ISyN

Systems Club II - Mouse Behavioral Tracking
2022-07-29 14:00 - 19:00 UTC | Biozentrum 1, Hanns-Dieter-Hüsch-Weg 15, Mainz, RLP, 55128, Germany
Methods for deriving Resilience against stress in mice from long term observations

When individuals in major psychological or physical stressful situations are able to maintain their mental health or restore it quickly after a short period of experiencing stress-related symptoms, this is referred to as resilience. The classification of mice into resilient and susceptible animals based on tests such as chronic social defeat test, is a common approach in resilience research. However, this classification based on short-term observation has recently been viewed increasingly critically and its transferability to the definition of resilience in humans has at least been questioned. But which methods can give us information about the long-term behavior of mice and is this really helpful for determining the resilience status of a mouse?

In our Systems Clubs we want to encourage you, to join the discussion. Bring your own opinions and if you think it is helpful, feel free to bring a short presentation giving a new impulse on the topic at hand. This Systems Club is particularly aimed to discover different methods for mouse behavioral tracking and to discuss them together as well as how to define resilience in general.

2 – 3:30PM: Welcome, Presentations and Discussion Round on:

The IntelliCage System and Data Analysis using IntelliPy (Nicolas Ruffini)VAME, Deeplabcut & Analysing generic tracking and behavioral data using Resilipy (Vincent Dietrich)Using and analyzing data with the LiveMouseTracker System (Saskia Göbler & Nicolas Ruffini)

3:30 – 4:30PM: Short tours showing the Live Mouse Tracker System and/or the IntelliCage System

Tour on the IntelliCage SystemTour on the Live Mouse Tracker System

While you are waiting for your next tour slot or if you are already done, you can start joining the catering and open discussion .

4:30PM – Open End: Discussion with Catering and Wine

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top.

After you registered for this event, you will get an e-mail asking you for some more details regarding e.g. tours you’d like to participate in.

Deep Learning using Keras and Tensorflow
2022-05-27 09:00 - 13:00 | LIR Mainz, Wallstraße 7, Mainz, 55122, Germany
Motivation

Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.

Course Details:

This course will be an introduction to deep learning. We will talk about different concepts, neural network (NN) architectures and their respective implementations in Python. Concepts and algorithms are of course library independent, nevertheless we will mainly focus on using the Keras Framework (part of Tensorflow) to implement different models. After some theoretical discussion we will setup together the deep learning environment and try to solve different problems using different NN architectures.

Requirements

You should already feel quite comfortable in programming in python and bring some prior knowledge in how to work with pandas/numpy if possible. I would recommend to participate in the “Scikit-Learn: Using Machine Learning in Python” courses if you are not comfortable with machine learning at all yet. I would advise to use a computer with a NVIDIA GPU to speed up computaions, but this is not mandatory.

Correspondence:

For questions write to:

Stanislav Sys – stsys@uni-mainz.de or Stephan Weißbach – s.weissbach@uni-mainz.de (regarding Content)

Nicolas Ruffini Nicolas.ruffini@lir-mainz.de (regarding Event)

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top

Scikit-Learn: Using Machine Learning in Python
2022-05-20 09:00 - 13:00 | LIR Mainz, Wallstraße 7, Mainz, 55122, Germany
Motivation

Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.

This is the third appointment out of four in our 2022 spring Python Course Series. Here we want to cover some basic machine learning techniques with the Python package scikit-learn and deepen our skills in plotting with seaborn.

Course Details:

This course will show you how to install scikit-learn on your machine and how to use some basic Machine Learning algorithms. We will talk about different concepts in Machine Learning, about how to tackle which problems and of course also program to solve some Machine Learning problems together in Python.

Requirements

You should already feel quite comfortable in programming in python and bring some prior knowledge in how to work with pandas, the data managment library in python. If you joined the second course and were able to follow its contents, you should also be able to follow all concepts introduced in this course- even though this time we will have to introduce some more theoretical concepts than before!

