paper reading list

Connectome

• FlyWire for College Classrooms

• Not everything, not everywhere, not all at once: a study of brain-wide encoding of movement

• Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

• Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling Approach bilinear + Mu Qiao

• How deep is the brain? The shallow brain hypothesis

• Generating brain-wide connectome using synthetic axonal morphologies Blue Brain Project

• The cell-type underpinnings of the human functional cortical connectome

• Evolution of connectivity architecture in the Drosophila mushroom body

• The connectome of the adult Drosophila mushroom body provides insights into function

• Fly connectome over the wire Review, October 2024

connectome dataset

• Neuronal wiring diagram of an adult brain flywire, female

• Whole-brain annotation and multi-connectome cell typing of Drosophila Nature 2024 FlyWire

• Neuronal parts list and wiring diagram for a visual system FlyWire

• The connectome of an insect brain larval brain

• Synaptic architecture of leg and wing premotor control networks in Drosophila John C. Tuthill motor

• Mapping of neuronal and glial primary cilia contactome and connectome in the human cerebral cortex human

• Connectome-driven neural inventory of a complete visual system

• bioRxiv 2024 Janelia, 2025, Nature, a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly’s visual neurons

• Waves of differentiation in the fly visual system Developmental Biology

MICrONS Project

• Collection: The MICrONS Project

• Functional connectomics spanning multiple areas of mouse visual cortex

• Functional connectomics reveals general wiring rule in mouse visual cortex

• Inhibitory specificity from a connectomic census of mouse visual cortex

• A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster

technology of connectome

• Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
  MIDL 2022

• Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set

• 3D reconstruction of cell nuclei in a full Drosophila brain

• Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster 2024 Machine learning identifies synaptic transmitters from electron micrographs

connection reconsturction

• Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)

• Light-microscopy-based connectomic reconstruction of mammalian brain tissue

connectome analysis

• Network statistics of the whole-brain connectome of Drosophila Mala Murthy, Nature

• Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila hemibrain and flywire

• A connectome and analysis of the adult Drosophila central brain hemibrain

• Brain rewiring during developmental transitions: A Comparative Analysis of Larva and Adult Drosophila melanogaster

• Connectomic analysis of the Drosophila lateral neuron clock cells reveals the synaptic basis of functional pacemaker classes

• Cross-species functional alignment reveals evolutionary hierarchy within the connectome

• A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

• Identifying Inputs to Visual Projection Neurons in Drosophila Lobula by Analyzing Connectomic Data

• Hue selectivity from recurrent circuitry in Drosophila

• Multilayer network analysis of C. elegans: Looking into the locomotory circuitry

• The Multilayer Connectome of Caenorhabditis elegans

• Exploring the Brain Characteristics of Structure-informed Functional Connectivity through Graph Attention Network

• The fly connectome reveals a path to the effectome
  Gregory S. X. E. Jefferis, Mala Murthy & Jonathan W. Pillow From connectome to effectome: learning the causal interaction map of the fly brain

• Hierarchical Modular Structure of the Drosophila Connectome We defined analogous measures for cell types. To identify cell types that may be partitioned into dense subnetworks, we introduce the Fraction of Type measure,

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connectome and gene

review

• The cell biology of synapse formation SAM
  Review, 2021, from Thomas C. Südhof
  synaptic adhesion molecules.png

• Towards an Understanding of Synapse Formation Review, 2018, from Thomas C. Südhof

• The Dynamic Synapse Review, 2013, Neuron, Daniel Choquet, Antoine Triller

• Making Connections in the Fly Visual System Review, 2002, Thomas R Clandinin, S.Lawrence Zipursky

• Synaptic Connectivity and Neuronal Morphology: Two Sides of the Same Coin

• Beyond Molecular Codes: Simple Rules to Wire Complex Brains Review, 2015, Bassem A. Hassan, P. Robin Hiesinger

• Chemoaffinity Revisited: Dscams, Protocadherins, and Neural Circuit Assembly Teview, 2010, S. Lawrence Zipursky, Joshua R. Sanes

• Architectures of neuronal circuits reivew, 2021, Science, Liqun Luo

computational methods

• bilinear: Uncovering the genetic blueprint of the C. elegans nervous system István A. Kovács, Dániel L. Barabási, and Albert-László Barabási PNAS

• Graph similarity learning for cross-level interactions “Incorporates direction attributes via a bilinear model for better adaptability to data.”

