Related work and literature

neurogenesis-sim sits between four lines of work: models of neural development, developmental (indirect) encodings in neuroevolution, spiking network simulators, and connectome-constrained modelling. This page lists the work the toolbox builds on or should be compared with.

Note on references

This list was compiled with the help of an AI assistant. References with a DOI were checked against Crossref, DataCite or Europe PMC on 2026-11-02; the others come from well-known publications. Check each reference against the publisher’s record before citing it.

How the toolbox relates to existing tools

Tool	Focus	Difference from neurogenesis-sim
Cx3D / BioDynaMo	physics of growing cells and neurites, agent-based	detailed mechanics; no evolution of genomes for function, no spiking kernel tied to development
NETMORPH, TREES toolbox	statistical or optimal-wiring growth of neuronal morphologies	morphology-centred; networks are not grown from evolvable genomes for a task
NeuroDevSim	phenomenological growth, migration and pruning of neurons in a tissue (Python, parallel)	the closest Python peer for growth stages; no evolvable genome, no integrated spiking kernel
DevNCA	neural cellular automata co-evolving developmental patterns and plasticity rules for spiking networks	the closest conceptual neighbour (development + spiking + evolution); no biological geometry, cell types or data interchange
NEST, Brian 2, NEURON, GeNN, Arbor	simulating spiking networks	networks are specified, not developed; neurogenesis-sim exports to them (SONATA now; PyNN and NeuroML 2 planned, roadmap phase J)
SONATA, NeuroML 2, PyNN, NIR	describing networks independently of a simulator	interchange formats, not development; targets of the exporters
Connectome-Manipulator	rewiring, fitting and transplanting connectomes of SONATA circuits	operates on given circuits; can serve as a baseline for, and manipulate, grown circuits written as SONATA
NEAT, HyperNEAT, ES-HyperNEAT, NDPs	evolving networks through (indirect) encodings	abstract encodings; no biological geometry, cell types, stages or spiking biophysics
Connectome-constrained models	simulating measured wiring diagrams	wiring taken as given; neurogenesis-sim asks how development could produce its statistics

Models of neural development

• Turing AM (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society B 237:37–72.

• Wolpert L (1969). Positional information and the spatial pattern of cellular differentiation. Journal of Theoretical Biology 25:1–47.

• Gierer A, Meinhardt H (1972). A theory of biological pattern formation. Kybernetik 12:30–39.

• Sperry RW (1963). Chemoaffinity in the orderly growth of nerve fiber patterns and connections. PNAS 50:703–710.

• Willshaw DJ, von der Malsburg C (1976). How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society B 194:431–445.

• Swindale NV (1996). The development of topography in the visual cortex: a review of models. Network 7:161–247.

• Collier JR, Monk NAM, Maini PK, Lewis JH (1996). Pattern formation by lateral inhibition with feedback: a mathematical model of Delta–Notch intercellular signalling. Journal of Theoretical Biology 183:429–446.

• McLaughlin T, O’Leary DDM (2005). Molecular gradients and development of retinotopic maps. Annual Review of Neuroscience 28:327–355.

• Simpson HD, Mortimer D, Goodhill GJ (2009). Theoretical models of neural circuit development. Current Topics in Developmental Biology 87:1–51.

• van Ooyen A (2011). Using theoretical models to analyse neural development. Nature Reviews Neuroscience 12:311–326.

• Goodhill GJ (2016). Can molecular gradients wire the brain? Trends in Neurosciences 39:202–211.

Developmental and growth simulators

• Zubler F, Douglas R (2009). A framework for modeling the growth and development of neurons and networks. Frontiers in Computational Neuroscience 3:25. (Cx3D)

• Koene RA, Tijms B, van Hees P, et al. (2009). NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics 7:195–210.

• Cuntz H, Forstner F, Borst A, Häusser M (2010). One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Computational Biology 6:e1000877. (TREES toolbox)

• Breitwieser L, Hesam A, de Montigny J, et al. (2022). BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics 38:453–460.

• De Schutter E (2023). Efficient simulation of neural development using shared memory parallelization. Frontiers in Neuroinformatics 17:1212384. doi:10.3389/fninf.2023.1212384 (NeuroDevSim)

Activity-dependent development

• Wong ROL (1999). Retinal waves and visual system development. Annual Review of Neuroscience 22:29–47.

• Ben-Ari Y (2002). Excitatory actions of GABA during development: the nature of the nurture. Nature Reviews Neuroscience 3:728–739.

• Khazipov R, Sirota A, Leinekugel X, et al. (2004). Early motor activity drives spindle bursts in the developing somatosensory cortex. Nature 432:758–761.

• Bi GQ, Poo MM (1998). Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18:10464–10472.

• Frémaux N, Gerstner W (2016). Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Frontiers in Neural Circuits 9:85.

Developmental encodings and neuroevolution

• Kitano H (1990). Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4:461–476.

