Aims

Upon successful completion of this course, students will have acquired a robust and thorough understanding of both the fundamental principles and the advanced methodologies central to the computational modeling of neural systems. The curriculum is designed to span multiple scales of biological organization, from the detailed dynamics of individual cells to the emergent properties of large-scale neural networks. The primary objective of this course is to cultivate the student's ability to formulate, implement, and critically analyze mathematical and computational models. These models serve as powerful tools for investigating complex neurophysiological phenomena that are often intractable through experimental approaches alone. Emphasis will be placed on developing practical skills, thereby preparing students to confidently apply these modeling techniques in their own scientific research endeavors, particularly within the fields of neuroscience and biomedical engineering.

Prerequisites: To ensure successful and fruitful participation in this course, it is expected that students possess a solid foundation in several key areas. A strong background in mathematics is essential, specifically including differential and integral calculus and linear algebra. Foundational knowledge of physics, particularly mechanics and electromagnetism, is also required. Furthermore, students should have a grasp of general biology, with a particular emphasis on cell biology and human physiology. A familiarity with the core concepts of neurophysiology and cellular electrophysiology is considered a critical prerequisite for engaging with the course material. In addition to these disciplinary foundations, a demonstrated proficiency in quantitative methodologies for analyzing and solving complex problems is necessary. This includes having a firm understanding of elementary concepts in statistics and probability theory. Students must also possess basic scientific computing skills, with a strong preference for experience in Python, Julia, or MATLAB, as these will be the primary tools used for implementing and simulating the models discussed in the course.

Schedule

    In class: classes start at 2:30pm (sharp), and break for 10min, every 45-50min.

    There is no stupid question. Read it again, please! The Professor does NOT judge you or even remember/care of your questions at the final exam. He strives to make sure you understand the material. If you do not understand it, it is likely that others do not understand it either, but maybe they are too shy to ask. Go first and ask your questions! Besides office hours, the Professor is available for questions during the class or the breaks, as well as before its start and after its end.

    Office hours: no explanation is provided by the Professor over email or instant messaging. Office hours are available to all students only in person (Via Campi 287, building Biomedical Sciences, office MO-15) and upon prior appointment (or on Goole Meet if you are abroad).

    Collective learning: Consider posting your question in public, on our Teams group: other students are encouraged to try answering to their peers' questions.

  • Week 1
    • Oct 10th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 2
    • Oct 17th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 3
    • Oct 24th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 4
    • Nov 7th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 5
    • Nov 14th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 6
    • Nov 21st 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 7
    • Nov 28th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 8
    • Dec 5th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 9
    • Dec 12th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

  • Week 10
    • Dec 19th 2025 (14:30 - 17:00) - Mirandola, Centro Culturale "Il Pico"

content

1. Compartmental Modeling: Morphological Reconstruction and Ionic Currents
  • Techniques for representing a real neuron's complex 3D morphology as a set of interconnected electrical compartments.
  • Incorporating a diverse library of voltage- and ligand-gated ion channels with non-uniform distributions across the neuron.
  • Simulating dendritic integration and local computations within biophysically realistic and detailed models.
2. Simplified Neuron Models: From Leaky Integrate-and-Fire to Izhikevich
  • Analysis of the classic Leaky Integrate-and-Fire (LIF) model for its computational efficiency in large-scale simulations.
  • Exploring phenomenological extensions like the Quadratic and Exponential LIF models.
  • Using the adaptive exponential Integrate-and-Fire to efficiently reproduce the rich repertoire of firing patterns (e.g., bursting, chattering) observed in biological neurons.
3. Microcircuit Connectivity, Plasticity, and Dynamics
  • Examining how canonical connectivity patterns (e.g., recurrent excitation, feedback inhibition) in microcircuits shape network behavior.
  • Modeling the emergence of network oscillations (e.g., gamma rhythms) from the interaction of excitatory and inhibitory populations.
  • Understanding network computation through concepts like attractor dynamics and persistent activity for working memory.
4. Mean-Field Models and Neural Population Dynamics
  • Introduction to mean-field theory, a powerful mathematical abstraction for analyzing the dynamics of large networks.
  • Developing and analyzing firing-rate models (e.g., the Wilson-Cowan model) that describe the average activity of neural populations.
  • Modeling the collective behavior, state transitions, and stability of brain-scale networks in a computationally tractable manner.
5. Introduction to Neural Simulation Tools
  • Practical overview of NEURON, the gold standard for creating biophysically detailed, multi-compartmental models.
  • Introduction to Brian2, a user-friendly simulator ideal for rapid prototyping with equation-based model definitions.
  • Hands-on look at NEST, a highly optimized tool for the efficient simulation of massive, large-scale networks of simpler point neurons.

