Topic: generalized linear models

Use R to answer research questions through descriptive and inferential statistics. Guided practicals begin with importing, checking, cleaning and visualizing data, then develop estimates, confidence intervals and hypothesis tests. 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

This webinar addressed computational perspectives on how animals and humans make decisions, spanning normative, 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

VIP inhibitory neurons in the cortex receive neuromodulatory and top-down feedback inputs, hence they are well-suited to process behavioral and predictive signals. A recent study found that VIP neurons respond to unexpected image omissions in an image sequence (Garrett et al., 2020). This omission-evoked activity may represent changes in the animal’s behavioral state (e.g., running or arousal), or it may represent a prediction-error signal that could either be stimulus specific, as suggested 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

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

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