• Gruau F (1994). Neural network synthesis using cellular encoding and the genetic algorithm. PhD thesis, École Normale Supérieure de Lyon.

• Whitley D, Rana S, Heckendorn RB (1999). The island model genetic algorithm: on separability, population size and convergence. Journal of Computing and Information Technology 7:33–47.

• Stanley KO, Miikkulainen R (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation 10:99–127.

• Stanley KO (2007). Compositional pattern producing networks: a novel abstraction of development. Genetic Programming and Evolvable Machines 8:131–162.

• Stanley KO, D’Ambrosio DB, Gauci J (2009). A hypercube-based encoding for evolving large-scale neural networks. Artificial Life 15:185–212.

• Risi S, Stanley KO (2012). An enhanced hypercube-based encoding for evolving the placement, density, and connectivity of neurons. Artificial Life 18:331–363.

• Zador AM (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications 10:3770.

• Najarro E, Sudhakaran S, Risi S (2023). Towards self-assembling artificial neural networks through neural developmental programs. Proceedings of the Artificial Life Conference (ALIFE 2023). doi:10.1162/isal_a_00697

• Gaskin B, et al. (2025). DevNCA: co-evolving developmental patterns and plasticity rules for self-organising spiking neural networks. Proceedings of the Artificial Life Conference (ALIFE 2025). doi:10.1162/ISAL.a.840 (full author list not verified)

• Shuvaev S, Lachi D, Koulakov A, Zador A (2024). Encoding innate ability through a genomic bottleneck. PNAS 121:e2409160121.

Neuron models and spiking simulators

• Lapicque L (1907). Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarisation. Journal de Physiologie et de Pathologie Générale 9:620–635.

• Hodgkin AL, Huxley AF (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117:500–544.

• Izhikevich EM (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks 14:1569–1572.

• Gerstner W, Kistler WM, Naud R, Paninski L (2014). Neuronal Dynamics. Cambridge University Press.

• Hines ML, Carnevale NT (1997). The NEURON simulation environment. Neural Computation 9:1179–1209.

• Gewaltig MO, Diesmann M (2007). NEST (NEural Simulation Tool). Scholarpedia 2:1430.

• Davison AP, Brüderle D, Eppler J, et al. (2009). PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics 2:11. doi:10.3389/neuro.11.011.2008 (online 2008)

• Gleeson P, Crook S, Cannon RC, et al. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology 6:e1000815.

• Yavuz E, Turner J, Nowotny T (2016). GeNN: a code generation framework for accelerated brain simulations. Scientific Reports 6:18854.

• Stimberg M, Brette R, Goodman DFM (2019). Brian 2, an intuitive and efficient neural simulator. eLife 8:e47314.

• Dai K, Hernando J, Billeh YN, et al. (2020). The SONATA data format for efficient description of large-scale network models. PLoS Computational Biology 16:e1007696.

• Eshraghian JK, Ward M, Neftci EO, et al. (2023). Training spiking neural networks using lessons from deep learning. Proceedings of the IEEE 111:1016–1054.

Connectomes and connectome-constrained models

• Peters A, Feldman ML (1976). The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. I. General description. Journal of Neurocytology 5:63–84. (origin of “Peters’ rule”)

• Hill SL, Wang Y, Riachi I, Schürmann F, Markram H (2012). Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. PNAS 109:E2885–E2894.

• Markram H, Muller E, Ramaswamy S, et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell 163:456–492.

• Billeh YN, Cai B, Gratiy SL, et al. (2020). Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron 106:388–403.

• Scheffer LK, Xu CS, Januszewski M, et al. (2020). A connectome and analysis of the adult Drosophila central brain. eLife 9:e57443.

• Dorkenwald S, Matsliah A, Sterling AR, et al. (2024). Neuronal wiring diagram of an adult brain. Nature 634:124–138.

• Schlegel P, Yin Y, Bates AS, et al. (2024). Whole-brain annotation and multi-connectome cell typing of Drosophila. Nature 634:139–152.

• Lappalainen JK, Tschopp FD, Prakhya S, et al. (2024). Connectome-constrained networks predict neural activity across the fly visual system. Nature 634:1132–1140.

• Shiu PK, Sterne GR, Spiller N, et al. (2024). A Drosophila computational brain model reveals sensorimotor processing. Nature 634:210–219.

• Berg S, Beckett IR, Costa M, Schlegel P, Januszewski M, Marin EC, et al. (2026). Sexual dimorphism in the complete Drosophila male central nervous system connectome. Cell 189:5504–5526.e15. doi:10.1016/j.cell.2026.08.015 (FlyEM male CNS v1.0; preprint bioRxiv 10.1101/2025.10.09.680999). Data under CC-BY 4.0.

