Topic: Latent space

Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system

Despite the large number of active neurons in the cortex, the activity of neuronal populations is expected to lie on a low-dimensional manifold for different brain regions [1]. Variants of principal component analysis (PCA) are commonly used to assess this manifold. However, these methods are limited by the assumption that the data follows a Gaussian distribution and neglect additional features such as the curvature of the manifold. Hence, their performance as generative models tends to be subpar. To construct a generative model that entirely learns the statistics of neural activity with no a

Cognitive maps are abstract internal representations of the external world that facilitate planning and efficient navigation. How the brain learns such maps from variable sensory experience remains elusive. Previous models either relied on discrete environments [1], linear embeddings of continuous states [2], or generative models trained by predicting sensory input [3]. However, the world is neither discrete nor linear, and the brain presumably does not learn a generative model of its sensory inputs. So, how can the brain learn a map and use it to plan strategically? In this work, we develop