refactor notebook; create README, requirements file, and disclosure log | Final project repository checkpoint | 2, 5, 6 | Reproducibility Audit | | 13 | Week of [Date] | Flex/buffer week: guest research, catch-up, or advanced topic | Possible topics: neurotechnology, brain-machine interfaces, Bayesian models, deep learning links, neuromorphic hardware | Guest reading or catch-up readings posted by [Date] | Lecture: guest speaker or advanced topic. Lab: project consultation and accessibility-centered debugging studio | Final project analysis; presentation outline | 3, 5, 6 | Draft Project Notebook | | 14 | Week of [Date] | Final synthesis: from model to claim | Scientific storytelling; limitations; visualization; oral presentation; responsible AI disclosure | Review of all prior readings; final project presentation guide | Lecture: synthesis workshop. Lab: project presentations and peer review | Final report and repository revision | 4, 5, 6 | Final Presentation | | 15 | Finals Week | Final submission | No new content | No new readings | Optional office hours and troubleshooting session | Submit final project package | 2, 5, 6 | Final Project Report and Repository | --- ## 6. ASSESSMENT & GRADING ARCHITECTURE ### Assessment Plan | Assessment Type | Weight | CLOs Assessed | Due Date/Window | Description | Rubric Type | | ----------------------------------------------------- | -----: | ------------- | ---------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | | Weekly Digital Lab Notebooks | 20% | 1, 2, 3 | Weeks 2, 4, 6, 9, 11 | Screen-reader-compatible Jupyter/Markdown notebooks documenting model implementation, outputs, interpretation, and version history | Analytic rubric | | Quantitative Problem Sets | 15% | 1, 3, 4 | Weeks 3, 5, 10 | Mathematical and computational exercises on ODEs, membrane dynamics, spike trains, and model comparison | Standards-based analytic rubric | | Midterm Exam | 15% | 1, 3, 4 | Week 7 | In-class applied exam with derivation, interpretation, and model-analysis questions | Point-based rubric with partial credit | | Model Critique and Reproducibility Audit | 15% | 5, 2, 3 | Week 12 | Structured critique of a published or archived model, including assumptions, dependencies, reproducibility barriers, and improvement plan | Analytic rubric | | Final Computational Modeling Project | 25% | 2, 3, 4, 5, 6 | Proposal Week 8; presentation Week 14; final package Finals Week | Original or substantially extended computational model with repository, notebook, report, and presentation | Signature assignment rubric | | Participation, Peer Review, and Professional Practice | 10% | 2, 5, 6 | Ongoing | Lab preparedness, peer feedback, Git hygiene, discussion contributions, AI disclosure logs, and professional communication | Engagement checklist and reflective rubric | **Total:** 100% ### Percentage-to-Letter Grade Conversion | Percentage | Letter Grade | | ---------: | ------------ | | 93.0–100 | A | | 90.0–92.9 | A- | | 87.0–89.9 | B+ | | 83.0–86.9 | B | | 80.0–82.9 | B- | | 77.0–79.9 | C+ | | 73.0–76.9 | C | | 70.0–72.9 | C- | | 67.0–69.9 | D+ | | 60.0–66.9 | D | | Below 60.0 | F | ### Signature Assignment Rubric Framework: Final Computational Modeling Project | Criterion | Exemplary | Proficient | Developing | Beginning | | ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | Biological and Theoretical Framing | Poses a focused biological question; justifies model assumptions with strong literature connection; clearly defines variables, parameters, and expected behavior | Poses a clear question; identifies major assumptions; connects model to appropriate literature | Question or assumptions need refinement; literature connection is partial | Question is vague; assumptions are missing or poorly connected to biology | | Computational Implementation | Code is modular, documented, executable, version-controlled, and appropriate for the model class; results can be reproduced from repository instructions | Code runs with minor setup; documentation and version history support reproducibility | Code partially runs or requires substantial instructor intervention; documentation is incomplete | Code is missing, non-executable, or insufficiently connected to the model | | Quantitative Analysis and Interpretation | Applies appropriate analyses; interprets parameter effects, uncertainty, limitations, and model behavior with precision | Applies relevant analyses and interprets core outputs accurately | Analysis is limited, mostly descriptive, or weakly connected to claims | Analysis is incorrect, absent, or unsupported by outputs | | Reproducibility and Research Practice | Includes clear README, environment file, notebook structure, data provenance, AI disclosure, and accessible documentation | Includes most reproducibility components with minor gaps | Reproducibility materials are present but incomplete or difficult to follow | Reproducibility materials are absent