Skip to main content

Decision Making

Level
Beginner

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.

 

This course introduces the "hidden states" that neurons and networks have that affect their function and how to use graphical models with hidden states to learn about the dynamics in the world that we only have access to from noisy measurements. In addition, the course introduces multiple topics on dynamical neural modeling and inference and their application to basic neuroscience and neurotechnology design:

  • How to develop multiscale dynamical models and filters?
  • How to study neural dynamics across spatiotemporal scales?
  • How to dissociate and model behaviorally relevant neural dynamics?
  • How to model neural dynamics in response to electrical stimulation input?
  • How to apply these techniques in developing brain-machine interfaces (BMIs) to restore lost motor or emotional function?
Course Features
Lectures
Tutorials
Videos
Suggested reading
Recordings of question and answer sessions
Discussion forum on Neurostars.org
Lessons of this Course
1
1
Duration:
31:30
Speaker:

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

2
2
Duration:
4:46
Speaker:

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.

3
3
Duration:
4:48
Speaker:

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.

4
4
Duration:
2:38

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.

5
5
Duration:
30:40

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