Correspondence:

For questions write to:

Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top

Programming and Plotting in Python for Beginners with prior knowledge
2022-05-13 09:00 - 13:00 | LIR Mainz, Wallstraße 7, Mainz, 55122, Germany
Motivation

Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.

This is the second appointment out of four in our 2022 spring Python Course Series. Here we want to cover programming in Python for users with some prior knowledge and extend the insights we got in the first course by learning the usage of some of the most useful libraries in Python for data handling and plotting.

Course Details:

This course will show you some more advanced concepts when programming with Python. We will load additional packages in Python, and install third-party packages using pip. We will further focus on loading and working with tables using pandas and plotting with seaborn and matplotlib.

Requirements

Some knowledge about basic concepts of programming is helpful for this second course.

For this course, you should already have Python installed on your machine.

Try to install Python here (basic Python):https://www.python.org/downloads/

Or here (including some helpful packages and package managing):https://www.anaconda.com/products/individual

You can also start getting familiar with Python, e.g. using one of these online courses:https://www.datacamp.com/courses/intro-to-python-for-data-sciencehttps://www.codecademy.com/learn/learn-python-3

Correspondence:

For questions write to:

Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top

Python for total Beginners
2022-05-06 09:00 - 13:00 | LIR Mainz, Wallstraße 7, Mainz, 55122, Germany
Motivation

Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.

Thus, we want to offer a weekly Hands-On programming course in Python, to start with basic programming concepts for total beginners and then tackle more advanced topics in the following three appointments.

Course Details:

This course will show you how to install Python on your machine and together we aim to understand the basic concepts of programming in Python. We will have a look into some basic objects in programming such as integers, floats, strings and will cover some concepts such as loops and if-else conditions.

Requirements

No deeper knowledge about programming is necessary for the first course. If you have some preferences and / or are already familiar with some concepts of Python or programming in general, join form the second/ third/ fourth course on, or just tell us, so we can shift our focus if many of you are interested in a specific topic.

If you already want to prepare some more, try to install Python here (basic Python):https://www.python.org/downloads/

Or here (including some helpful packages and package managing):https://www.anaconda.com/products/individual

You can also start getting familiar with Python, e.g. using one of these online courses:https://www.datacamp.com/courses/intro-to-python-for-data-sciencehttps://www.codecademy.com/learn/learn-python-3

Correspondence:

For questions write to:

Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top

Systems Club I - Calcium Imaging Pipeline:
2022-04-29 14:00 - 19:00 | Biozentrum 1, Hanns-Dieter-Hüsch-Weg 15, Mainz, RLP, 55128, Germany
From Data Acquisition to Analysis

The field of optical calcium imaging, particularly of individual neuron function in the intact tissue, relies on detecting subtle changes in light intensity. The implementation of 2-photon microscopy in combination with calcium indicators allowed for the first time a functional readout of neuronal circuits comprising of several hundred neurons in the rodent cortex. This led to a tremendous advance in our understanding on how complex circuit dysfunction arise, particularly also in early stages of neurological disorders. Particularly 2 photon calcium imaging approaches pose unique challenges, both in terms of system integration, experimental design, and not the least, analysis. Most of these steps are processed quite differently depending on the laboratory. Here, in a hands-on-manner, we aim to show you our procedure of data acquisition and analysis, including the GUI ViNe-Seg, which has been recently developed in the Stroh-Lab.

In our Systems Clubs we want to encourage you to join the discussion. Bring your own opinions and if you think it is helpful, feel free to bring a short presentation giving a new impulse on the topic at hand. This Systems Club is particularly aiming for initiating a discussion of different procedures for image processing and downstream analyses of calcium imaging data.