or unusable | | Scientific Communication | Written report and presentation are clear, concise, visual, well organized, and appropriate for a computational neuroscience audience | Communication is organized and technically accurate | Communication is uneven, overly procedural, or missing key rationale | Communication lacks organization or prevents evaluation of the work | ### Late Work Policy Work submitted within 48 hours after the deadline may receive up to a 10% reduction unless an approved extension is in place. Work submitted more than 48 hours late may receive reduced or no credit depending on whether feedback has already been released. Students facing illness, disability-related barriers, caregiving responsibilities, religious observance, technology failure, or other documented circumstances should contact the instructor as early as feasible so that an equitable plan can be arranged. ### Resubmission Policy You may revise and resubmit up to two lab notebooks or problem sets within one week of receiving feedback. Resubmissions must include a brief revision memo that identifies changes and explains how feedback was addressed. Final projects include staged checkpoints so that revision is built into the assignment process. ### Extra Credit Policy Small extra-credit opportunities may be offered to the entire class, such as attending an approved computational neuroscience seminar and submitting a one-page connection memo. Extra credit will not exceed 2% of the final course grade. ### Grade Appeal Procedure Students should first review the rubric and written feedback, then submit a concise written appeal within seven calendar days of grade release. The appeal should identify the specific criterion being reconsidered and provide evidence from the submitted work. The instructor will review the appeal and respond in writing. Further appeals follow departmental and university procedures. ### Mastery / Specifications Grading Alternative Instructors who prefer specifications grading may convert the assessment architecture into bundles of required competencies. For example, students earning an A would complete all core lab notebooks at “Proficient” or higher, earn “Proficient” or higher on the model critique, complete the final project at “Exemplary” on at least three rubric criteria, and meet professional-practice expectations. Students earning a B would complete all core competencies at “Proficient” with fewer exemplary marks. Students earning a C would complete essential competencies at “Developing” or higher. This alternative should be announced before the first graded assignment. --- ## 7. ASSESSMENT CALENDAR AT-A-GLANCE | Week | Graded Item | Weight | | ----------: | ------------------------------------------------------ | ------------------------: | | 1 | Technology readiness check | Included in participation | | 2 | Lab Notebook 1: RC membrane and numerical integration | 4% | | 3 | Problem Set 1: Passive membrane and LIF models | 5% | | 4 | Lab Notebook 2: Hodgkin-Huxley implementation | 4% | | 5 | Problem Set 2: Phase planes and excitability | 5% | | 6 | Lab Notebook 3: Synaptic conductance model | 4% | | 7 | Midterm Exam | 15% | | 8 | Final Project Proposal | Included in final project | | 9 | Lab Notebook 4: NEURON and ModelDB workflow | 4% | | 10 | Problem Set 3: Neural coding and decoding | 5% | | 11 | Lab Notebook 5: Plasticity model | 4% | | 12 | Model Critique and Reproducibility Audit | 15% | | 13 | Draft Project Notebook | Included in final project | | 14 | Final Project Presentation | Included in final project | | Finals Week | Final Project Report, Repository, and Notebook Package | 25% | | Ongoing | Participation, Peer Review, Professional Practice | 10% | | | **Total** | **100%** | --- ## 8. COURSE POLICIES ### Attendance and Participation Because this course depends on collaborative debugging, live modeling demonstrations, and hands-on lab practice, regular attendance is expected. You may miss up to two class meetings without grade penalty. Additional absences may be excused for documented illness, disability-related needs, religious observance, university-sponsored travel, military service, caregiving responsibilities, bereavement, or emergency circumstances. Participation includes preparing for class, contributing to discussions, engaging in lab work, asking and answering questions, giving peer feedback, maintaining your repository, and supporting an inclusive computational learning environment. ### Late Work and Missed Assessments Missed exams, presentations, or labs should be communicated as soon as feasible. When circumstances are documented or otherwise verified under university policy, the instructor will provide an equivalent make-up option or adjusted timeline. Students should not attend class or lab while ill in a way that risks community health. ### Communication Use LMS messages for routine course questions and university email for confidential