Graduate School of health and Medical Sciences

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Theoretical and Computational Neuroscience - Understanding Cognition
Provider: Faculty of Health and Medical Sciences

Activity no.: 3354-26-00-00	There are 18 available seats
	Enrollment deadline: 01/02/2026

Date and time	02.03.2026, at: 12:30 - 27.05.2026, at: 15:30
Regular seats	18
Course fee	8,520.00 kr.
Lecturers	Rune Berg
ECTS credits	4.80

Contact person	Rasmus Herlo Beenfeldt Jensen E-mail address: herlo@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration SUND E-mail address: phdkursus@sund.ku.dk

Enrolment guidelines This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: The student will learn to understand fundamental concepts and mathematical models in computational neuroscience, and how they link explanatory to the essential building blocks of cognitions, including working memory, decision-making and behavioral flexibility. They will gain the ability to reframe a scientific question into a testable computational model, which can inform experimental designs, and the ability to test computational models against experimental data. The course also provides an introduction to essential mathematical tools with which the student will be able to solve first-order differential equations, identify attractor states and perform matrix operations sufficiently for single value decompositions. These mathematical tools will be practiced as a part of the homework, which will be solved together in class. To support the higher computational demands, a part of the course is utilizing python and jupyter notebook to illustrate dynamic concepts. The students will be introduced to these platforms and will by the end of the course have acquired the necessary skills to set up and edit scripts independently. Participants:18 (max 24) Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: Neuroscience All graduate programmes Language: English Form: Lectures, workshop, exercises and presentations Content: General and Weekly Specifications Length of course: 8 weeks General: The course aims to provide an overview of the central computational neuroscience concepts that connect single synaptic regulations to network dynamics and cognitive behaviors. This understanding will require both an appreciation of central mathematícal concepts, which will be taught at the beginning, as well as a broader conceptual comprehension of how a model is generated and tested against experimental data, and how it provides new predictions to inform future experiments. The specific contents are listed below, and span from mathematical terms, like attractors, over neural integration and recurrent networks, to reinforcement learning and ramp-up based decision-making. BOOK: Theoretical Neuroscience – Understanding Cognition by Xiao-Jin Wang Week 1: Mathematics Lectures (hand-out notes): • First order linear differential equations • Modelling dynamics • Linear Algebra • Singular Value Decomposition • Linear Stability Analysis and Attractor States Homework: • Matrix Multiplication by hand Exercises: • Introduction to python and Jupyter Notebook Week 2: Computational Modelling and Single Neuron Models: Lectures (Chp 1 + Chp2 start): • Epistemology o How to approach computational models (incl. Marr) o Graph models and frameworks o Cross-level mechanistic theory • Single Neuron Models o Integrate and fire models o Single and Multi-compartment models o Hodgkin-Huxley model o Membrane equation and electrical properties Homework: Week 3: Neural Dynamics and Synaptic Coupling Lectures (Chapter 2 end +3 start): • Conductance versus current based neurons • Variability of spiking characterized by the CV and Fano factor. • Consequence of synaptic noise on the firing properties of neurons. • Resonance in neuronal response • Spike rate adaptation • Short-term synaptic plasticity • Membrane equation including synaptic coupling • Distribution of synaptic strengths and lognormal distributions • Half center oscillator • Neural integrator • Neuronal correlations Homework: Exercises: Week 4: Network Dynamics and Attractors Lectures (chp 3 end): • Feedforward, feedback and recurrent networks • Distribution of synaptic strengths and lognormal distributions • Signal Propagation in feedforward networks • Balanced excitation and inhibition and asynchronous state in recurrent networks • Firing rate model and Wilson-Cowan networks • Inhibition-stabilization and balanced amplification • Coherent neural circuit oscillation • Spatio-temporal dynamics and traveling waves • Reservoir computing • Feedforward and Recurrent random networks Homework: Exercises: Week 5: Plasticity, Learning and Memory + Wrap Up Lectures (chp 4): • Supervised Learning + gradient descent • Unsupervised Learning + hidden structures • Reinforcement Learning + cumulative rewards • Hebbian Plasticity • Pattern formation • Homeostatic regulation • Memory consolidation Week 6: Working Memory Lectures (chp 5): • Stimulus-selective self-sustained activity • Attractor Network concept • Multiple Attractor States • Positive Feedback • Slow dynamics and short-term facilitation • Distractor filtering • Gating Mechanisms • Capacity and limitations Week 7: Cognition: Decision-Making, Value-Based Choice and Behavioral Flexibility Lectures (parts of Chpt 6,7,8) • Core decision components • Linear Dynamical Systems • Probabilistic Bayesian Models • Nonlinear circuit models • Unified Neural Circuit Model • Reward-dependent plasticity • Stochastic Foraging • Dynamical Adaptation • Power Law Reward Memories • Automaticity • Attention and Executive Control • Response Inhibition • Exogenous and Endogens Attention • Biases Week 8: Student Projects • Students will pick projects, either alone or in small groups the week before • Monday Lectures will be used to go through developments and give feedback, and present examples on project elaborations, and prepare for exam. • Thursday will be reserved for student presentations and give last heads-up before exams. Course director Rune W. Berg, Associate Professor, Department of Neuroscience, runeb@sund.ku.dk Rasmus Herlo Jensen, Assistant Professor, Center for Translational Neuromedicine, herlo@sund.ku.dk Teachers Rune W. Berg, Associate Professor, Department of Neuroscience Rasmus Herlo Jensen, Assistant Professor, Center for Translational Neuromedicine Dates Week 1: March 2-6 (mathematical concepts) Week 2: March 9-13 (Computational Modelling and Single Neuron Models) Week 3: March 16-20 (Neural Dynamics and Synaptic Coupling) Week 4: March 23-27 (Network Dynamics and Attractors) Week 5: April 7-10 (Plasticity, Learning and Memory + Wrap Up) Week 6: April 13-17 (Working Memory) Week 7: April 20-24 (Cognition: Decision-Making, Value-Based Choice and Behavioral Flexibility) Week 8: April 27-Maj 1 (Project-presentation) Exam Course location Faculty of Health and medical science, University of Copenhagen. Registration Please register before 1 February 2026 Expected frequency Once per year, spring Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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