Topic: local learning rules

A fundamental problem in systems neuroscience is understanding how the brain learns the non-linear dynamics of the complex world and identifies the environment's state. Data-driven learning in such high-dimensional spaces requires lifting the curse of dimensionality. One way of reducing dimensionality relies on extracting from observed trajectories the underlying topological skeleton, i.e., fixed points connected by invariant manifolds. Thus, online extraction of fixed points from input trajectories is an important task. Whereas Dynamic Mode Decomposition (DMD) provides a framework to calculat

Learning in the brain is thought to be largely unsupervised since the bulk of an animal’s natural experience arrives neither with associated reinforcement signals nor explicit supervisory signals. A compelling account of unsupervised learning in animals is provided by the notion that the brain learns by trying to predict the future, thereby identifying statistical redundancies in sensory inputs over time. Based on this principle, self-supervised learning (SSL) techniques have enabled deep artificial neural networks (ANNs) to achieve competitive results on challenging perceptual tasks. SSL atte

The neocortex is composed of a hierarchy of regions and is thought to perform sensory inference by combining two complementary information streams: 1) a feedforward stream propagating bottom-up in the hierarchy, representing sensory information, and 2) a feedback stream propagating top-down, representing expectations or priors derived from contextual information integrated in higher-order associative regions. Within a cortical region, pyramidal neurons in layers 2 & 3 are a key cellular component of the feedforward pathway which integrate these two streams at their basal and apical dendrit