BI NMA 03: Stochastic Processes Panel
from Brain Inspired Hosted by Paul Middlebrooks
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.
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