Generalized linear models

…Participants fit and interpret linear models, interactions, generalized linear models and mixed models, learning how to connect analytical choices to data structure and report results clearly. 2027-01-18–2027-01-21, 14:00–19:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €480. Suitable for beginners and early-career researchers; no prior statistics, pr

Foraging, the set of behaviors associated with seeking valuable resources (e.g., food, water) while avoiding danger (e.g., predators), is ubiquitous among organisms, but its neural circuit basis is presently unclear. Most existing work on navigation, a key component of foraging, involves animals in small, fully observable arenas with few-to-no obstacles, partly because recording neural activity in naturalistic settings is challenging. How do animals successfully forage in complex naturalistic environments, especially given realistic neural circuit constraints on capacity and connectivity? To s

…descriptive, and mechanistic models. Sam Gershman (Harvard) presented a capacity-limited reinforcement learning framework in which policies are compressed under an information bottleneck constraint. This approach predicts pervasive perseveration, stimulus‐independent “default” actions, and trade-offs between complexity and reward. Such policy compression reconciles observed action stochasticity and response time patterns with an optimal balance between learning capacity and performan

The prefrontal cortex (PFC) plays an important role in cognitive processes such as working memory, but it is also involved in emotional and motivational processes such as anxiety and social interaction. Previous studies have shown that PFC neurons are modulated by different variables in one specific task, but it remains unclear how the same neurons respond during different tasks. Here we address this question by imaging the same PFC neurons in mice while they perform multiple tasks. We expressed the calcium indicator GCaMP6f in the medial PFC (mPFC) of C57BL/6N mice and implanted a GRIN lens t

…and removes EEG artifacts, and employs generalized linear models to extract spectral EEG features linked to cognitive decline. Clustering and cross

…by the classical predictive coding models (Rao & Ballard, 1992), or stimulus non-specific. We investiga

Generalized linear models (GLMs) are a gold-standard in computational neuroscience, allowing for the statistical investigation of neural activity when a population of neurons is exposed to stimuli (Gerwinn, 2010 et al.). Logistic regression models are one of such GLMs that allow for the prediction of two possible outcomes (i.e. binary dependent variables) based on predictor variables (i.e. the neural activity). One of such neural activity of interest is the one produced during language-related tasks in a study done by the Human Connectome Project (Binder et. al, 2020). In this study, the subje

Understanding how the brain can store and process information is one of the biggest challenges we are currently facing in the field of neuroscience. It is possible to study the brain as a whole. However, such approaches tend to only recover neural activity from a sparse subset. To counteract this problem, we are using a bottom-up neuroscience paradigm, where neurons are cultured on top of a multi-electrode array (MEA). The MEA can be used to stimulate and record neural activity. To further reduce the complexity, we culture neurons inside of a polydimethylsiloxane (PDMS) microstructure, which e

Sensory representations are typically thought as neuronal activity patterns that encode physical attributes of the outside world. However, increasing evidence is showing that as animals learned the association between a sensory stimulus and its behavioral relevance, stimulus representation in sensory cortical areas can change. In this seminar I will present recent experiments from our lab showing that the activity in the olfactory piriform cortex (PC) of mice encodes not only odor information, but also non-olfactory variables associated with the behavioral task. By developing an associative ol