Search Results for author:
Found 19 papers, 7 papers with code
A large language model-assisted education tool to provide feedback on open-ended responses
Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material.
Comparing dendritic trees with actual trees
Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists.
Neural Networks as Paths through the Space of Representations
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 understand.
Clustering units in neural networks: upstream vs downstream information
However, defining and quantifying modularity remains an open problem.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Object Based Attention Through Internal Gating
Object-based attention is a key component of the visual system, relevant for perception, learning, and memory.
Learning to infer in recurrent biological networks
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.
Spike-based causal inference for weight alignment
Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.
Reverse-Engineering Deep ReLU Networks
It has been widely assumed that a neural network cannot be recovered from its outputs, as the network depends on its parameters in a highly nonlinear way.
Identifying Weights and Architectures of Unknown ReLU Networks
The output of a neural network depends on its parameters in a highly nonlinear way, and it is widely assumed that a network's parameters cannot be identified from its outputs.
Movement science needs different pose tracking algorithms
Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos.
What does it mean to understand a neural network?
In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing.
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
Self-Expressive Decompositions for Matrix Approximation and Clustering
Classical approaches discover such structure by learning a basis that can efficiently express the collection.
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization
Current high-resolution imaging techniques require an intact sample that preserves spatial relationships.
Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics
We propose a neural connectomics strategy called Fluorescent In-Situ Sequencing of Barcoded Individual Neuronal Connections (FISSEQ-BOINC), leveraging fluorescent in situ nucleic acid sequencing in fixed tissue (FISSEQ).
Neurons and Cognition
Physical Principles for Scalable Neural Recording
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience.
Neurons and Cognition Biological Physics