Teams Group & Code of Conduct

UNIMORE graciously makes available to us a Teams group as an (online, real-time) virtual meeting place and as an (offline, asynchronous) forum for questions and answers, for discussions on topics of the course, as well as for the students to offer mutual assistance during their study process. Access is reserved only to students attending the course.

If you do qualify as a legitimate member of our community, simply click on the icon above.

Before joining, please do take a serious look at our Code of Conduct, below:

Code of Conduct of our Class Teams Group

We are committed to creating a collaborative, open, and inclusive teaching and learning environment. All students, teaching assistants, affiliated faculty, organizers and contributors are expected to adhere to this Code of Conduct.

Participants or affiliates who are asked to stop any inappropriate behaviour are expected to comply immediately. This applies to any events and platforms, either online or in-person. If a participant engages in behaviour that violates this Code of Conduct, the organisers may warn the offender, ask them to leave the event or platform, or engage UniTs/SISSA’s Ombuds Offices to investigate the Code of Conduct violation and impose appropriate sanctions.

Violations of the Code of Conduct should be reported to MG.

1. Be inclusive

We welcome and support people of all backgrounds and identities. This includes, but is not limited to members of any sexual orientation, gender identity and expression, race, ethnicity, culture, national origin, social and economic class, educational level, color, immigration status, sex, age, size, family status, political belief, religion, and mental and physical ability.

2. Be considerate

We all depend on each other to produce the best work we can as an organization. Your decisions will affect students, teaching assistants, and colleagues around the world, and you should take those consequences into account when making decisions.

3. Be respectful

We won’t all agree all the time, but disagreement is no excuse for disrespectful behavior. We will all experience frustration from time to time, but we cannot allow that frustration become personal attacks. An environment where people feel uncomfortable or threatened is not a productive or creative one.

4. Choose your words carefully

Always conduct yourself professionally. Be kind to others. Do not insult or put down others. Harassment and exclusionary behavior aren’t acceptable. This includes, but is not limited to:

  • Threats of violence
  • Insubordination
  • Discriminatory jokes and language
  • Sharing sexually explicit or violent material via electronic devices or other means
  • Personal insults, especially those using racist or sexist terms
  • Unwelcome sexual attention
  • Advocating for, or encouraging, any of the above behavior.

5. Don’t harass

In general, if someone asks you to stop something, then stop. When we disagree, try to understand why. Differences of opinion and disagreements are mostly unavoidable. What is important is that we resolve disagreements and differing views constructively.

6. Make differences into strengths

We can find strength in diversity. Different people have different perspectives on issues, and that can be valuable for solving problems or generating new ideas. Being unable to understand why someone holds a viewpoint doesn’t mean that they’re wrong. Don’t forget that we all make mistakes, and blaming each other doesn’t get us anywhere. Instead, focus on resolving issues and learning from mistakes.

7. Act honestly and with academic integrity

We expect you to respect basic academic integrity principles and take academic integrity to mean adherence to the following values:

  • Honesty
  • Trust
  • Fairness
  • Respect
  • Responsibility
  • Courage.

More information on academic integrity and these values can be found at the International Center of Academic Integrity.

Be honest in your applications and in your potential reasons for missing classes, or project assignments. Take responsibility for your mistakes and work to remedy them. Don’t take the course under someone else’s name or identity.