Extracting and visualizing hidden activations and computational graphs of PyTorch models with TorchLens
JM Taylor, N Kriegeskorte - Scientific Reports, 2023 - nature.com
Deep neural network models (DNNs) are essential to modern AI and provide powerful
models of information processing in biological neural networks. Researchers in both …
models of information processing in biological neural networks. Researchers in both …
The algonauts project 2023 challenge: How the human brain makes sense of natural scenes
The sciences of biological and artificial intelligence are ever more intertwined. Neural
computational principles inspire new intelligent machines, which are in turn used to advance …
computational principles inspire new intelligent machines, which are in turn used to advance …
The sensorium competition on predicting large-scale mouse primary visual cortex activity
The neural underpinning of the biological visual system is challenging to study
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
Functional connectivity via total correlation: Analytical results in visual areas
Recent studies invoke the superiority of the multivariate Total Correlation concept over the
conventional pairwise measures of functional connectivity in biological networks. Those …
conventional pairwise measures of functional connectivity in biological networks. Those …
Exploring the brain-like properties of deep neural networks: A neural encoding perspective
Nowadays, deep neural networks (DNNs) have been equipped with powerful representation
capabilities. The deep convolutional neural networks (CNNs) that draw inspiration from the …
capabilities. The deep convolutional neural networks (CNNs) that draw inspiration from the …
Deep neural networks and brain alignment: Brain encoding and decoding (survey)
How does the brain represent different modes of information? Can we design a system that
automatically understands what the user is thinking? Such questions can be answered by …
automatically understands what the user is thinking? Such questions can be answered by …
System identification of neural systems: If we got it right, would we know?
Artificial neural networks are being proposed as models of parts of the brain. The networks
are compared to recordings of biological neurons, and good performance in reproducing …
are compared to recordings of biological neurons, and good performance in reproducing …
Statistical inference on representational geometries
Neuroscience has recently made much progress, expanding the complexity of both neural
activity measurements and brain-computational models. However, we lack robust methods …
activity measurements and brain-computational models. However, we lack robust methods …
[HTML][HTML] The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
P Turishcheva, PG Fahey, L Hansel, R Froebe… - ArXiv, 2023 - ncbi.nlm.nih.gov
Understanding how biological visual systems process information is challenging due to the
complex nonlinear relationship between neuronal responses and high-dimensional visual …
complex nonlinear relationship between neuronal responses and high-dimensional visual …
Functional connectivity in visual areas from total correlation
A recent study invoked the superiority of the Total Correlation concept over the conventional
pairwise measures of functional connectivity in neuroscience. That seminal work was …
pairwise measures of functional connectivity in neuroscience. That seminal work was …