Search Results for author: Konrad P. Kording

Found 19 papers, 7 papers with code

A large language model-assisted education tool to provide feedback on open-ended responses

1 code implementation25 Jul 2023 Jordan K. Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange, Konrad P. Kording

Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material.

Language Modelling Large Language Model +2

Comparing dendritic trees with actual trees

no code implementations4 Jul 2023 Roozbeh Farhoodi, Phil Wilkes, Anirudh M. Natarajan, Samantha Ing-Esteves, Julie L. Lefebvre, Mathias Disney, Konrad P. Kording

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

no code implementations22 Jun 2022 Richard D. Lange, Devin Kwok, Jordan Matelsky, Xinyue Wang, David S. Rolnick, Konrad P. Kording

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.

Object Based Attention Through Internal Gating

1 code implementation8 Jun 2021 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.

Learning to infer in recurrent biological networks

1 code implementation18 Jun 2020 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.

Bayesian Inference Variational Inference

Spike-based causal inference for weight alignment

1 code implementation ICLR 2020 Jordan Guerguiev, Konrad P. Kording, Blake A. Richards

Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.

Causal Inference Econometrics

Reverse-Engineering Deep ReLU Networks

no code implementations ICML 2020 David Rolnick, Konrad P. Kording

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

no code implementations25 Sep 2019 David Rolnick, Konrad P. Kording

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

no code implementations24 Jul 2019 Nidhi Seethapathi, Shaofei Wang, Rachit Saluja, Gunnar Blohm, Konrad P. Kording

Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos.

Pose Tracking

What does it mean to understand a neural network?

no code implementations15 Jul 2019 Timothy P. Lillicrap, Konrad P. Kording

In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties.

The Roles of Supervised Machine Learning in Systems Neuroscience

no code implementations21 May 2018 Joshua I. Glaser, Ari S. Benjamin, Roozbeh Farhoodi, Konrad P. Kording

Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing.

BIG-bench Machine Learning

Machine learning for neural decoding

1 code implementation2 Aug 2017 Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich, Lee E. Miller, Konrad P. Kording

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.

BIG-bench Machine Learning Hippocampus

Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics

no code implementations21 Apr 2014 Adam H. Marblestone, Evan R Daugharthy, Reza Kalhor, Ian D Peikon, Justus M Kebschull, Seth L Shipman, Yuriy Mishchenko, Je Hyuk Lee, Konrad P. Kording, Edward S. Boyden, Anthony M Zador, George M. Church

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

no code implementations24 Jun 2013 Adam H. Marblestone, Bradley M. Zamft, Yael G. Maguire, Mikhail G. Shapiro, Thaddeus R. Cybulski, Joshua I. Glaser, Dario Amodei, P. Benjamin Stranges, Reza Kalhor, David A. Dalrymple, Dongjin Seo, Elad Alon, Michel M. Maharbiz, Jose M. Carmena, Jan M. Rabaey, Edward S. Boyden, George M. Church, Konrad P. Kording

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

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