Upcoming lab teachings
posted on August 29, 2018


Upcoming lab teachings

Every Friday, we get together (over pizza, sometimes) for lab teachings. On a rotating basis, each member of the lab speaks and teaches about something they know. Anything, really. Relevant and interesting topics, good skills to know, nice Python packages, neuroscientific princples, new findings and literature reviews… whatever!

Get on the listserve for announcements: https://groups.google.com/forum/#!forum/kording-lab-teachings

Fall 2019/ Spring 2020 topics

Date Name Topic
November 27 Shaofei Wang Differentiable Structured Inference and Attention
December 4 Rachit Saluja TBD
December 18 Titipat Achakulvisut TBD

Requests and suggestions

  1. Recent progress in NLP (Transformer networks, pretraining methods…)
  2. Graph Convolution Technique

Recently taught topics

For inspiration. Add ones you’ve done!!

Date Name Topic
Sept. 28: Ilenna Capacity of Neural Networks
Oct. 5: Tung Pham GANs for EEG
Oct. 12: Ben GPUs – beneath the heatsink Slides
Oct. 19: Rachit Graph Convolution Networks
Oct. 26: Tony Docker for science
Nov. 2 Titipat AllenNLP library and a little bit of Pytorch
Nov. 9 Roozbeh Multiple Hypothesis Testing
Nov. 16 David Reinforcement learning and catastrophic forgetting
Dec. 3 Ari Independent Component Analysis
Jan. 9 Netanel Ofer Automated Analysis of Interneuron Axonal Tree Morphology and Activity Patterns
Jan. 18 Nidhi Dynamic Time Warping
Jan. 25 Ben Bandit problems
Feb. 11 David Autoencoders & Information Bottleneck
Feb. 27 Adrian Radillo Perfecting the research process dropbox doc from the teaching
Mar. 6 Ari Biologically plausible backprop
Mar. 13 Greg Corder (http://www.corderlab.com/) emotional processing of pain in the amygdala
Mar. 20 Ilenna Topics in the Philosophy of Science
Mar. 27 Tony Code Workflow for Research
May 1 Edgar Dobriban Data augmentation
May 15 Ben Baker (Miracchi lab) Representation and information in neuroscience
May 29 Sebastien Tremblay (Platt Lab) The limits of neurophys and why we need your help
June 5 Zhihao (Princeton University) TBA
October 9 David Rolnick Climate change
October 16 Ari Benjamin TBD (plasticity & learning in the brain)
October 23 Ethan Blackwood Neural models of indirection and abstraction
October 30 Ben Lansdell Invariance and causality
November 6 Nidhi Seethapathi Inferring Dynamics from Data
November 13 Tony Liu Theory of Computation
November 20 Ilenna Jones Ion Channel Kinetics

Older:

  1. Generalization in neural networks (Ari)
  2. Synaptic learning rules (Ari)
  3. How to science (debugging strategies etc.) (Konrad)
  4. Reinforcement learning and causal inference (Ben)
  5. DAGs and causal inference (Ben)
  6. Neuron firing dynamics and bifurcations (Ilenna)
  7. Submodular functions (Roozbeh)
  8. Recommendation systems (Rachit)

Previous lab teaching