List of upcoming talks
Speaker: Arnald Puy, Associate Professor in Social and Environmental Uncertainties, School of Geography, Earth and Environmental Sciences
Date: 4th of May 2023, 16:00 – 17:00
Format: The lectures are hybrid, and can be attended in person or online (via Zoom).
The necessary links and information is always sent via the mailing list. Please contact us if you would like to attend!
Abstract: Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuzzier estimates because it generally increases the model’s effective dimensions, i.e., the number of influential parameters and the weight of high-order interactions. By tracking the evolution of the effective dimensions and the output uncertainty at each model upgrade stage, modelers can better ponder whether the addition of detail truly matches the model’s purpose and the quality of the data fed into it. Based on: Puy, Arnald, et al. “Models with higher effective dimensions tend to produce more uncertain estimates.” Science Advances 8.42 (2022): eabn9450.
Speaker: Jorge Mejias
Date: 26th of May 2023, 16:00 – 17:00
Title: Simulating large-scale brain networks and their inner workings
Format: The lectures are hybrid, and can be attended in person (please subscribe to the attendance list) or online (via Zoom).
The necessary links and information is always sent via the mailing list. Please contact us if you would like to attend!
Abstract: Computational models of large-scale brain networks have been traditionally focused on reproducing brain dynamics such as resting state activity. However, embedding the mechanisms and structure needed to reproduce brain functions related to perception and cognition, in a way that also matches the neuroanatomical and electrophysiological evidence, has been more challenging. In this talk, I will present two recent examples of computational models of brain networks which include rudimentary but behaviorally relevant functions. The first example will focus on how the delay activity underlying working memory may emerge as a distributed phenomenon across multiple regions of the macaque and human brains –rather than restricted to prefrontal areas as assumed in classical computational models. In the second example, I will present simulations of the mouse brain which show that the integration of signals from multiple sensory sources occurs across multiple brain areas in a context-dependent way. These computational results, strongly constrained by anatomical and electrophysiological data, suggest that both working memory and multisensory integration are intrinsically distributed phenomena in the brain.