**C1.- Data Analysis of Calcium Imaging Signals in Neural Circuits:**

Supported by:

**Organizers****: Germán Sumbre** (Institut de Biologie de l ́École Normale Superieure, CNRS, INSERM) and **Violeta Medan** (IFIBYNE-UBA/CONICET y FCEN-UBA).

**Instructors:**** Sebastián Romano **(Instituto de Investigación en Biomedicina de Buenos Aires, IBIOBA-MPSP, CONICET) and** Emiliano Marachlian** (Institut de Biologie de l ́École Normale Superieure, CNRS, INSERM).

**Teaching Assistants:** **Nicolás Martorell **(IFIBYNE-CONICET/UBA) and **Verónica Pérez Schuster **(iB3-FBMC y DF-FCEN, UBA).

Audience:

Undergraduate and graduate students in the fields of biology, physics, engineering, computer science, and related disciplines. Basic programming knowledge, especially in MATLAB and/or Python, is desirable.

Overview:

The main aspects of calcium imaging data analysis will be covered, from neuron detection to population-level analysis of calcium signals. The activities will be organized into classes that introduce theoretical concepts during the mornings and practical sessions with pre-collected datasets (provided by the instructors or contributed by the students) in the afternoons, to practice different analysis techniques.

Course objectives:

By the end of the course, students are expected to:

- Understand the basic concepts of in vivo calcium signal acquisition, advantages, and limitations of different acquisition techniques.
- Learn techniques for handling and preprocessing imaging data, with a focus on managing large datasets. Software, toolboxes, and analysis strategies.
- Become familiar with typical pipelines for analyzing fluorescence time series.
- Grasp the basic concepts of large dataset analysis techniques: topography, dimensionality reduction and clustering, linear regression, and deconvolution.

**DOWNLOAD THE PROGRAM: Análisis de Datos de señales de Imaging de Calcio de circuitos neuronales**

**C2.- Spatial filtering techniques for electroencephalography signals**

**Organizer:** **Victoria Peterson** (Instituto de Matemática Aplicada del Litoral, IMAL, UNL-CONICET Santa Fe, Argentina; Facultad de Ingeniería Química, FIQ-UNL, Santa Fe, Argentina).

**Instructors:**** Catalina María Gálvan** (Instituto de Matemática Aplicada del Litoral, IMAL, UNL-CONICET Santa Fe, Argentina; Facultad de Ingeniería Química, FIQ-UNL, Santa Fe, Argentina) and **Bruno Zorzet** (Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), UNL-CONICET, Santa Fe, Argentina).

Audience:

Undergraduate and graduate students in the fields of biology, physics, engineering, computer science, and related disciplines.

Requirements:

Basic understanding of linear algebra, optimization and programming (preferably in Python).

Overview:

Brain activity recorded through surface electroencephalography (EEG) can be thought of as the result of a linear mixture of different statistical sources. These sources can originate from the group of neurons underlying the EEG sensor location, as well as neighboring groups of neurons. Additionally, other non-brain sources may be present in the EEG recordings, which ultimately will be defined as signal artifacts.

Statistical generative models assume that brain signals arise from the activity of uncorrelated sources, and these sources appear distorted in the recorded signal as a consequence of the linear mixing process.

In the context of spatial filtering, the objective is to transform the signal that exists in the “sensor” space to the “source” space. Spatial filtering methods can be used to improve the signal-to-noise ratio, identify the most correlated source to a specific event, find independent sources, etc. Thus, the application of spatial filters to the EEG signal could be performed for: (i) reducing the dimensionality of the input signal, (ii) feature extraction, (iii) elimination of noise sources. Throughout this course, the main spatial filtering algorithms used in EEG signal processing to enhance the signal-to-noise ratio, extract features, and remove artifacts will be reviewed.

Course objectives:

By the end of the course, students are expected to:

– Understand the basic neurophysiological concepts underlying electroencephalography signals

– Learn basic methods of spatial filtering of time series.

– Understand basic concepts of statistical signal processing.

– Acquire basic implementation skills in MNE-Python of specific spatial filtering methods.