## Difficulty level

This lecture provides a summary of concepts associated with linear dynamical systems, covered in Linear Systems I (Intro Lecture) and Tutorials 1 - 4, and also introduces motor neuroscience/neuroengineering, brain-machine interfaces, and applications of dynamical systems.

Difficulty level: Beginner
Duration: 33:03
Speaker: : Krishna Shenoy

This lesson provides an introduction to the Decision Making course, specifically focusing on hidden states in neural systems.

Difficulty level: Beginner
Duration: 31:30
Speaker: : Sean Escola

This tutorial introduces the Sequential Probability Ratio Test between two hypotheses 𝐻𝐿 and 𝐻𝑅 by running simulations of a Drift Diffusion Model (DDM). As independent and identically distributed (i.i.d) samples from the true data-generating distribution coming in, we accumulate our evidence linearly until a certain criterion is met before deciding which hypothesis to accept. Two types of stopping criterion/stopping rule will be implemented: after seeing a fixed amount of data, and after the likelihood ratio passes a pre-defined threshold. Due to the noisy nature of observations, there will be a drifting term governed by expected mean output and a diffusion term governed by observation noise.

Difficulty level: Beginner
Duration: 4:46
Speaker: : Yicheng Fei

This tutorial covers how to simulate a Hidden Markov Model (HMM) and observe how changing the transition probability and observation noise impacts what the samples look like. Then we'll look at how uncertainty increases as we make future predictions without evidence (from observations) and how to gain information from the observations.

Difficulty level: Beginner
Duration: 4:48
Speaker: : Yicheng Fei

This tutorial covers how to infer a latent model when our states are continuous. Particular attention is paid to the Kalman filter and its mathematical foundation.

Difficulty level: Beginner
Duration: 2:38

This lecture covers multiple topics on dynamical neural modeling and inference and their application to basic neuroscience and neurotechnology design.

Difficulty level: Beginner
Duration: 30:40

This lecture provides an introduction to optimal control, different types of control, as well as potential applications.

Difficulty level: Beginner
Duration: 36:23
Speaker: : Maurice Smith

In this tutorial, you will perform a Sequential Probability Ratio Test between two hypotheses HL and HR by running simulations of a Drift Diffusion Model (DDM).

Difficulty level: Beginner
Duration: 4:46
Speaker: : Zhengwei Wu

In this tutorial, you will implement a continuous control task: you will design control inputs for a linear dynamical system to reach a target state.

Difficulty level: Beginner
Duration: 10:02
Speaker: : Zhengwei Wu

This lecture covers the utility of action, including the vigor and neuroeconomics of movement and applications to foraging and the marginal value theorem.

Difficulty level: Beginner
Duration: 28:48
Speaker: : Reza Shadmehr

This lecture provides an introduction to a variety of topics in reinforcement learning.

Difficulty level: Beginner
Duration: 39:12
Speaker: : Doina Precup

This tutorial presents how to estimate state-value functions in a classical conditioning paradigm using Temporal Difference (TD) learning and examine TD-errors at the presentation of the conditioned and unconditioned stimulus (CS and US) under different CS-US contingencies. These exercises will provide you with an understanding of both how reward prediction errors (RPEs) behave in classical conditioning and what we should expect to see if dopamine represents a "canonical" model-free RPE.

Difficulty level: Beginner
Duration: 6:57
Speaker: : Eric DeWitt

In this tutorial, you will use 'bandits' to understand the fundamentals of how a policy interacts with the learning algorithm in reinforcement learning.

Difficulty level: Beginner
Duration: 6:55
Speaker: : Eric DeWitt

In this tutorial, you will learn how to act in the more realistic setting of sequential decisions, formalized by Markov Decision Processes (MDPs). In a sequential decision problem, the actions executed in one state not only may lead to immediate rewards (as in a bandit problem), but may also affect the states experienced next (unlike a bandit problem). Each individual action may therefore affect affect all future rewards. Thus, making decisions in this setting requires considering each action in terms of their expected cumulative future reward.

Difficulty level: Beginner
Duration: 11:16
Speaker: : Marcelo Mattar

In this tutorial, you will implement one of the simplest model-based reinforcement learning algorithms, Dyna-Q. You will understand what a world model is, how it can improve the agent's policy, and the situations in which model-based algorithms are more advantageous than their model-free counterparts.

Difficulty level: Beginner
Duration: 9:10
Speaker: : Marcelo Mattar

This lecture highlights up-and-coming issues in the neuroscience of reinforcement learning.

Difficulty level: Beginner
Duration: 33:25
Speaker: : Tim Behrens

This lecture introduces the FAIR principles, how they relate to the field of computational neuroscience, and the resources available.

Difficulty level: Beginner
Duration: 8:47
Speaker: : Sharon Crook

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

Difficulty level: Beginner
Duration: 15:14

This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

Difficulty level: Beginner
Duration: 14:19
Speaker: : Michael Denker
Course:

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.

This lecture provides an overview of The Virtual Brain Simulation Platform.

Difficulty level: Beginner
Duration: 9:36
Speaker: : Petra Ritter