120 citations · 871 across the 41 of their papers we have counts for
46 papers
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador, Sean Escola, Blake Richards +24
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in…
Nothing makes sense in deep learning, except in the light of evolution
Artem Kaznatcheev, Konrad Paul Kording
Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its tra…
Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations
Max Dabagia, Konrad P Kording, Eva L Dyer
Many questions in neuroscience involve understanding of the responses of large populations of neurons. However, when dealing with large-scale neural activity, interpretation become…
Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules
Jeffrey Cheng, Ari Benjamin, Benjamin Lansdell +1
The brain performs unsupervised learning and (perhaps) simultaneous supervised learning. This raises the question as to whether a hybrid of supervised and unsupervised methods will…
A critical reappraisal of predicting suicidal ideation using fMRI
Timothy Verstynen, Konrad Kording
For many psychiatric disorders, neuroimaging offers a potential for revolutionizing diagnosis, and potentially treatment, by providing access to preverbal mental processes. In thei…
Do biological constraints impair dendritic computation?
Ilenna Simone Jones, Konrad Paul Kording
Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they…
A Philosophical Understanding of Representation for Neuroscience
Ben Baker, Benjamin Lansdell, Konrad Kording
Neuroscientists often describe neural activity as a representation of something, or claim to have found evidence for a neural representation. But what do these statements mean? The…
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Tara van Viegen, Athena Akrami, Kate Bonnen +32
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or fl…
Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees
Ilenna Simone Jones, Konrad Paul Kording
Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artifi…
PDE constraints on smooth hierarchical functions computed by neural networks
Khashayar Filom, Konrad Paul Kording, Roozbeh Farhoodi
Neural networks are versatile tools for computation, having the ability to approximate a broad range of functions. An important problem in the theory of deep neural networks is exp…
MoVi: A Large Multipurpose Motion and Video Dataset
Saeed Ghorbani, Kimia Mahdaviani, Anne Thaler +4
Human movements are both an area of intense study and the basis of many applications such as character animation. For many applications, it is crucial to identify movements from vi…
Appreciating the variety of goals in computational neuroscience
Konrad P. Kording, Gunnar Blohm, Paul Schrater +1
Within computational neuroscience, informal interactions with modelers often reveal wildly divergent goals. In this opinion piece, we explicitly address the diversity of goals that…
End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition
Shaofei Wang, Vishnu Lokhande, Maneesh Singh +2
Modern computer vision (CV) is often based on convolutional neural networks (CNNs) that excel at hierarchical feature extraction. The previous generation of CV approaches was often…
Spike-based causal inference for weight alignment
Jordan Guerguiev, Konrad P. Kording, Blake A. Richards
In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients. For the real brain…
Reverse-Engineering Deep ReLU Networks
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. Here, we prove that in fa…
Movement science needs different pose tracking algorithms
Nidhi Seethapathi, Shaofei Wang, Rachit Saluja +2
Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos. This promises to enable movement science to detect d…
What does it mean to understand a neural network?
Timothy P. Lillicrap, Konrad P. Kording
We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain…
Reverse engineering neural networks from many partial recordings
Elahe Arani, Sofia Triantafillou, Konrad P. Kording
Much of neuroscience aims at reverse engineering the brain, but we only record a small number of neurons at a time. We do not currently know if reverse engineering the brain requir…
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning
Titipat Achakulvisut, Chandra Bhagavatula, Daniel Acuna +1
Claims are a fundamental unit of scientific discourse. The exponential growth in the number of scientific publications makes automatic claim extraction an important problem for res…
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…
Learning to solve the credit assignment problem
Benjamin James Lansdell, Prashanth Ravi Prakash, Konrad Paul Kording
Backpropagation is driving today's artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neurosci…
Rarely-switching linear bandits: optimization of causal effects for the real world
Benjamin Lansdell, Sofia Triantafillou, Konrad Kording
Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so…
On functions computed on trees
Roozbeh Farhoodi, Khashayar Filom, Ilenna Simone Jones +1
Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree i…
The lure of misleading causal statements in functional connectivity research
David Marc Anton Mehler, Konrad Paul Kording
As neuroscientists we want to understand how causal interactions or mechanisms within the brain give rise to perception, cognition, and behavior. It is typical to estimate interact…