• biclique motifs A Genetic Model of the Connectome Dániel L. Barabási 1, Albert-László Barabási

• bilinear Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling Approach Mu Qiao

• Constructive connectomics: How neuronal axons get from here to there using gene- expression maps derived from their family trees

• Building a small brain with a simple stochastic generative model Oren Richter, Elad Schneidman

• The structure and function of neural connectomes are shaped by a small number of design principles Elad Schneidman

• Cell lineage predicts neural connectivity beyond cell type C. elegans
  Konrad P. Kording,

• A framework for modeling the growth and development of neurons and networks Frederic Zubler and Rodney Douglas(ETH Zurich), 2009

experimental test

• Rewiring an olfactory circuit by altering the combinatorial code of cell-surface proteins application Liqun Luo

• Brain wiring determinants uncovered by integrating connectomes and transcriptomes Yoo2023 T4_T5

• Molecular topography of an entire nervous system key_and_lock

• The Prop1-like homeobox gene unc-42 specifies the identity of synaptically connected neurons C. elegans

• Cell-type-Specific Patterned Stimulus-Independent Neuronal Activity in the Drosophila Visual System during Synapse Formation S. Lawrence Zipursky

• Genome-wide identification of neuronal activity-regulated genes in Drosophila

• Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability Anthony Zador
  connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring

• Dissecting origins of wiring specificity in dense cortical connectomes

• Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes

data:

• Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome

• Constrained roads to complex brains perspective, Neural development and brain circuit evolution converged in birds and mammals

• Evolutionary convergence of sensory circuits in the pallium of amniotes

• Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

• Developmental origins and evolution of pallial cell types and structures in birds

other methods to reconstruct connectome

• Building a small brain with a simple stochastic generative model Oren Richter, Elad Schneidman

• Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster Six transmitters predicted across the whole fly brain connectome: https://www.cell.com/cms/10.1016/j.cell.2024.03.016/asset/24723725-eb0b-40f7-b29d-10f859442e2b/main.assets/gr5_lrg.jpg Schematic of a neuron broken into its neuronal compartments. Inset, the proportion of presynapses in each of the four compartment types.

human connectome

• Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout 3GCs(global connectopies)

• A practical guide to linking brain-wide gene expression and neuroimaging data seven major steps to link expression measures and neuroimaging data

• Molecular logic of neocortical projection neuron specification, development and diversity, Neocortical projection neurons

experiment support

• A Conserved Role for Drosophila Neuroglian and Human L1-CAM in Central-Synapse Formation
  2006, Current Biology

• Spatial patterning controls neuron numbers in the Drosophila visual system

• Whole-cortex in situ sequencing reveals input-dependent area identity Zador lab

• Cell-type-Specific Patterned Stimulus-Independent Neuronal Activity in the Drosophila Visual System during Synapse Formation

• Pattern formation in the Drosophila eye
  2007, Review, 2007, Richard W Carthew, Northwestern University

• Novel brain wiring functions for classical morphogens: a role as graded positional cues in axon guidance
  morphogen gradients also serve to guide axonal pathfinding during development of the nervous system

• Ig Superfamily Ligand and Receptor Pairs Expressed in Synaptic Partners in Drosophila S. Lawrence Zipursky 2015, Cell

cell-cell communication and interaction

• CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

• Deciphering cell–cell interactions and communication from gene expression

• Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

• Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data

• Inference and analysis of cell-cell communication using CellChat

protein interaction

• PPI (Protein-Protein Interactions (PPI))

experiment support

• DIP/Dpr interactions and the evolutionary design of specificity in protein families binding specificity of DIP/Dpr subgroups is controlled by "negative constraints", which interfere with binding

• Nematode Extracellular Protein Interactome Expands Connections between Signaling Pathways

• An Extracellular Interactome of Immunoglobulin and LRR Proteins Reveals Receptor-Ligand Networks cell, 2013

• A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery PPIs

• Attentive cross-modal paratope prediction Antibodies

• Principles of branch dynamics governing shape characteristics of cerebellar Purkinje cell dendrites Growing dendrites are retracted or stalled by contacts with other dendrites.

• Neuropil Pattern Formation and Regulation of Cell Adhesion Molecules in Drosophila Optic Lobe Development Depend on Synaptobrevin 1999

deep learning methods

• paper with code: https://paperswithcode.com/dataset/ppi

• Powerful antibiotics discovered using AI

• Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

• Accurate protein function prediction via graph attention networks with predicted structure information

result verification

• A conserved fertilization complex bridges sperm and egg in vertebrates AlphaFold-Multimer This approach employs structural predictions of pairwise protein-protein interactions between each bait protein and the candidate proteins. A high interface-predicted template modeling score (ipTM score) reflects high confidence in the relative positioning of the interfaces.

data mapping between transcriptome and connectome

• Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells Cell2020, all kinds of neurons!!!

• Bicoid gradient formation and function in the Drosophila pre-syncytial blastoderm

• An anteroposterior Dorsal gradient in theDrosophila embryo

• Variation in the Dorsal Gradient Distribution Is a Source for Modified Scaling of Germ Layers in Drosophila

• Gradient-Based DNA Transcription Control in Animals

• Inferring causal connectivity from pairwise recordings and optogenetics Konrad P. Kording

• Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

neural type

my blog on connectivity_to_type

• Automatic discovery of cell types and microcircuitry from neural connectomics
  Eric Jonas, Konrad Kording
  It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses.

