Conferences, symposia, workshops, courses. Our members are actively involved in many events. Here is the current list of upcoming events of interest to computational neuroscience researchers.
Neuroscience 2024
Each year, scientists from around the world congregate to discover new ideas, share their research, and experience the best the field has to offer. Attend so you can: present research, network with scientists, attend session and events, and browse the exhibit hall. Join the nearly half a million neuroscientists from around the world who have propelled their careers by presenting an abstract at an SfN annual meeting — the premier global neuroscience event.
The Bernstein Network will have an information booth at this event!
EITN EBRAINS Fall School in Computational Neuroscience
The EITN Fall School in Computational Neuroscience consists of a 10-day course in theoretical and computational neuroscience, from cellular to whole-brain levels. The course is structured in thematic days with lectures, tutorials, and project work.
The course is typically aimed for PhD students, young postdocs, or master students interested to learn more about techniques of computational neuroscience, and the use of various simulation environments for model building. The students will form thematic groups to work on predefined subjects, with the help of tutors.
The course will cover cellular models, models of brain signals, circuit models and networks, mean-field models, and whole-brain models. There will be lectures and tutorials associated with these topics.
Science Days
Jedes Jahr im Herbst finden die Science Days statt – Deutschlands größtes Wissenschafts- und MINT-Festival. Gemeinsam mit Institutionen aus Wissenschaft, Bildung, und Wirtschaft wird ein faszinierendes und sehr vielfältiges Angebot zusammengestellt. MINT-Themen werden aus unterschiedlichen Richtungen und Blickwinkeln präsentiert und die Besuchenden bekommen Einblicke in hochaktuelle Themen und deren Erforschung.
Das Bernstein Netzwerk wird bei dieser Veranstaltung einen Informationsstand haben.
SPONT 2024
We invite you to participate in the “Spontaneous Activity in Brain Development” meeting, a dynamic forum for neuroscientists exploring brain activity during development. The third SPONT meeting, SPONT2024, is scheduled from 4-6 November 2024 in Altea, a picturesque seaside town near Alicante in Spain.
SNUFA 2024
This online workshop brings together researchers in the fields of computational neuroscience, machine learning, and neuromorphic engineering to present their work and discuss ways of translating these findings into a better understanding of neural circuits. Topics include artificial and biologically plausible learning algorithms and the dissection of trained spiking circuits toward understanding neural processing. We have a manageable number of talks with ample time for discussions.
Friedemann Zenke
Advanced Neural Data Analysis and Neuroinformatics (ANDA-NI)
Techniques to record neuronal data from populations of neurons are rapidly improving, allowing for simultaneous recordings from hundreds of channels while animals perform complex behavioral tasks. The analysis of such massive and intricate data sets poses significant challenges. ANDA-NI aims to equip participants with theoretical and practical training in state-of-the-art analysis approaches for neurophysiological data.
Sonja Grün
Thomas Wachtler
Hansjörg Scherberger
Martin Nawrot
Symmetry and Geometry in Neural Representations
The NeurReps Workshop brings together researchers from applied mathematics and deep learning with neuroscientists whose work reveals the elegant implementation of mathematical structure in biological neural circuitry. The first and second editions of NeurReps were held at NeurIPS 2022 and at NeurIPS 2023. The invited and contributed talks drew exciting connections between trends in geometric deep learning and neuroscience, emphasizing parallels between equivariant structures in brains and machines. This year's workshop will feature five invited talks covering emerging topics in geometric deep learning, mechanistic interpretability, geometric structure in the brain, world models and the role of dynamics in shaping neural representations.