Talks and Events

Despite an unprecedented number of women entering neuroscience, and decades-long recruitment and retention efforts, women continue to be disproportionately underrepresented in European academic tenure-track faculty and leadership positions. This Perspective focuses on two major career points where women exhibit diminished representation: the transition from postdoctoral fellow to junior professor and the promotion to more senior (tenured) faculty positions. We discuss below recently implemented country-specific and Europe-wide initiatives supporting equal career progression and propose further concrete steps to be taken to break down the structural barriers that prevent women’s progression up the academic career ladder as European neuroscientists.

Understanding the nature of our mental representations is a central aim of the cognitive sciences. In this talk, I will discuss past, present, and future work from our lab targeted at (1) unraveling the nature of these representations, (2) revealing their neural substrate along the ventral visual system, and (3) identifying the representations uniquely associated with vision and semantics. To achieve these aims, we draw on a range of methods ranging from computational modeling of large-scale online behavioral data, the development and use of densely-sampled neuroimaging datasets comparing representations of images and words and those of sighted and blind individuals, and a direct comparison of neural representations of objects in humans and macaque monkeys. Together, our present results support a multifaceted view where humans make sense of the world around them by combining a set of representational dimensions to structure their environments, form categories and communicate their knowledge with others.

You can find the program here: buerger.uni-frankfurt.de/143422054/programmbroschure-frankfurter-burger-universitat-wintersemester-2023-24.pdf

Welcome to our workshop on Computational Models for Neuroscience, where we will delve into the fascinating intersection of artificial intelligence and cognitive research. In recent years, deep neural networks (DNNs) have emerged as powerful computational models for understanding the complexities of the primate visual cortex. Numerous studies have highlighted the potential of DNNs in unravelling the computational principles and neurobiological mechanisms behind visual processing.
To facilitate this cutting-edge research, we introduce Net2Brain, a comprehensive toolbox designed to map model representations to human brain data. Unlike existing toolboxes that primarily focus on supervised image classification models, Net2Brain goes beyond by enabling the extraction of activations from diverse visual tasks, including semantic segmentation, depth estimation, and action recognition. With over 600 pre-trained DNNs and support for custom models, Net2Brain simplifies the entire process from feature extraction to analysis, offering a seamless pipeline for researchers. The toolbox computes representational dissimilarity matrices (RDMs) over activations, allowing for in-depth comparisons with brain recordings using representational similarity analysis (RSA) and weighted RSA, employing both ROI-based and searchlight analyses.
Net2Brain is an open-source toolbox that comes with preloaded brain data for immediate testing, and it seamlessly accommodates the integration of your own recorded data. Join us as we explore the vast potential of Net2Brain in advancing our understanding of the brain’s visual processing through computational models.

MNE-Python is a library that has rapidly become one of the most widely used tools for M/EEG analysis. In this brief workshop, after a recap on the basics of the event-related potential (ERP) approach to M/EEG analysis, we’ll walk through an analysis of some example data in MNE-Python and explore options for pre-processing and epoching the data. Finally, most likely in R, we will explore options for fitting robust models to epoched data that can be used to describe patterns and test hypotheses.