24 citations · 54 across the 11 of their papers we have counts for
11 papers
Causality in the human niche: lessons for machine learning
Richard D. Lange, Konrad P. Kording
Humans interpret the world around them in terms of cause and effect and communicate their understanding of the world to each other in causal terms. These causal aspects of human co…
The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions
Gal Haspel, Ben Baker, Isabel Beets +34
Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand…
A large language model-assisted education tool to provide feedback on open-ended responses
Jordan K. Matelsky, Felipe Parodi, Tony Liu +2
Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such r…
Comparing dendritic trees with actual trees
Roozbeh Farhoodi, Phil Wilkes, Anirudh M. Natarajan +4
Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists.…
The neuroconnectionist research programme
Adrien Doerig, Rowan Sommers, Katja Seeliger +8
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been la…
Learning domain-specific causal discovery from time series
Xinyue Wang, Konrad Paul Kording
Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning. Techniques for CD encompass randomized experiments, which are generally u…
Neural Networks as Paths through the Space of Representations
Richard D. Lange, Devin Kwok, Jordan Matelsky +3
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to…
Clustering units in neural networks: upstream vs downstream information
Richard D. Lange, David S. Rolnick, Konrad P. Kording
It has been hypothesized that some form of "modular" structure in artificial neural networks should be useful for learning, compositionality, and generalization. However, defining…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
Object Based Attention Through Internal Gating
Jordan Lei, Ari S. Benjamin, Konrad P. Kording
Object-based attention is a key component of the visual system, relevant for perception, learning, and memory. Neurons tuned to features of attended objects tend to be more active…
Learning to infer in recurrent biological networks
Ari S. Benjamin, Konrad P. Kording
A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most…