• Connectivity of single neurons classifies cell subtypes in mouse brains Neuronal Connectivity as a Determinant of Cell Types and Subtypes

• Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons
  SI Bayesian method

$$p(\mathbf{y} \mid Q, \mathbf{f}) \propto e^{-\frac{1}{2} \mathbf{f}^{\prime} \mathbf{M f}+\mathbf{y}^{\prime} Q \mathbf{f}}$$

• Molecularly defined and spatially resolved cell atlas of the whole mouse brain Xiaowei Zhuang

• Morphological diversity of single neurons in molecularly defined cell types 2021 Nature, Hongkui Zeng, Allen Institute

• Probabilistic cell typing enables fine mapping of closely related cell types in situ

• Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types 9,200 models are generated that capture multimodal single-cell datasets

neural activity and connectivity

• Homogenized C. elegans Neural Activity and Connectivity Data

• Homogenized C. elegans Neural Activity and Connectivity Data https://arxiv.org/abs/2411.12091 Konrad P Kording, Edward S Boyden

morphology

• Mapping Function Onto Neuronal Morphology Klaus M. Stiefel, Terrence J. Sejnowski
  We used a Lindenmayer-system (L-system) (Lindenmayer 1968) for the algorithmic construction of model neuron morphologies. Simulations of electrophysiological dynamics were carried out in the neuronal simulation language NEURON (version 5.7) (Hines and Carnevale 1997, 2000).

• Neuronal contact predicts connectivity in the C. elegans brain
  Peters’ rule explains synaptic specificity throughout the C. elegans nervous system

• Development of Dendritic Form and Function
  Review paper, 2015, Julie L. Lefebvre, Joshua R. Sanes, and Jeremy N. Kay

• One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application
  2010

• Molecular mechanisms of dendrite morphogenesis Review, 2012, Jyothi Arikkath

• Automated analysis of neuronal morphology, synapse number and synaptic recruitment

• L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies

• TreeMoCo: Contrastive Neuron Morphology Representation Learning

• Principles of branch dynamics governing shape characteristics of cerebellar Purkinje cell dendrites Growing dendrites are retracted or stalled by contacts with other dendrites.

morphology data

NeuroMorpho.Org is a centrally curated inventorys of digitally reconstructed neurons and glia

maximum weighted independent set (MWIS)

• Exactly Solving the Maximum Weight Independent Set Problem on Large Real-World Graphs

• A note on greedy algorithms for the maximum weighted independent set problem

Graph compression or sparsification

• Vertex Sparsification for Edge Connectivity

Maximum Dispersion

Maximum Diversity Problem (MDP)

• Dispersion on Trees

• Estimating Pairwise Distances in Large Graphs

• Approximation Algorithms for Dispersion Problems

• Approximation Algorithms for Maximum Dispersion

• Obnoxious Facility Location on Graphs

• 变邻域搜索(VNS)算法求解Max-Mean Dispersion Problem

• On memetic search for the max-mean dispersion problem

• Tabu search for the Max–Mean Dispersion Problem

• Upper bounds and exact algorithms for p-dispersion problems http://yalma.fime.uanl.mx/~roger/work/teaching/class_tso/docs_project/problems/PDP/cor-2006-Pisinger.pdf

• A review on discrete diversity and dispersion maximization from an OR perspective

• Heuristic and special case algorithms for dispersion problems

• A network flow solution to some nonlinear 0-1 programming problems, with applications to graph theory

connectome visualization

• FlyWire visualization tools

• CAVE: Connectome Annotation Versioning Engine https://vcg.seas.harvard.edu/publications/cave

• NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

• SmartEM: machine-learning guided electron microscopy

• BOOK: Visualization in Connectomics

• A Unified attentional bottleneck in the human brain connection bottleneck

• Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome

• ViMO - Visual Analysis of Neuronal Connectivity Motifs

• Network Repository. An Interactive Scientific Network Data Repository. the first scientific network data repository with interactive visual analytics. new GraphVis: interactive visual graph mining and machine learning

human connectome

• NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

• Visualizing the PHATE of Neural Networks
  Table 1. A survey of neuroimaging connectomic software.

gene data

• Isoforms of Terminal Selector Mamo Control Axon Guidance during Adult Drosophila Memory Center Construction via Semaphorin-1a

• SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

• Benchmarking methods for mapping functional connectivity in the brain

• Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

• Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells.
  nature neuroscience review 2024

• Science Forum: The Human Cell Atlas

gene data for Drosophila

• A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain

• A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain Cell, 2018

• Transcriptional Programs of Circuit Assembly in the Drosophila Visual System S. Lawrence Zipursky