matters. Professional messages should include a clear subject line, course number, specific question, and any relevant attachment or screenshot. The instructor will normally respond within 24–48 business hours. ### Classroom Conduct and Discussion Ground Rules Computational neuroscience often involves uncertainty, approximation, and iterative correction. You are expected to critique ideas, models, code, and assumptions without disparaging classmates. Productive participation includes asking clarifying questions, making space for others, naming uncertainty, and grounding claims in evidence. During lab, students should avoid taking over another student’s keyboard unless invited; explain your reasoning and support the other person’s agency. ### Recording and Intellectual Property Students may not record class sessions without instructor permission except where recording is part of an approved accommodation. Instructor materials, slides, notebooks, assignments, and recordings are for use by students enrolled in this course and may not be redistributed, sold, or posted publicly without written permission. Students retain ownership of their own original project work unless they choose an open-source license. ### Course Materials and Copyright Readings and media are provided under library license, fair use, open-access terms, or instructor-created materials. Students are responsible for using course materials in accordance with copyright law and university policy. --- ## 9. ACADEMIC INTEGRITY & GENERATIVE AI POLICY ### Academic Integrity Statement This course follows the university honor code and academic integrity policy. You are expected to submit work that accurately represents your own reasoning, modeling decisions, coding contributions, sources, and collaborations. Academic integrity in computational work includes transparent attribution, reproducible workflows, accurate reporting of results, and honest documentation of assistance received. ### Definitions * **Plagiarism:** Presenting another person’s words, code, figures, models, data, or ideas as your own without appropriate attribution. * **Collusion:** Collaborating beyond the permitted scope of an assignment or submitting substantially shared work as individual work. * **Contract cheating:** Submitting work produced by another person, paid service, or unauthorized agent. * **Fabrication:** Inventing data, simulation results, citations, parameters, lab notes, or debugging records. * **Self-plagiarism:** Reusing substantial work from another course or prior submission without instructor permission and disclosure. ### Generative AI Policy The university permits assisted use of generative AI with disclosure. This course uses a three-zone policy. #### Zone 1: AI-Prohibited Assignments Generative AI tools may not be used for the following unless explicitly authorized in writing: * In-class midterm exam * Short in-class diagnostic exercises * Individual reflection paragraphs that ask you to describe your own process or learning * Peer review feedback, unless the assignment states otherwise #### Zone 2: AI-Assisted Assignments with Disclosure Generative AI may be used as a support tool for the following, provided you disclose use and remain responsible for correctness: * Brainstorming project questions * Explaining error messages * Suggesting code structure * Improving prose clarity * Generating draft comments or documentation * Reviewing mathematical notation for readability You must verify outputs, cite sources independently, and test all code yourself. AI-generated code that you cannot explain may not be submitted as your own work. #### Zone 3: AI-Integrated Assignments AI use is required and assessed in designated reproducibility or code-review activities. For example, the Reproducibility Audit may ask you to compare your own debugging process with AI-generated suggestions and evaluate which suggestions were valid, invalid, incomplete, or risky. ### Required AI Disclosure Format At the end of any assignment where AI is permitted or required, include: > **AI Use Disclosure:** I used [tool name/version, if known] on [date] for [specific purpose]. I entered the following type of prompt: [brief description, not necessarily full prompt if confidential]. I accepted, rejected, or modified the output in the following ways: [specific explanation]. I verified the final work by [tests, citations, calculations, or reasoning]. If no AI was used, write: > **AI Use Disclosure:** I did not use generative AI tools for this assignment. ### Consequences and Reporting Procedure Possible responses to violations include revision with education, grade adjustment, referral to the academic integrity office, or other outcomes described in university policy. The instructor will communicate concerns in writing and provide students an opportunity to respond through the established university process. --- ## 10. ACCESSIBILITY, UDL & ACCOMMODATIONS ### Disability Accommodations Students with disabilities are entitled to reasonable accommodations that support equitable access. If you have an accommodation letter or believe you may need accommodations, contact [Disability Services Office], [email], [phone], [website], and share approved accommodations with the instructor as early as possible. Accommodations can be implemented without requiring disclosure of private medical details to the instructor. This course is designed with digital-first lab notebooks to support screen reader compatibility, keyboard navigation, flexible input methods, and accessible review of code, outputs, and written explanations. Students with motor-disability accommodations affecting lab work may use adaptive hardware, paired navigation protocols, flexible lab timing, alternative demonstration formats, speech-to-text, or approved assistants as coordinated through Disability Services. ### UDL 3.0 Implementation #### Multiple Means of Engagement 1. Students choose final project topics from several biologically meaningful modeling domains, including single-neuron excitability, synaptic plasticity, network dynamics, neural coding, or neurotechnology applications. 2. Lab activities include individual checkpoints, structured peer collaboration, and optional challenge extensions so that students can engage at an appropriate level of difficulty. 3. Assignment milestones reduce high-stakes overload by distributing project work across proposal, draft notebook, presentation, and final repository. #### Multiple Means of Representation 1. Core concepts are presented through equations, diagrams, simulations, code, verbal explanation, biological interpretation, and worked examples. 2. Lecture slides, notebooks, datasets, and lab instructions are posted in accessible digital formats before or shortly after class when feasible. 3. Mathematical derivations are paired with computational demonstrations so that students can connect symbolic, visual, and executable representations. #### Multiple Means of Action and Expression 1. Students demonstrate competence through problem sets, notebooks, code repositories, oral presentation, written critique, peer feedback, and project reports. 2. Lab notebooks use accessible Markdown/Jupyter templates with headings, alt text for figures, plain-language captions, and structured code cells. 3. Students with approved accommodations may use alternative input devices, pair-programming protocols, oral code walkthroughs, or extended lab completion windows without reducing learning standards. ### Religious Observance Students may request reasonable accommodations for religious observances. Please notify the instructor as early as possible, preferably within the first two weeks of the term or as soon as the conflict is known. The instructor will provide an equivalent opportunity to complete missed work. ### Pregnancy and Parenting Accommodations Students who are pregnant, recovering from childbirth, lactating, adopting, fostering, or managing parenting responsibilities may be eligible for reasonable accommodations under Title IX and university policy. Contact [Title IX Office] at [email/phone/website] and notify the instructor as early as feasible so that course access can be supported. ### Mental Health and Basic Needs Learning is affected by stress, health, housing, food access, and financial stability. Students may contact [Counseling Center], [Student Wellness], [Basic Needs Office], [Food Pantry], [Emergency Aid Office], or [Dean of Students] for support. For immediate crisis support, contact [24/7 Crisis Hotline] or emergency services. ### Land Acknowledgment Placeholder [Insert institutionally approved land acknowledgment here, if culturally appropriate and developed in consultation with relevant Indigenous communities.] --- ## 11. DIVERSITY, EQUITY & INCLUSION STATEMENT Computational neuroscience benefits when people with different disciplinary backgrounds, identities, languages, abilities, and lived experiences contribute to model-building and critique. This course treats modeling as a human practice: every equation, dataset, parameter choice, and visualization reflects assumptions that should be examined. You are encouraged to bring perspectives from neuroscience, mathematics, computing, engineering, psychology, disability studies, ethics, and lived experience into our discussions. We will use inclusive language, respect names and pronouns, and avoid assumptions about prior access to computing resources. Students may update the instructor about their name, pronunciation, or pronouns at any time. The course reading list includes foundational and contemporary scholarship from authors across institutions, countries, identities, and career stages where the field’s available literature permits. We will also discuss how access, documentation, open science, and reproducible tools shape who can participate in computational