BI NMA 03: Stochastic Processes Panel

Brain Inspired

00:00 / 01:00:48

Panelists:

Yael Niv.
- @yael_niv
Konrad Kording
- @KordingLab.
- Previous BI episodes:
  - BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.
  - BI 014 Konrad Kording: Regulators, Mount Up!
Sam Gershman.
- @gershbrain.
- Previous BI episodes:
  - BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?
  - BI 028 Sam Gershman: Free Energy Principle & Human Machines.
Tim Behrens.
- @behrenstim.
- Previous BI episodes:
  - BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.
  - BI 024 Tim Behrens: Cognitive Maps.

This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.

The other panels:

First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
Second panel, about linear systems, real neurons, and dynamic networks.
Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.