• Neuronal diversity and convergence in a visual system developmental atlas Claude Desplan NYU, 2020

• Morphological and functional convergence of visual projection neurons from diverse neurogenic origins in Drosophila Mehmet Neset Özel & Claude Desplan, January 2025,Nature Communications

• Decoding gene regulation in the fly brain Stein Aerts, Nature, 2022

• A complete temporal transcription factor series in the fly visual system Claude Desplan

• A High-Resolution Atlas of the Brain Predicts Lineage and Birth Order Underly Neuronal Identity Stephen F. Goodwin, 2025
  we generate the first high-resolution, single-cell transcriptomic atlas of the adult Drosophila melanogaster central brain by integrating multiple datasets, achieving an unprecedented tenfold coverage of every neuron in this complex tissue.

analysis

• Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

• Single-cell RNA sequencing technologies and applications: A brief overview

• Methods and applications for single-cell and spatial multi-omics Nature Reviews Genetics (2023)

• Gene2vec: distributed representation of genes based on co-expression

logical gene for connectome

• Discovering Symbolic Models from Deep Learning with Inductive Biases

• On scientific understanding with artificial intelligence

• End-to-end symbolic regression with transformers

• interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

• KAN: Kolmogorov-Arnold Networks

gene regulatory network

• Decoding gene regulation in the fly brain

• Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks Nature Machine Intelligence, 2022

• Genome-wide identification of neuronal activity-regulated genes in Drosophila Brandeis eLife, 2016

• Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning

• Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis
  gene coexpression in scCOP and bulkCOP

$$\begin{array}{|c|c|c|} \hline \text { Gene pairs } & \text { scCOPs } & \neg \text { scCOPs } \\ \hline \text { bulkCOPs } & 313 & 2993 \\ \hline \neg \text { bulkCOPs } & 3245 & 232603 \\ \hline \end{array}$$

• Multiomic foundation model predicts epigenetic regulation by zero-shot

• An atlas of gene regulatory elements in adult mouse cerebrum An anatomically comprehensive atlas of the adult human brain transcriptome https://www.nature.com/articles/nature11405

review paper

• Gene regulatory network inference in the era of single-cell multi-omicsJulio Saez-Rodriguez Nature Reviews Genetics, 2023

• Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data npj Systems Biology and Applications, review, 2023

• A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data Briefings in Bioinformatics, 2021

sota models

• SCENIC: single-cell regulatory network inference and clustering

• SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

• LINGER: Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

application

• Encoding innate ability through a genomic bottleneck

• The Genomic Code: The genome instantiates a generative model of the organism

• TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

• A Comprehensive Drosophila melanogaster Transcription Factor Interactome

gene normalization

• Scanpy – Single-Cell Analysis in Python

• scanpy Preprocessing and clustering

• scanpy highly_variable_genes

• seurat Normalize Data

• RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

• Integrating single-cell transcriptomic data across different conditions, technologies, and species

functional and structure conenctome

• Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease

• The cell-type underpinnings of the human functional cortical connectome

• Complex brain networks: graph theoretical analysis of structural and functional systems Ed Bullmore & Olaf Sporns (2009).
  Nature reviews neuroscience

• Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013). Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron, 80(4), 867-886.

• Biswal, B., F. Zerrin Yetkin, Victor M. Haughton, James S. Hyde (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine 34(4): 537-541.

• Markov NT, et al. (2014) A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb Cortex 24:17–36.

• Donahue, C.J., Sotiropoulos, S.N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T.E., Dyrby, T.B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D.C., Glasser, M.F. (2016). Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J. Neurosci. 36, 6758–6770. doi:10.1523/JNEUROSCI.0493-16.2016.

• Honey, C., Thivierge JP, Sporns O (2010). Can structure predict function in the human brain? Neuroimage 52(3): 766-776.

• Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., Hagmann, P., & Jirsa, V. K. (2014). Identification of optimal structural connectivity using functional connectivity and neural modeling. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34(23), 7910–7916.

• Dawson, DA, Cha K, Lewis LB, Mendola JD, Shmuel A (2013). "Evaluation and calibration of functional network modeling methods based on known anatomical connections." Neuroimage 67: 331-343.

• Predicting brain structural network using functional connectivity. Medical image analysis, 79, 102463. https://doi.org/10.1016/j.media.2022.102463.

• Predicting brain structural network using functional connectivityj.media
  multi-GCN based GAN, AD

• Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model

• Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout

• A practical guide to linking brain-wide gene expression and neuroimaging data

other connectome model:

• Modelling brain connectomes networks: Solv is a worthy competitor to hyperbolic geometry!

