Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution
Srinivas C. Turaga
!!!
previous work: A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System
One-to-one mapping between deep network units and real neurons uncovers a visual population code for social behavior
Jonathan W. Pillow, Mala Murthy
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention
Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim
NeurIPS 2021 Poster
A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models
Ida Momennejad
Mapping effective connectivity by virtually perturbing a surrogate brain
Effective Brain Connectome: the whole-brain effective connectivity from neural perturbational inference
Quanying Liu
Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation Andreas Horn
Elegans-AI: How the connectome of a living organism could model artificial neural networks
Francesco Bardozzo, Andrea Terlizzi, Claudio Simoncini, Pietro Lió, Roberto Tagliaferri
Deep connectomics networks: Results from neural network architectures inspired from network neuroscience
Nicholas Roberts, Vinay Uday Prabhu
ICML Deep Phenomena 2019
Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks
Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu
Real Neurons & Hidden Units @ NeurIPS 2019 Poster
C. Elegans and the mouse visual cortex
Biological connectomes as a representation for the architecture of artificial neural networks
Samuel Schmidgall, Catherine Schuman, Maryam Parsa
ICLR 2023 Conference Withdrawn Submission
A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species analysis toolbox
Elegans-AI: How the connectome of a living organism could model artificial neural networks
Learning dynamic representations of the functional connectome in neurobiological networks
eural signal propagation atlas of Caenorhabditis elegans
Inferring brain-wide interactions using data-constrained recurrent neural network models
Kanaka Rajan, Mount Sinai
RNN
CURBD allows the total activity of each region to be decomposed into a set of source currents from all other regions
Learning to Learn with Feedback and Local Plasticity
Real Neurons & Hidden Units @ NeurIPS 2019 Oral
Jack Lindsey, Columbia University
The Simplest Neural Model and a Hypothesis for Language
Daniel Mitropolsky, Columbia University
Prediction of neural activity in connectome-constrained recurrent networks Manuel Beiran, Ashok Litwin-Kumar
Is the connectome insufficient to constrain the dynamics?
The structural connectome constrains fast brain dynamics
Exploring Randomly Wired Neural Networks for Image Recognition
Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He
When brain-inspired AI meets AGI
Review
Meta-Radiology
Catalyzing next-generation Artificial Intelligence through NeuroAI