• Matsliah A, Yu S-c, Kruk K, et al. (2024). Neuronal parts list and wiring diagram for a visual system. Nature 634:166–180. doi:10.1038/s41586-024-07981-1

• Nern A, Loesche F, Takemura S-y, et al. (2025). Connectome-driven neural inventory of a complete visual system. Nature 641:1225–1237. doi:10.1038/s41586-025-08746-0

• The MICrONS Consortium, Bae JA, Baptiste M, et al. (2025). Functional connectomics spanning multiple areas of mouse visual cortex. Nature 640:435–447. doi:10.1038/s41586-025-08790-w

• Reimann MW, Gevaert M, Shi Y, Lu H, Markram H, Muller E (2019). A null model of the mouse whole-neocortex micro-connectome. Nature Communications 10:3903. doi:10.1038/s41467-019-11630-x (statistical connectivity rules instantiated into a SONATA circuit)

• Pokorny C, Awile O, Isbister JB, Kurban K, Wolf M, Reimann MW (2025). A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations. Network Neuroscience 9:207–236. doi:10.1162/netn_a_00429 (Connectome-Manipulator)

Interchange formats, data standards and tools

Formats and libraries that neurogenesis-sim reads or writes, or plans to (roadmap phase J).

• Dai K, Hernando J, Billeh YN, Gratiy SL, Planas J, Davison AP, et al. (2020). The SONATA data format for efficient description of large-scale network models. PLoS Computational Biology 16:e1007696. doi:10.1371/journal.pcbi.1007696

• Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, et al. (2020). Brain Modeling ToolKit: an open source software suite for multiscale modeling of brain circuits. PLoS Computational Biology 16:e1008386. doi:10.1371/journal.pcbi.1008386 (BMTK)

• Pedersen JE, Abreu S, Jobst M, Lenz G, Fra V, Bauer FC, et al. (2024). Neuromorphic intermediate representation: a unified instruction set for interoperable brain-inspired computing. Nature Communications 15:8122. doi:10.1038/s41467-024-52259-9 (NIR)

• Cannon RC, Gleeson P, Crook S, Ganapathy G, Marin B, Piasini E, et al. (2014). LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2. Frontiers in Neuroinformatics 8:79. doi:10.3389/fninf.2014.00079

• Vella M, Cannon RC, Crook S, Davison AP, Ganapathy G, Robinson HPC, et al. (2014). libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience. Frontiers in Neuroinformatics 8:38. doi:10.3389/fninf.2014.00038

• Sinha A, Gleeson P, Marin B, Dura-Bernal S, Panagiotou S, Crook S, et al. (2025). The NeuroML ecosystem for standardized multi-scale modeling in neuroscience. eLife 13:RP95135. doi:10.7554/eLife.95135

• Gleeson P, Cantarelli M, Marin B, Quintana A, Earnshaw M, Sadeh S, et al. (2019). Open Source Brain: a collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron 103:395–411.e5. doi:10.1016/j.neuron.2019.05.019

• Abi Akar N, Cumming B, Karakasis V, et al. (2019). Arbor — a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures. 27th Euromicro PDP, 274–282. doi:10.1109/EMPDP.2019.8671560

• Rhodes O, Bogdan PA, Brenninkmeijer C, et al. (2018). sPyNNaker: a software package for running PyNN simulations on SpiNNaker. Frontiers in Neuroscience 12:816. doi:10.3389/fnins.2018.00816

• Pehle C-G, Pedersen JE (2021). Norse — a deep learning library for spiking neural networks. Zenodo. doi:10.5281/zenodo.4422025

• Cannon RC, Turner DA, Pyapali GK, Wheal HV (1998). An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods 84:49–54. doi:10.1016/S0165-0270(98)00091-0 (origin of the SWC format)

• Thompson CL, Ng L, Menon V, Martinez S, Lee C-K, Glattfelder K, et al. (2014). A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain. Neuron 83:309–323. doi:10.1016/j.neuron.2014.05.033 (Allen Developing Mouse Brain Atlas; research and non-commercial use)

• Rübel O, Tritt A, Ly R, Dichter BK, Ghosh S, Niu L, et al. (2022). The Neurodata Without Borders ecosystem for neurophysiological data science. eLife 11:e78362. doi:10.7554/eLife.78362

• Garcia S, Guarino D, Jaillet F, Jennings T, Pröpper R, Rautenberg PL, et al. (2014). Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8:10. doi:10.3389/fninf.2014.00010

• Smith AM, Niemeyer KE, Katz DS, Barba LA, Githinji G, Gymrek M, et al. (2018). Journal of Open Source Software (JOSS): design and first-year review. PeerJ Computer Science 4:e147. doi:10.7717/peerj-cs.147

Licences: these tools keep their own licences (several are GPL, LGPL or CeCILL, and the Allen data are non-commercial). neurogenesis-sim uses them only as optional dependencies or as test references, never copies their code, and never redistributes their data; see Third-party software and prior work.

PLACEHOLDER

A comparison table with measured numbers (time to grow and simulate a reference circuit) against Cx3D / BioDynaMo, Brian 2 and NEST; a BibTeX file of all references; and short summaries of how each listed model maps onto toolbox stages and Params.