Reinforcement learning (RL)

• Learning Symbolic Rules for Interpretable Deep Reinforcement Learning

• Applying Logical Rules to Reinforcement Learning for Interpretable Knowledge Graph Reasoning

• A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs

• A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

• Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming

• Reinforcement Logic Rule Learning for Temporal Point Processes

graph

• JC: https://docs.google.com/spreadsheets/d/10R6poOIIqA4OCsiSGs5mK4Zkq9pm8aPxk1IQ3Zl0NPU

• datasests: Gephi sample datasets

• DGL 2.0: Streamlining Your GNN Data Pipeline from Bottleneck to Boost
  github

• The Graph Neural Network Model

• Variational Graph Auto-Encoders Bayesian Deep Learning Workshop (NIPS 2016) code

• Graph Attention Networks Yoshua Bengio

• Graph Attention Networks

• Attention-based Graph Neural Network for Semi-supervised Learning

• GraphFM: A Scalable Framework for Multi-Graph Pretraining

• A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

• Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective within-community connection prediction

Link prediction

https://paperswithcode.com/task/link-prediction

• Link Prediction via Matrix Factorization

• GraphSAGE: Inductive Representation Learning on Large Graphs GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data.

• Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks

• Community-guided link prediction in multiplex networks

review

• A Survey of Link Prediction in Complex Networks

• Link prediction in complex networks: A survey

Graph Embedding

• LINE: Large-scale Information Network Embedding

• GraphVite - graph embedding at high speed and large scale
  GraphVite github

review

• A Comprehensive Survey of Graph Embedding

• Network Structure Inference, A Survey: Motivations, Methods, and Applications

• A Comprehensive Survey on Graph Neural Networks Cited by 11155

• Graph neural networks: A review of methods and applications Cited by 6966

• Graph Learning: A Survey

• papers with code: Graph Models

• Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks we construct the Heterogeneous Graph Benchmark (HGB), consisting of 11 diverse datasets with three tasks.

| Dataset | Nodes | Node Types | Edges | Edge Types | Target |
|---------|-------|------------|-------|------------|--------|
| Amazon  | 10,099| 1          | 148,659| 2          | product-product |
| LastFM  | 20,612| 3          | 141,521| 3          | user-artist |
| PubMed  | 63,109| 4          | 244,986| 10         | disease-disease |

disease gene

• GANLDA: Graph attention network for lncRNA-disease associations prediction

• Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes

• Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning

graph and connectome

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis https://www.sciencedirect.com/science/article/abs/pii/S1361841521002784

Graph Neural Networks for Brain Graph Learning: A Survey https://arxiv.org/abs/2406.02594 2024

dynamics system & Ripple

connectome inspired neural network
html connectome inspired neural network

-A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances R. D. Traub,R. K. Wong,R. Miles, andH. Michelson, 1991

• Intrinsic and network rhythmogenesis in a reduced traub model for CA3 neurons Paul F. Pinsky & John Rinzel, 1994

• A two compartment model of a CA1 pyramidal neuron One compartment represents the soma and proximal dendrites, while the second represents the distal dendrites.

• Hodgkin-Huxley Model

• Multi-Compartmental Model of a Purkinje Cell

• An integrative data-driven model simulating C. elegans brain, body and environment interactions

Soma compartments and neurite compartments

Spiking neural network

• Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks

single neuron dynamics

• Learning by the Dendritic Prediction of Somatic Spiking
  Robert Urbanczik, Walter Senn, 2014, Neuron

$$\dot{U}=-g_{\mathrm{L}} U+g_{\mathrm{D}}\left(V_{\mathbf{w}}-U\right)+I_U^{\text {som }} \\ I_U^{\text {som }}(t)=g_{\mathrm{E}}(t)\left(E_{\mathrm{E}}-U\right)+g_{\mathrm{I}}(t)\left(E_{\mathrm{I}}-U\right) .$$

• Sparse bursts optimize information transmission in a multiplexed neural code
  Richard Naud, Henning Sprekeler

$$\begin{gathered} \frac{d}{d t} u_i^{(x)}=-\frac{u_i^{(x)}-E_L}{\tau_x}+\frac{g_x f\left(u_i^{(d)}\right)+c_x K\left(t-\hat{t}_i^{(s)}\right)+I_i^{(x)}+w_i^{(x)}}{C_x} \\ \frac{d}{d t} w_i^{(x)}=-w_i^{(x)} / \tau_w^{(x)}+a_w^{(x)}\left(u_i^{(x)}-E_L\right) / \tau_w^{(x)}+b_w^{(x)} S_i^{(x)}, \end{gathered}$$

• Impact of dendritic size and dendritic topology on burst firing in pyramidal cells E. van, A. J. Ronald and A. Ooyen,PLoS Comput. Biol., 2010
  Morphologically simplified cells, the set of 23 neurons

• Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input W. Rall, J. Neurophysiol., 1967

• Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties Etay Hay ,Sean Hill,Felix Schürmann,Henry Markram,Idan Segev