research. --- ## 12. STUDENT SUCCESS RESOURCES ### Tutoring and Academic Support * [Quantitative Learning Center]: support for calculus, differential equations, linear algebra, and statistics * [Computer Science Tutoring Center]: support for Python, Git, and debugging strategies * [Neuroscience Program Peer Mentoring]: program-specific academic support * Instructor and TA office hours: recommended for conceptual clarification and project planning ### Writing and Communication * [Writing Center]: support for scientific writing, project reports, abstracts, and presentation organization * [Speaking Center/Communication Studio]: support for oral presentations and visual communication ### Library Research Support * [Science Librarian Name/Contact]: help locating articles, citation managers, data repositories, and model archives * Library course guide: [Insert link] * Reserve materials: [Insert reserve information] ### Technology Support * [IT Help Desk]: LMS, JupyterHub, VPN, Git, software installation * [Loaner Laptop Program]: short-term equipment loans * [Assistive Technology Center]: screen readers, speech-to-text, adaptive input devices, accessible document support ### Counseling, Wellness, and Crisis Resources * [Counseling Center]: [phone/email/website] * [Student Wellness Center]: [phone/email/website] * [24/7 Crisis Hotline]: [phone/text/website] * Emergency services: [campus police/local emergency number] ### Career and Professional Development * [Career Services]: resumes, internships, graduate school preparation * [Undergraduate Research Office]: research placements and funding opportunities * [Neurotechnology/Computational Neuroscience Club]: peer community and seminar announcements * Professional communities: Society for Neuroscience, Organization for Computational Neurosciences, Neuromatch Academy, INCF --- ## 13. CHANGE CLAUSE & ACKNOWLEDGMENT ### Change Clause This syllabus represents the instructor’s current plan for the course. The instructor may adjust readings, due dates, lab activities, or policies when such changes improve learning, respond to class needs, address accessibility concerns, or accommodate university schedules. Changes will be announced in class and posted on the LMS with reasonable notice. ### Optional Student Acknowledgment I acknowledge that I have reviewed the syllabus for NEUR 4300/6300: Introduction to Computational Neuroscience. I recognize that I am responsible for course expectations, academic integrity requirements, communication policies, accessibility procedures, and major assignment deadlines. **Student Name:** _______________________________ **Signature:** __________________________________ **Date:** _______________________________________ --- ## 14. APPENDIX: ASSIGNMENT DESCRIPTIONS ### Assignment 1: Model Critique and Reproducibility Audit **Weight:** 15% **Due:** Week 12 **CLOs Assessed:** 2, 3, 5 **Format:** Written audit report, reproducibility checklist, annotated notebook or repository notes #### Purpose Computational neuroscience advances when models can be inspected, reproduced, modified, and evaluated. This assignment prepares you to read computational work as both a scientist and a reviewer. You will assess whether a model’s biological claims are supported by its structure, assumptions, parameters, documentation, and executable materials. This skill is essential for graduate research, industry neurotechnology work, and responsible use of shared scientific code. #### Task Select one instructor-approved published or archived computational neuroscience model. Suitable sources include ModelDB, Brian2 examples, NEURON tutorials, or an article with accessible code. You will: 1. Identify the biological question or phenomenon addressed by the model. 2. Describe the model class, variables, parameters, assumptions, and outputs. 3. Attempt to run or partially reproduce the model in a documented environment. 4. Record dependencies, installation steps, errors, modifications, and successful outputs. 5. Evaluate the alignment between the model and the biological claim. 6. Assess reproducibility using a structured checklist: README quality, environment specification, data availability, parameter documentation, code organization, licensing, citation guidance, and accessibility of outputs. 7. Propose at least three concrete improvements that would make the model more reusable or interpretable. 