Connectome Inspired Neural Network

Drosophila

• 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

• The connectome predicts resting-state functional connectivity across the Drosophila brain

Human

• 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

• BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

• sinergia connectomics summer school

• Connectome-Based Attractor Dynamics Underlie Brain Activity in Rest, Task, and Disease

Perturbation

• 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

• Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition
  TMS EEG

C. Elegans

• 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  ICLR 2023 Conference Withdrawn Submission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

• Learning dynamic representations of the functional connectome in neurobiological networks

• Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
  Srinivas C Turaga

Dataset

• Neural signal propagation atlas of Caenorhabditis elegans

Rodent

• 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

Theory-Based

• 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?

• Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks

• Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

• Geometric Scaling Law in Real Neuronal Networks
  Gang Yan

cognitive inspired (collected by Ruizhe Zhou)

• Bridging the data gap between children and large language models
  Frank, M. C. (2023)

• Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner
  Dupoux, E. (2018)

• Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
  Warstadt, A. et al. (2023)

• MEWL: Few-shot multimodal word learning
  Jiang, G. et al. (2023)

• Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
  Zhuang, C. et al. (2024)

• Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
  Zhuang, C. et al. (2023)

• Context Limitations Make Neural Language Models More Human-Like
  Kuribayashi, T. et al. (2022)

• Does Vision Accelerate Hierarchical Generalization in Neural Language Learners?
  Kuribayashi, T. (2023)

• Emergent Word Order Universals from Cognitively-Motivated Language Models Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe, Timothy Baldwin

Others

• The structural connectome constrains fast brain dynamics

Basic Architecture

• Exploring Randomly Wired Neural Networks for Image Recognition
  Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He

Review

• When brain-inspired AI meets AGI
  Meta-Radiology

• Catalyzing next-generation Artificial Intelligence through NeuroAI

• Deciphering the Blueprint of the Fruit Fly’s Brain

Researcher (TODO)

• Andreas Horn