HH model

Hodgkin & Huxley 1952

LIF

• Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model

• Generalized leaky integrate-and-fire models classify multiple neuron types

• Linear leaky-integrate-and-fire neuron model based spiking neural networks and its mapping relationship to deep neural networks

GLIF model

• Generalized leaky integrate-and-fire models classify multiple neuron types

• An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons 2023

• The Fractional order Leaky Integrate-and-Fire Model: Fractional differentiation-spiking properties Neural Networks, 2017

synapse

• A role for NMDA-receptor channels in working memory Xiao-Jing Wang,1998
• Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input Wilfrid Rall, 1967
• The Contribution of AMPA and NMDA Receptors to Persistent Firing in the Dorsolateral Prefrontal Cortex in Working Memory J Neurosci, 2020 -Synaptic AMPA Receptor Plasticity and Behavior neuron, review, 2009

tool

• Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics jaxley_experiments

• Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Tool for neuron dynamics simulation (like brian2, spikingjelly)

• NEURON

• BrainPy

• neuron morphology is an open-source Python package for working with single-neuron morphological reconstruction data, such as those in the Allen Cell Types Database.

• A GPU-based computational framework that bridges neuron simulation and artificial intelligence

review

• Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction 4 levels models - review 2006

• Dynamics of compartmental model neurons

• What Is the Most Realistic Single-Compartment Model of Spike Initiation?

• 深度复盘：AI 与脑科学的错位

• 大脑精细模拟重塑 NeuroAI 范式

• A GPU-based computational framework that bridges neuron simulation and artificial intelligence

temporal inhibition
timing-dependent inhibition

other

• The frequency of nerve action potentials generated by applied currents R. B. Stein and Alan Lloyd Hodgkin

• Fractional Spiking Neuron: Fractional Leaky Integrate-and-Fire Circuit Described with Dendritic Fractal Model

• A Dendritic Compartment Model Neuron

olfactory system

• A computational model of conditioning inspired by Drosophila olfactory system

• Predictive olfactory learning in Drosophila

• A vector-based strategy for olfactory navigation in Drosophila L.F. Abbott, Vanessa Ruta

• Neural dynamics for working memory and evidence integration during olfactory navigation in Drosophila

• Generating parallel representations of position and identity in the olfactory system

• Olfactory Information Processing in Drosophila Gregory S.X.E. Jefferis, 2009

• Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe

• Information flow, cell types and stereotypy in a full olfactory connectome Gregory S X E Jefferis

• Subpopulations of Projection Neurons in the Olfactory Bulb REVIEW article,Front. Neural Circuits, 27 August 2020

• Teneurins instruct synaptic partner matching in an olfactory map Weizhe Hong, Timothy J. Mosca & Liqun Luo
  Teneurins instruct matching specificity between synaptic partners through homophilic attraction.

NeuroAI

• In vitro neurons learn and exhibit sentience when embodied in a simulated game-world

• Brain organoid reservoir computing for artificial intelligence

• The Forward-Forward Algorithm: Some Preliminary Investigations Geoffrey Hinton

• Constraining computational models using electron microscopy wiring diagrams Ashok Litwin-Kumar , Srinivas C Turaga
  Review, Current Opinion in Neurobiology, 2019

• NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila Pavan Ramdya

Connectome & ANN

connectome inspired neural network
html connectome inspired neural network

• Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks

• Forecasting Whole-Brain Neuronal Activity from Volumetric Video ZAPBench

• Foundation model of neural activity predicts response to new stimulus types

geometry

• Modelling brain connectomes networks: Solv is a worthy competitor to hyperbolic geometry! ICLR 2024 Conference Submission

Brain simulation / activity prediction

• AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity NeurIPS 2023 poster

model

• Learning to Simulate Complex Physics with Graph Networks
  为了做这个，我们需要什么样的data。有wide field and the 2p microscope，是不是就可以模拟一些基础规律了

Network analysis

BOOK Fundamentals of Brain Network Analysis

BOOK Chapter 3 - Connectivity Matrices and Brain Graphs

Review Neural Networks With Motivation

• Modeling small networks 1998, L F Abbott, Eve Marder

synapse plasticity

STDP

nearest neighbor STDP (Izhikevich, 2003)

• Relating STDP to BCM

• Tensor formalism for predicting synaptic connections with ensemble modeling or optimization
  Tirthabir Biswas, Tianzhi Lambus Li, James E. Fitzgerald

• Inhibitory and Excitatory Spike-Timing-Dependent Plasticity in the Auditory Cortex Inhibitory potentiation occurs when either pre- or postsynaptic spikes come first
  Excitatory and inhibitory inputs become bound together by postsynaptic spiking

neural data recording

whole brain imaging

drosophila data

• The spatial and temporal structure of neural activity across the fly brain swept confocally-aligned planar excitation (SCAPE) microscopy,
  1419 ± 78 stable, single-cell ROIs per animal, contained nearly all neurons within 70 μm of the dorsal surface
  https://springernature.figshare.com/articles/dataset/Flygenvectors_The_spatial_and_temporal_structure_of_neural_activity_across_the_fly_brain/23749074