The planned time table is the following:

2 – 3:30PM: Welcome, Presentations and Discussion Round on:

The Data Acquisition (Hendrik Backhaus)The Autosegmentation, Trace Extraction, Baseline Estimation and Peak Detection (Nicolas Ruffini, Saleh Altahini, Hendrik Backhaus)The Downstream Analysis (Anna Wierczeiko, Nicolas Ruffini)

3:30 – 4:30PM: Short tours showing the Experimental Calcium Imaging Setup in RG Stroh

2-Photon Microscope TourMiniscope & Electrophysiology Tour

While you are waiting for your next tour slot or if you are already done, you can start joining the catering and open discussion.

4:30PM – Open End: Discussion with Catering and Wine

Registration:

Registration is available after login – if you do not have an ISyN-Account yet, create one, using the “Become a member”-Button in the top

After you registered for this event, you will get an e-mail asking you for some more details regarding e.g. tours you’d like to participate in.

Scikit-Learn: Using Machine Learning in Python
2021-12-03 09:00 - 13:00 | RLP
Motivation
Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.
Thus, we want to offer a biweekly Hands-On programming course in Python, to start with basic programming concepts for total beginners and then tackle more advanced topics in the following three appointments.
Course Details:
This course will show you how to install scikit-learn on your machine and how to use some basic Machine Learning algorithms. We will talk about different concepts in Machine Learning, about how to tackle which problems and of course also program to solve some Machine Learning problems together in Python.
Requirements
No deeper knowledge about programming is necessary for the first course. If you have some preferences and / or are already familiar with some concepts of Python or programming in general, join form the second/ third/ fourth course on, or just tell us, so we can shift our focus if many of you are interested in a specific topic.
If you already want to prepare some more, try to install Python here:
https://www.python.org/downloads/
You can also start getting familiar with Python, e.g. using one of these online courses:
https://www.datacamp.com/courses/intro-to-python-for-data-science
https://www.codecademy.com/learn/learn-python-3
Correspondence:
For questions write to:
Roberta Guimaraes-Backhaus Roberta.Backhaus@lir-mainz.de or
Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Programming and Plotting in Python for Beginners with prior knowledge
2021-11-05 09:00 - 2021-11-26 13:00 | RLP
Offline course for four groups, each at one friday in november.

Motivation
Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.
As we had more registrations than we could handle for the advanced programming course and no registrations were made for plotting only, we decided to separate all registrations into four groups instead of two groups. Consequently, we can arrange four appointments with each approx. ten participants in november for learning both, programming in Python for users with some prior knowledge and usage of some of the most useful libraries in Python for data handling and plotting.
Course Details:
This course will show you some more advanced concepts when programming with Python. We will load additional packages in Python, and install third-party packages using pip. We will further focus on loading and working with tables using pandas and plotting with seaborn and matplotlib.
Requirements
Some knowledge about basic concepts of programming is helpful for this second course.
For this course, you should already have Python installed on your machine.
Try to install Python here (basic Python):
https://www.python.org/downloads/
Or here (including some helpful packages and package managing):
https://www.anaconda.com/products/individual
You can also start getting familiar with Python, e.g. using one of these online courses:
https://www.datacamp.com/courses/intro-to-python-for-data-science
https://www.codecademy.com/learn/learn-python-3
Correspondence:
For questions write to:
Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Python for total Beginners
2021-10-22 09:00 - 13:00 | RLP
Registration Closed
Motivation
Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.
Thus, we want to offer a biweekly Hands-On programming course in Python, to start with basic programming concepts for total beginners and then tackle more advanced topics in the following three appointments.
Course Details:
This course will show you how to install Python on your machine and together we aim to understand the basic concepts of programming in Python. No deeper knowledge about programming is necessary for this first course. If you have some preferences and / or are already familiar with some concepts of Python or programming in general, join form the second/ third/ fourth course on.
Requirements
No deeper knowledge about programming is necessary for the first course. If you have some preferences and / or are already familiar with some concepts of Python or programming in general, join form the second/ third/ fourth course on, or just tell us, so we can shift our focus if many of you are interested in a specific topic.
If you already want to prepare some more, try to install Python here (basic Python):
https://www.python.org/downloads/
Or here (including some helpful packages and package managing):
https://www.anaconda.com/products/individual
You can also start getting familiar with Python, e.g. using one of these online courses:
https://www.datacamp.com/courses/intro-to-python-for-data-science
https://www.codecademy.com/learn/learn-python-3
Correspondence:
For questions write to:
Nicolas Ruffini Nicolas.ruffini@lir-mainz.de

