Module 1. Modeling Behaviour (5 lectures)

• The Rescorla-Wagner Rule + Exploration-Exploitation (Boris)
• Reinforcement Learning: TDRL & Model-Based Approaches (Boris)
• Reinforcement Learning as Rational Decision-Making (Joao)
• Perceptual Decision-Making: Drift Diffusion Model (Boris)
• Inferring Latent Behavioral States (Heike Stein)

Module 2. Encoding/Decoding Models of Neural Data (3 lectures)

• Introduction to Neural Data: Encoding/Decoding Dichotomy (Joao)
• Neural Encoding (Joao)
• Neural Decoding (Joao)

Module 3. Network Models of Cognition (4 lectures)

• Building Blocks: Single Neuron Models (Pierre Houzelstein)
• Feed-Forward vs. Recurrent Networks (Joao)
• Linear Dynamical Systems (Joao)
• Modeling Complex Cognitive Tasks (Joao)

Drop me an email if you want the slides for my lectures.

Course Content (2 lectures)

• Biophysical models: from synapses to EEG
• Inferring network models from data

Drop me an email if you want the slides for my lectures.

Lecture: Cortical NMDA Receptors: From models of working memory deficits to early psychosis onset

This lecture explores the role of NMDA receptors in cortical function and dysfunction, bridging computational models of working memory with clinical observations in early psychosis. The course covers biophysical models of NMDA receptor function, their role in working memory maintenance, and how alterations in NMDA signaling can lead to cognitive deficits observed in psychiatric conditions.

Drop me an email if you want the slides for my lectures.

Lecture: Use of recurrent neural networks to study cognition

After a brief reminder on RNNs, this course introduced and explained the notions of state space, dimensionality reduction, and dynamic system. It presented a framework to reverse engineer a recurrent neural network to gain insights on their working. The course provided a comparison with neuronal data and various examples of applications: decision-making, working memory, spatial navigation (head direction and grid cells), motor cortex.

Drop me an email if you want the slides for my lectures.

Tutorial Overview

A hands-on tutorial on understanding and implementing low-rank recurrent neural networks for modeling cognitive tasks. The tutorial covers:

• Mathematical foundations of low-rank networks
• Applications to working memory and decision-making tasks
• Analysis techniques for understanding network dynamics
• Reverse engineering trained RNN

Resources:

• GitHub Repository with code and notebooks