• Whole-Brain Calcium Imaging Reveals an Intrinsic Functional Network in Drosophila Current Bilogy https://data.mendeley.com/datasets/8b6nw2xxhn/1

• Mapping the neural dynamics of locomotion across the Drosophila brain

other data

• In vivo autofluorescence lifetime imaging of the Drosophila brain captures metabolic shifts associated with memory formation

• The spatial and temporal structure of neural activity across the fly brain Evan S. Schaffer ... L. F. Abbott & Richard Axel

• Imaging whole-brain activity to understand behaviour Review Article

• Wide-field fluorescence lifetime imaging of neuron spiking and subthreshold activity in vivo

• Mapping the neural dynamics of locomotion across the Drosophila brain

• Correspondence of the brain's functional architecture during activation and rest

• Fast near-whole–brain imaging in adult Drosophila during responses to stimuli and behavior Plos Biology

• Imaging through the Whole Brain of Drosophila at λ/20 Super-resolution

• Coupling of activity, metabolism and behaviour across the Drosophila brain Surya Ganguli & Thomas R. Clandinin

cell tracking

• EMC2: A versatile algorithm for robust tracking of calcium dynamics from individual neurons in behaving animals

• 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images

wormid

• Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans Daniel Y. Sprague,..., Saul Kato

neural data analysis & modeling

• Spatiotemporal conditional inference and hypothesis tests for neural ensemble spiking precision

• Maximum entropy models as a tool for building precise neural controls Cristina Savin, Gašper Tkačik

• Unsupervised pretraining in biological neural networks Lin Zhong, Scott Baptista, Rachel Gattoni, Jon Arnold, Daniel Flickinger, Carsen Stringer & Marius Pachitariu Nature (2025)

reivew

• Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience ICCV, 2019

• Analysis methods for large-scale neuronal recordings Carsen Stringer, Marius Pachitariu

high dimensional data

• Spontaneous behaviors drive multidimensional, brainwide activity Carsen Stringer

• High-dimensional geometry of population responses in visual cortex Carsen Stringer

• Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

dimensional reduction

Phate: - Visualizing structure and transitions in high-dimensional biological data Phate

manifold reconstriction

• Approximation of Functions on Manifolds in High Dimension from Noisy Scattered Data Manifold Locally Optimal Projection (MLOP)

• Manifold Repairing, Reconstruction and Denoising from Scattered Data in High-Dimension Repairing Manifold Locally Optimal Projection (R-MLOP)

• Parameterization-free Projection for Geometry Reconstruction

activity analysis

• Neural signal propagation atlas of Caenorhabditis elegans Andrew M. Leifer

• Dynamical constraints on neural population activity Byron M. Yu & Aaron P. Batista

• Preserved neural dynamics across animals performing similar behaviour

• Dynamical constraints on neural population activity Byron M. Yu & Aaron P. Batista

head direction

• Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models Ning Chang, Hsuan-Pei Huang & Chung-Chuan Lo (2023)

• Estimating orientation in natural scenes: A spiking neural network model of the insect central complex

Rachael Stentiford ,James C. Knight,Thomas Nowotny,Andrew Philippides,Paul Graham 2024

• From Synapses to Dynamics: Obtaining Function from Structure in a Connectome Constrained Model of the Head Direction Circuit Sunny Duan, Ling Liang Dong, Ila Fiete, 2025

encoding and decoding

• Neural Decoding of Visual Imagery During Sleep

• Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1

• Bayesian decoding using unsorted spikes in the rat hippocampus Fabian Kloosterman, Stuart P. Layton, Zhe Chen, and Matthew A. Wilson

• Decoding the brain: From neural representations to mechanistic models

• Hebbian instruction of axonal connectivity by endogenous correlated spontaneous activity

deep learning method

• Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity

• LFADS:Inferring single-trial neural population dynamics using sequential auto-encoders LFADS (method part)

• MtM: Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution Yizi, Liam, Cole

• NEMO: In vivo cell-type and brain region classification via multimodal contrastive learning

• POYO-1: A Unified, Scalable Framework for Neural Population Decoding

• EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs

fundation model

?

• GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence NeurIPS 2023 poster

diffusion model/ visual Stimulus

• Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths

• Natural scene reconstruction from fMRI signals using generative latent diffusion Scientific Report, 2023

• Neural Decoding of Visual Imagery During Sleep Science, 2013

• Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding CVPR, 2023

behavior

• GLM-HMM: Unsupervised identification of the internal states that shape natural behavior

• Mice alternate between discrete strategies during perceptual decision-making Jonathan W. Pillow

• End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior Thomas L. Griffiths

• Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

• Arithmetic and local circuitry underlying dopamine prediction errors

• A Neural Substrate of Prediction and Reward

• Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior Zoe Ashwood, Aditi Jha, Jonathan W. Pillow
  NeurIPS 2022

• Spontaneous behaviour is structured by reinforcement without explicit reward Sandeep Robert Datta

• The spinal premotor network driving scratching flexor and extensor alternation

oscilation

• Brain rhythms have come of age

place cell and grid cell

• medial entorhinal cortex

• The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation

• Emergence of robust global modules from local interactions and smooth gradients

vision

• The limits of color awareness during active, real-world vision using VR to test the color fade perception

• A common evolutionary origin for the ON- and OFF-edge motion detection pathways of the Drosophila visual system Ian A. Meinertzhagen

• Neuronal circuits integrating visual motion information in Drosophila T4 and T5 neuron

• Social state alters vision using three circuit mechanisms in Drosophila Vanessa Ruta & Gerald M. Rubin

drosophila

• The Neural Substrate of Spectral Preference in Drosophila

• Neuronal circuits integrating visual motion information in Drosophila melanogaster

• Morphological and functional convergence of visual projection neurons from diverse neurogenic origins in Drosophila

others

ML

• Logic + Reinforcement Learning + Deep Learning: A Survey Review, 2023

• Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach 2024

• Applying Logical Rules to Reinforcement Learning for Interpretable Knowledge Graph Reasoning

• Neural Logic Reinforcement Learning

hippocamus

• Network Dynamics Underlying the Formation of Sparse, Informative Representations in the Hippocampus

• Sparse and distributed coding of episodic memory in neurons of the human hippocampus

• Synaptic properties of newly generated granule cells support sparse coding in the adult hippocampus

gene

• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution Transformer + gene

• Synaptic Pruning by Microglia Is Necessary for Normal Brain Development

cognition

attention

learning and memory

• How our understanding of memory replay evolves

decision making

• Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated by the Environment

• Dopamine pathways mediating affective state transitions after sleep loss

DS & OR

• Association rule learning

Quadratic assignment problem

• quadratic assignment problem

• Sliced Gromov-Wasserstein

• ot.gromov Fused Gromov-Wasserstein transport

similarity matrix

• Euclidean Distance Matrices: Essential Theory, Algorithms and Applications

• Representational similarity analysis – connecting the branches of systems neuroscience

• Euclidean Distance Matrices: Essential Theory, Algorithms and Applications

• CCA Kernel and nonlinear canonical correlation analysis

• Similarity of Neural Network Representations Revisited Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton

• Representational dissimilarity metric spaces for stochastic neural networks Lyndon R. Duong, Jingyang Zhou, Josue Nassar, Jules Berman, Jeroen Olieslagers, Alex H. Williams

ML

• Inverse Reinforcement Learning with the Average Reward Criterion

• Artificial Neural Networks for Neuroscientists: A Primer

• Attention Is All You Need Transformer

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting recurrent CNN

NAS

• NAS-Bench-101: Towards Reproducible Neural Architecture Search Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter

CNN kernel

Clustering convolutional kernels

• Framework for Automatic Selection of Kernels based on Convolutional Neural Networks and CkMeans Clustering Algorithm

• How convolutional neural network see the world - A survey of convolutional neural network visualization methods

• Visualizing and Understanding Convolutional Networks

sparse decoding

• Identifiable Deep Generative Models via Sparse Decoding

UFLDL Tutorial

• Sparse Coding

• Sensing Matrix Optimization for Block-Sparse Decoding

• A Tutorial on Decoding Techniques of Sparse Code Multiple Access

• On the iterative decoding of sparse quantum codes

others

• Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

• A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines SVM

• Vestibular System: The Many Facets of a Multimodal Sense

• Spatial modulation of hippocampal activity in freely moving macaques Dora E. Angelaki

• How Hubel and Wiesel Revolutionized Neuroscience and Made Me a Neuroscientist a story

• Sleep and circadian rhythmicity as entangled processes serving homeostasis

• Tuned geometries of hippocampal representations meet the computational demands of social memory

• Complex computation from developmental priors

• A practical guide to linking brain-wide gene expression and neuroimaging data gene

wiki

• wiki Non-negative least squares

• wiki Kronecker product

• wiki F-score

• wiki Precision_and_recall

TODO

• Synaptic gradients transform object location to action S. Lawrence Zipursky & Gwyneth M. Card

• Whole brain alignment of spatial transcriptomics between humans and mice with BrainAlign

• Cell2Sentence: Teaching Large Language Models the Language of Biology

• Reversed graph embedding resolves complex single-cell trajectories

• Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

• viewpoint Your Students are Your legacy