8. Include an AI Use Disclosure, whether or not AI tools were used. **Deliverables:** * 1,500–2,000 word audit report * Reproducibility checklist supplied by instructor * Annotated notebook, terminal log, or repository notes * Short appendix documenting software environment and citations #### Criteria for Success Your work will be evaluated on: * Accuracy of model description and biological framing * Quality of computational reproduction attempt * Depth of critique regarding assumptions, parameters, and claims * Specificity and feasibility of reproducibility recommendations * Clear documentation, citation practice, and AI disclosure * Accessibility of submitted materials, including structured headings and readable figure captions --- ### Assignment 2: Final Computational Modeling Project **Weight:** 25% **Due:** Proposal Week 8; draft notebook Week 13; presentation Week 14; final package Finals Week **CLOs Assessed:** 2, 3, 4, 5, 6 **Format:** Repository, digital notebook, written report, oral presentation #### Purpose The final project asks you to integrate the central practices of the course: translating a biological question into a mathematical model, implementing that model in executable code, analyzing outputs quantitatively, evaluating limitations, and communicating results reproducibly. The project prepares you for graduate-level computational research, interdisciplinary collaboration, and technical roles in neurotechnology or data-intensive biomedical science. #### Task You will design an original or substantially extended computational model related to neural dynamics. Projects may focus on a single-neuron model, synaptic mechanism, small network, plasticity rule, population code, or reproduction and extension of a published model. Your project must include a biologically meaningful question and a computational implementation that generates interpretable outputs. You will complete the project in stages: 1. **Proposal:** Submit a 500–750 word plan identifying the question, model type, biological rationale, expected outputs, tools, risks, and initial references. 2. **Implementation:** Build the model in Python, Brian2, NEURON, or an approved computational environment. Use Git for version control. 3. **Analysis:** Run simulations, vary at least one meaningful parameter, visualize outputs, and connect results to model assumptions. 4. **Evaluation:** Discuss limitations, alternative model choices, reproducibility, and biological interpretation. 5. **Presentation:** Deliver a 7–10 minute presentation with clear visuals and a concise explanation of the model and findings. 6. **Final Package:** Submit a repository containing code, notebook, README, environment file, figures, written report, references, and AI disclosure. **Minimum Deliverable Specifications:** * Executable notebook or script-based workflow * README with setup instructions * Environment file, such as `environment.yml` or `requirements.txt` * At least three informative visualizations * At least one parameter sweep, sensitivity analysis, or model comparison * 2,500–3,500 word written report * Proper citations for readings, code sources, data, and model archives * AI Use Disclosure * Accessibility features: structured headings, alt text or descriptive captions for figures, readable tables, and screen-reader-compatible notebook organization #### Criteria for Success Your project will be evaluated using the signature assignment rubric in Section 6. Strong projects will: * Pose a focused and biologically motivated modeling question * Use equations, code, and explanation that align with the model’s assumptions * Produce executable, documented, and reproducible computational work * Analyze outputs with appropriate quantitative reasoning * Compare model choices or parameter effects in a meaningful way * Evaluate limitations rather than overstating conclusions * Communicate findings clearly to an interdisciplinary neuroscience, mathematics, and computing audience * Document all assistance, including collaboration and generative AI use [1]: https://elifesciences.org/articles/47314?utm_source=chatgpt.com "Brian 2, an intuitive and efficient neural simulator" [2]: https://mitpress.mit.edu/9780262041997/theoretical-neuroscience/?utm_source=chatgpt.com "Theoretical Neuroscience" [3]: https://neuronaldynamics.epfl.ch/?utm_source=chatgpt.com "Neuronal Dynamics - a neuroscience textbook by Wulfram ..." [4]: https://zenodo.org/records/17833036?utm_source=chatgpt.com "Brian 2" [5]: https://nrn.readthedocs.io/en/latest/guide/what_is_neuron.html?utm_source=chatgpt.com "What is NEURON — NEURON documentation" [6]: https://modeldb.science/howtocite?utm_source=chatgpt.com "How to cite ModelDB" [7]: https://pubmed.ncbi.nlm.nih.gov/12991237/?utm_source=chatgpt.com "A quantitative description of membrane current and its ..." [8]: https://www.izhikevich.org/publications/spikes.pdf?utm_source=chatgpt.com "Simple model of spiking neurons - Neural Networks, IEEE ..." [9]: https://pmc.ncbi.nlm.nih.gov/articles/PMC3732827/?utm_source=chatgpt.com "ModelDB: a Database to Support Computational Neuroscience" [10]: https://pmc.ncbi.nlm.nih.gov/articles/PMC10783158/?utm_source=chatgpt.com "Computational reproducibility of Jupyter notebooks from ..." [11]: https://apertureneuro.org/article/143700-toward-the-future-of-scientific-publishing-through-reproducible-research-artefacts-enabled-by-neurodesk?utm_source=chatgpt.com "Toward the future of scientific publishing through ..."