Title: Teaching Large Language Models to Reason with Reinforcement Learning Abstract: In this talk, I will discuss how we can use Reinforcement Learning (RL) to improve reasoning in Large Language Models (LLM), as well as when, where, and how to refine LLM reasoning. First, we study how different RL-like algorithms can improve LLM reasoning. We investigate both…

Title: Phase transition in learning with neural networks  Abstract: Statistical physics has studied exactly solvable models of neural networks for more than four decades. In this talk, we will put this line of work in perspective of recent questions stemming from deep learning. We will describe several types of phase transition that appear in the…

Title: Neural basis for skilled movements Abstract: Generating behavior is an incredible achievement of the nervous system, considering the range of possible actions and the complexity of musculoskeletal arrangements. Motor control involves understanding the surrounding environment, selecting appropriate plans, converting those plans into motor commands, and adaptively reacting to feedback. This seminar will review efforts…

Title: Scope of the working group, example project, and literature Short Description: From Nikolaus Kriegeskorte's (Professor of Psychology and of Neuroscience (in the Mortimer B. Zuckerman Mind Brain Behavior Institute) lab, Eivinas Butkus (grad student) will show an example of a modeling project optimizing energetic demands along with accuracy in a vision task, and Josh…

Title: Efficient coding in reward neurons Abstract: Two of the greatest triumphs of computational neuroscience have been efficient coding accounts of tuning properties of sensory neurons and reinforcement learning accounts of dopaminergic neurons in the midbrain. At first glance, these theories seem to have no connection, but I will argue that they do. One can…

Title: Translating computational mechanisms to clinical applications Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. In this lecture, I will provide an overview over recent approaches for translating computational research into an understanding of symptoms, and mechanisms…

Title: A recurrent network model of planning explains hippocampal replay and human behaviour Abstract:  When faced with a novel situation, humans often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behaviour must compensate for the time spent thinking. I will show how we recently captured these features…

Title: The Geometry of Abstraction Abstract: I'll first discuss the theoretical framework introduced in Bernardi et al. 2020, Cell, in which we propose a possible definition of abstract representations. I'll go into the details of the most up-to-date  conceptual framework, discuss the computational relevance of the representational geometry and the cross-validated measures of representational geometry that we…

Title: Risking your Tail: Curiosity, Danger & Exploration Abstract: Risk and reward are critical balancing determinants of adaptive behaviour, associated respectively with neophobia and neophilia in the case of exploration. There are rather great differences in how individuals engage with novelty - with substantial consequences for what they are able to learn. Here, we consider…