Review 39: Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations by Timothy Doyeon Kim and Carlos D. Brody
Bio
I am a machine learning researcher at Lawrence Livermore National Lab. In 2020, I received my BA in Cognitive Science and Data Science from University of California, Berkeley and was a research assistant at Dr. Kevin Bender’s lab (UCSF).
Research Interests
I am interested in computational neuroscience, particularly as it relates to understand the circuit and network level dynamics that allow for the efficent coding of internal representations. I am fascinated by how such a noisy, distributed, and error prone network can a) self organize and b) learn how to make representations of visual scences and abstract concepts (two very different tasks) and c) quickly generalize between tasks. These kinds of questions, fused with my interest in problem solving have led me down the paths of active learning, bayesian stats, and software engineering, but never away from my main focus: Neuroscience.
Recent posts
Review 38: Latent Factor Analysis via Dynamical Systems (LFADS)
LFADS - Latent Factor Analysis via Dynamical Systems by David Sussillo and Chethan Pandarinath
Review 37: Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling
Review 36: Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks by David Sussillo and Omri Barak
Review 35: Cortical activity during motor execution, motor imagery, and imagery-based online feedback
Cortical activity during motor execution, motor imagery, and imagery-based online feedback by Kai Miller and Rajesh P.N. Rao
Review 34: The Role of Population Structure in Computations Through Neural Dynamics
The Role of Population Structure in Computations Through Neural Dynamics by Alexis Dubreuil Adrian Valente and Srdjan Ostojic
Review 33: High-density Single-unit Human Cortical Recordings Using the Neuropixels Probe
High-density Single-unit Human Cortical Recordings Using the Neuropixels Probe by Jason Chung and Edward F. Chang
Review 32: Living Science: Maintaining the Joy of Discovery
Living Science: Maintaining the Joy of Discovery by Eve Marder
Review 31: Noninvasive Brain–Machine Interfaces for Robotic Devices
Noninvasive Brain–Machine Interfaces for Robotic Devices by Luca Toning and José del R. Millán
Review 30: Eavesdropping on the Brain With 10,000 Electrodes
Eavesdropping on the Brain With 10,000 Electrodes by Barun Dutta
Review 29: Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control
Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control by Vivek Athalye and Jose Carmena
Review 28: Four ethical priorities for neurotechnologies and AI
Four ethical priorities for neurotechnologies and AI by Rafael Yuste and Sara Goering
Review 27: Parsing learning in networks using brain–machine interfaces
Parsing learning in networks using brain-machine interfaces by Amy Orsborn and Bijan Pesaran
Review 26: A control-theoretic approach to brain-computer interface design
A control-theoretic approach to brain-computer interface design by Yin Zhang and Steven Chase
Review 25: Plug-and-play control of a brain-computer interface through neural map stabilization
Plug-and-play control of a brain-computer interface through neural map stabilization by Daniel Silversmith.
Review 24:The brain-reading devices helping paralysed people to move, talk and touch
The brain-reading devices helping paralysed people to move, talk and touch by Liam Drew.
Review 23: Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings
Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings by Nick Steinmetz.
Review 21: Cracking the Neural Code in Humans
Cracking the Neural Code in Humans by Emily Singer at the Simons Foundation.
Review 20: Trends in Computational Neuroscience: Cosyne 2022
Trends in Computational Neuroscience: Cosyne 2022 by Sabera Talukder
Review 19: Let us never speak of these values again.
Let us never speak of these values again. by Ben Recht
Review 18: Deep Learning Is Hitting a Wall
Deep Learning is Hitting a Wall by Gary Marcus
Review 17: Predictive Coding, Variational Autoencoders, and Biological Connections Pt 3
Predictive Coding, Variational Autoencoders, and Biological Connections by Joseph Marino
Review 16: Predictive Coding, Variational Autoencoders, and Biological Connections Pt 2
Predictive Coding, Variational Autoencoders, and Biological Connections by Joseph Marino
Review 15: Predictive Coding, Variational Autoencoders, and Biological Connections Pt 1
Predictive Coding, Variational Autoencoders, and Biological Connections by Joseph Marino
Review 14: Computation Through Neural Population Dynamics Part 2
Computation Through Neural Population Dynamics by Saurabh Vyas
Review 13: Computation Through Neural Population Dynamics Part 1
Computation Through Neural Population Dynamics by Saurabh Vyas
Review 12: Just Ask for Generalization
Just Ask for Generalization by Eric Jang
Review 11: The Bandwagon
The Bandwagon by Claude E. Shannon
Review 10: Beyond advertising: New infrastructures for publishing integrated research objects
Beyond advertising: New infrastructures for publishing integrated research objects by Elizabeth DuPre et al.
Review 9: The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning by Dr. Ha and Yujin Tang
Review 8: The Future of Artificial Intelligence is Self-Organizing and Self-Assembling
The Future of Artificial Intelligence is Self-Organizing and Self-Assembling by Prof. Sebastian Risi
Review 7: Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors
Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors by Dr. Austin Graves Dr. Richard Huganir.
Review 6: Spiking Neural Nets
Spiking Neural Networks by Anil Ananthaswamy.
Review 5
Making RL Tractable by Learning More Informative Reward Functions: Example-Based Control, Meta-Learning, and Normalized Maximum Likelihood.
Not Even Wrong
Wow, I just found out what it means to be “Not even wrong”. The phase, coined by Wolfgang Pauli has just given me a huge slice of humble pie. Basically not e...
Review 4
If we already understood the brain, would we even know it? by Tal Yarkoni.
Review 3
From synapse to network: models of information storage and retrieval in neural circuits by Johnatan (Yonatan) Aljadeff et al.
Review 2
Sparse balance: excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights by Ramin Khajeh, Francesco Fumarola, Lar...
Review 1
Review 1 : In Silico by Dr. Fairhall Citation: Fairhall, Adrienne L. “In silico: where next?.” Eneuro 8.2 (2021). Intro ...
Who’s In Charge
Well I am very busy so I’ll keep this short and sweet but I was listening to Alan Watts and ever since I’ve turned it off this one thought has stuck with me....