This workshop provides a hands-on learning experience with a focus on a wider variety of AI tools, their ethical implications and their practical applications. The aim is to facilitate the responsible and efficient use of AI-based tools in research and academia.

Content:

• Understand the importance of using AI in research and academia and assess the benefits and risks involved
• Craft effective prompts for your research tasks
• Develop strategies to integrate AI tools into your research workflow
• Stay informed about and adapt to new developments in the field of AI

At the end of the workshop, you will receive a list of generative AI prompts useful in research and academia. There will be practice sessions during the workshop for which you will need access to AI tools, particularly ChatGPT/GPT-4o. If you do not have an account with ChatGPT/GPT-4o, alternatives like Microsoft Copilot, Google Bard or Claude.ai could also be used.

In the workshop “Fit for AI - Prompting for advanced users” you can get to know and try out prompting tips and techniques. You will learn about prompting techniques such as Few Shot Prompting, Chain-of-Thought Prompting and others. You can try out these techniques directly on various tasks and your own examples.

This seminar focuses on the increasing importance of Artificial Intelligence (AI) in academic research and writing, providing practical insights into AI technologies; use in these areas.The workshop explores ChatGPT and prompt engineering, as well as other academic AI tools to aid research and writing, examining both benefits and challenges. Ethical aspects, such as copyright and authenticity of research results, are discussed, with the goal of equipping participants with practical knowledge and skills to effectively utilize AI in daily research through interactive elements like case studies and group discussions.

The course is practical and aims at teaching students how to:

• Use the programming environment R and RStudio, which includes installation, how to handle errors, problem solve and access helper documents.
• Use basic concepts of programming, such as data types, logical and arithmetic operators, if else conditions, loops and functions.
• Use common R packages to perform basic statistical analysis (e.g., t-test, chi2-test, correlation) and visual presentation (e.g., boxplot, histogram and heat-map) of data in R.

The course is structured with the intent to gradually make students more autonomous in writing code. Starting by introducing a concept through a lecture, then providing formative quizzes and tasks relateed to the concept. This all leads up to a project (exam) where the student gets to combine multiple concepts into a project with the intent of solving a certain problem or displaying specific statistical tests of visual components. 

Do you need to turn data into a publication figure? We offer tools and confidence for the student to independently select a statistical method for research questions in their field. The course is practical and includes implementing a basic statistical analysis in R, the leading statistical programming language in bioinformatics and medical science. Furthermore, we give a brief introduction to visualization in R, with a focus on R/ggplot2. Students can bring data from their own research project, or work on data from the course.

Topics covered include:

• Computational design strategies
• Differential equations
• Programming in Python
• Data analysis

Topics covered include:

• Coding: theory, practical training, coding styles, unit testing
• Collaborative software development workflows
• Data analytics workflows
• (Generalised) linear mixed effects models
• Bayesian statistics
• Data visualisation
• Workflow automation
• Meta-science

Topics covered include:

• Reconstruction of neuron morphologies
• Histological preparation of brain tissue
• Electrophysiological recordings of single neurons in vivo
• Simulations of cellular function via multi-compartmental neuron models

Topics covered include:

• linear and nonlinear time series analysis methods for the characterization of complex dynamical systems
• statistical tools
• analysis of biomedical data (e.g. EEG, structural/functional MRI data)

This course will cover:

• Intro to Jupyter Notebooks, IDEs
• Intro Python (loops, variables, functions)
• Core packages (Numpy, Pandas, Matplotlib, Seaborn)
• Accessing folders (shell, OS)

The module presents a variety of fundamental models and methods from computational neuroscience. By solving daily exercises the students learn how to practically apply the acquired concepts. The course introduces the employed more
advanced mathematical tools embedded into the different topics. Further there will be a pre-course teaching the required programming skills in python.

This course will provide up-to-date insights into the neurobiological basis of language. The course will be given by internationally leading researchers in the field. Students will learn how state-of-the-art methods and approaches are currently being applied, and what are the next big questions for the field. 

The course consists of theoretical sessions and practical work related to decision-making, memory formation and emotion. It will also include the neuroanatomy related to these functions using both MRI and human brains. The participants will be actively involved in group work dealing with practical and theoretical aspects of cognitive neuroanatomy.

Selection will be based on:
1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation),
2) start date of doctoral studies (priority given to earlier start date)

Developmental biology lies at the heart of an effort to understanding complex biological systems. By studying how neural circuits are assembled we can extrapolate key aspects of their function as well as devise strategies for their repair. This course is given to deepen the understanding of how molecular and cellular mechanisms underlie neurobiological function and to widen the horizon of students within the strong Karolinska neuroscience community.

Contents of the course: The course will cover the main steps of development from neural stem cells to mature circuits, including the patterning of the neural plate and thus the origin of cell types, the interplay between intrinsic and extrinsic factors, gene regulation including epigenetics, neuro-glia interactions and the role of network activity in shaping the final circuits. Different molecular and tracing technologies, and model organisms will be covered. An important aspect of the course regards molecular technologies for labeling, transcriptional analysis, and genetic manipulation of defined neural populations. Connections between aberrant developmental processes and neurodevelopmental and neurological disorders will be discussed.

Course director

The course is given by four course-leaders: Gonçalo Castelo-Branco, Jens Hjerling-Leffler and Ulrika Marklund all at MBB and François Lallemend at Dept of Neuroscience.

Experimental neuroscience is key to progress in the understanding of how the brain functions. The experimental toolbox for studies in rodents is currently without comparison, allowing detailed investigation of how the brain is built and the function of brain circuits. Technological advances also make it possible to directly connect neurons and circuits to behaviour. 

In the Brain Circuits course, students will meet international and KI neuroscientists who have made significant contributions to the study and understanding of neuronal circuits and behaviour. The development and application of novel technologies and analysis (high-density electrophysiology and imaging of single-neuron activity, optogenetics, behavioural tracking, machine learning etc) will be covered, with a focus on advances using transgenic rodents. We have a strong emphasis on engaging junior neuroscientists in the course and on creating a network for future neuroscience leaders.