Hands-on Programming - Introduction to Python
2021-06-29 08:00 - 17:00 | RLP
Motivation
Python is a general-purpose, versatile and popular programming language.Python is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read.
Outline
In this Hands-On Tutorial, I will try to get you familiar with the Installation, Usage and the benefits of the Python Programming language. Depending on your own skill set and interests, we can dive deeper into the pandas module for Data Science, SciKitLearn for some basic Machine Learning or just get familiar with the general properties of Python.
Requirements
No deeper knowledge about programming is necessary. If you have some preferences and / or are already familiar with some concepts of Python or programming in general, tell me, so I can try to prepare the course accordingly.
If you already want to prepare some more, try to install Python here:
https://www.python.org/downloads/
You can also start getting familiar with Python, e.g. using one of these online courses:
https://www.datacamp.com/courses/intro-to-python-for-data-science
https://www.codecademy.com/learn/learn-python-3
Correspondence:
For questions write to: Nicolas.ruffini@lir-mainz.de

Hands-on Data Analysis: IntelliPy
2021-06-21 08:00 - 17:00 | RLP
Python GUI for analyzing IntelliCage data
Motivation
The IntelliCage system helps researchers to conduct behavioral experiments and learning experiments with mice while ensuring minimal human intervention. The animals can be observed for long time periods – up to several weeks. This long-term data acquisition can provide new insights in mouse behavior, that might not be detectable in short-term observations. However, analyzing those big amounts of data is challenging for many researchers.
IntelliPy aims to simplify and automize many aspects of the analysis, such as acquiring data per group, creating learning curves or pivoting parameters in different timeframes. All plots are automatically created and the final tables for statistical tests are stored separately for the user.

Outline
In this Hands-On Tutorial, I will try to get you familiar with the Installation, Usage and the benefits of the IntelliPy GUI.
Requirements
No deeper knowledge about programming is necessary, as IntelliPy is aimed to be useable by scientists with little or no programming / data science experience.
Details
IntelliPy is capable of doing three kind of analyses:

Pivot Analyses
Learning Analyses
Sucrose Analyses

Pivot Analyses:
For the parameters, measured by the IntelliCage Systems, such as Lick Duration, Nosepoke Number or NosepokeDuration, pivot tables are created for each module by IntelliPy. By default, these timeframes are created per day, but the user can add more timeframes using the IntelliPy GUI, like e.g. 12-hour or 6-hour timeframes. For further sttistical analyses, the pivoting results are stored as CSV files.
Learning Analyses:
As the experiments conducted with the IntelliCage systems can be conducted as learning experiments with different setups per phase, the learning rate of each individual as well as for the each group can be of high interest. Rather than only the final rate of correct attempts, the rate per hour and per visit is computed and plotted by IntelliPy. This enables the user, to utilize longitudinal learning information for each individual and group. For those learning rates, it is even possible to include all nosepokes or to remove those that were not followed by a lick. It can be argued about, whether a nosepoe without a lick should or shouldn’t be accounted as a correct attempt, so this decision is up to the user. Furthermoe, there are both, the possibility to exclude all nosepokes not followed by a lick or to treat them as incorrect attempts.
Sucrose Analyses:
For learning experiments, including the choice between water and sucrose, the proportion of LickDuration spent for Sucrose over time is computed by IntelliPy. Additionally, it is possible to define Sucrose and Water or just one of both as correct for the learning rate.
Correspondence:
For questions write to: Nicolas.ruffini@lir-mainz.de
