Topic: latent space

Many of our most impressive skills, from speech to music, evolve across many dimensions and are learned spontaneously without external reinforcement. While we know much about how the basal ganglia contribute to learning low-dimensional, externally reinforced behaviors like lever pressing, their contribution to learning high-dimensional behaviors is less clear. Birdsong learning is a paradigmatic example of high-dimensional learning. Juvenile songbirds vary many acoustic dimensions as they learn to copy their tutor’s song, a spontaneous process that evolves without external reinforcement. While

The 3D morphology of cortical neurons is highly complex with widely varying shapes and has long been used to classify neurons into cell types. Classification based on morphology has classically been done by either laborious expert analysis through visual inspection or by computing a set of predefined features that could be quantitatively measured. Both approaches are prone to bias and subjectivity. The classification of neurons by experts was shown to have a high variance, and becomes infeasible with larger datasets. Similarly, the definition of fixed morphological features for classification

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

We propose a novel dual-model framework that integrates reinforcement learning (RL) and adaptive control to model cerebellar function in motor learning and adaptation. Our approach distinguishes between the roles of the basal ganglia and cerebellum, addressing limitations of current models in complex motor control tasks. We introduce a decomposition of the value function where the RL component optimizes the mean expected value while the cerebellar module minimizes deviations from optimal latent trajectories. This decomposition approximates a normative information-theoretic objective where the

Understanding how the brain encodes stimuli is a fundamental problem in computational neuroscience. This has inspired the development of artificial neural networks that learn representations by incorporating brain-like learning abilities. Recent work has focused on learning representations (latents) by capturing similarity between inputs and latents, but these methods have typically been used to learn downstream features from the input and have not been studied in the context of a generative paradigm, where one can map the representations back to the input space, incorporating not only bottom-

Elements of neural networks (NNs), both biological and artificial, can be described by selectivity to specific cognitive features. Revealing such features is important for understanding NNs' inner working mechanisms. For living systems, where the neural response to a stimulus is not a known function, one needs to build a feedback loop of exposing it to a set of different stimuli. The properties of such stimuli should be iteratively varied aiming in the direction of neuronal maximal response. To utilize such a loop for a biological network, one should run it quickly and efficiently, reaching th