References and prior work

Prior work this builds on

Work	What it shows	Gap CAKE addresses
Randi et al. 2023 (Nature): C. elegans signal propagation atlas	Anatomy alone mispredicts causal signaling; extrasynaptic peptides matter	Mechanistic model of why (L4/L5, H3)
Lappalainen et al. 2024 (Nature): connectome-constrained fly visual system	Connectome + task optimization predicts responses of single neurons	Point neurons, task objective; no biophysics, slow regulation or body
Shiu et al. 2024 (Nature): whole fly brain LIF model	Connectome alone predicts some sensorimotor pathways	Uniform neuron parameters; no graded potentials, peptides or gap junctions
BAAIWorm / MetaWorm (Zhao et al. 2024, Nat Comput Sci)	Closed-loop multicompartment worm brain + body	Hand-set parameters; no principled θ inference or perturbation generalization
Data-driven biophysical network model of C. elegans premotor dynamics (arXiv 2501.00278, 2025)	Connectome + voltage clamp + whole-brain imaging fit a premotor circuit model reproducing forward/reversal switching	Small circuit, parameters fitted directly; a baseline and a source of priors for Track A
Modular integration of connectomics, dynamics and biomechanics in C. elegans (arXiv 2504.18073, 2025)	Modular brain–body pipeline for identifying sensorimotor pathways	Architecture reference for the component/coupler design
Beiran & Litwin-Kumar 2024	Connectome underdetermines dynamics; small recorded subsets fix it	Applied in CAKE to experiment design and identifiability
Jaxley (Deistler et al. 2025, Nat Methods)	Gradient training of detailed biophysical networks at 10⁵ parameters	Engine basis for L1–L3 inference
FlyGM (arXiv 2602.17997, 2026)	Whole-brain connectome graph as a body controller trained with RL	Not biophysical; weights learned, not anchored
Connectome-constrained spontaneous-activity model (bioRxiv, Aug 2026)	Whole-brain fly model fitted to calcium recordings of spontaneous activity	Benchmark target for E3
O'Leary, Marder et al. (2013–2014)	Homeostatic rules produce correlated conductances and cell-type identity	Scaled up to whole-connectome inference (H*)
State of Brain Emulation Report 2025	Field lacks evaluation standards and a "which details matter" answer	Emulation Ladder + Necessity Map

Bibliography

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Lappalainen, J. K. et al. (2024). Connectome-constrained networks predict neural activity across the fly visual system. Nature. https://www.biorxiv.org/content/10.1101/2023.03.11.532232
Shiu, P. K. et al. (2024). A Drosophila computational brain model reveals sensorimotor processing. Nature.
Dorkenwald, S. et al. (2024). Neuronal wiring diagram of an adult brain. Nature. https://flywire.ai/
FlyWire / Harvard (2025). The BANC: Brain and Nerve Cord. https://blog.flywire.ai/2025/11/03/the-banc-brain-and-nerve-cord/
Google Research / HHMI Janelia et al. (2026). Complete male fruit fly CNS connectome. https://research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain/
Zhao, M. et al. (2024). An integrative data-driven model simulating C. elegans brain, body and environment interactions. Nat. Comput. Sci. https://www.nature.com/articles/s43588-024-00738-w
Deistler, M. et al. (2025). Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics. Nat. Methods. https://jaxley.readthedocs.io/
Beiran, M., Litwin-Kumar, A. (2024). Prediction of neural activity in connectome-constrained recurrent networks. bioRxiv. https://www.biorxiv.org/content/10.1101/2024.02.22.581667
Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly (2026). https://arxiv.org/abs/2602.17997
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Persistent adaptation through dual-timescale regulation of ion channel properties (2026). PNAS. https://www.pnas.org/doi/abs/10.1073/pnas.2530340123
Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function (2026). PLoS Comput. Biol. https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1014177
A data-driven biophysical network model reproduces C. elegans premotor neural dynamics (2025). https://arxiv.org/abs/2501.00278
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Prediction, history and methodological parallels

Used with explicit limits in 11-prediction-and-history.md; none is evidence for H* or for a universal limit on neural prediction.

Lorenz, E. N. (1963). Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141. Original article.
ECMWF. Data assimilation, quantifying forecast uncertainty, and modelling and prediction. Institutional descriptions of state estimation, ensembles and continued improvements to observations and models.
Wilson, K. G. (1983). The renormalization group and critical phenomena. Rev. Mod. Phys. 55, 583–600. Article; based on the Nobel lecture of 8 December 1982.
Frank, S. A. (2012). Natural selection. IV. The Price equation. J. Evol. Biol. Article. Distinguishes an identity from a dynamically sufficient evolutionary model.
Gershman, S. J., Niv, Y. (2010). Learning latent structure: carving nature at its joints. Curr. Opin. Neurobiol. 20, 251–256. Authors' manuscript.
Sanders, H., Wilson, M. A., Gershman, S. J. (2020). Hippocampal remapping as hidden state inference. eLife 9:e51140. Article. Computational account of remapping, not proof of its neural implementation.
Intensional Kleene and Rice Theorems for Abstract Program Semantics (2021). Research paper. Used for the scope of undecidability results; these do not supply runtime lower bounds for CAKE's particular forecasts.
Kolmogorov, A. N. (1941). The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. English translation reprinted in Proc. R. Soc. Lond. A 434, 9–13 (1991). Translation.
Clay Mathematics Institute. Navier–Stokes equation problem. The existence and smoothness problem is distinct from the range of statistical and modeling problems in turbulence.
Gneiting, T., Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359–378. Authors' copy. Proper scores for probabilistic forecasts, including CRPS and the energy score.

Information budget

Used in 12-information-budget.md.

Cover, T. M., Thomas, J. A. (2006). Elements of Information Theory, 2nd ed. Wiley. (KL divergence, Pinsker's inequality, Stein's lemma, the data-processing inequality, Gaussian channel capacity, reverse water-filling.)
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Byrd, R. H., Chin, G. M., Nocedal, J., Wu, Y. (2012). Sample size selection in optimization methods for machine learning. Math. Program. 134, 127–155. (Dynamic sample sizes as the iterate converges.)
Tishby, N., Pereira, F. C., Bialek, W. (1999). The information bottleneck method. Proc. 37th Allerton Conference. https://arxiv.org/abs/physics/0004057
Still, S. (2014). Information bottleneck approach to predictive inference. Entropy 16, 968–989.
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Mardt, A., Pasquali, L., Wu, H., Noé, F. (2018). VAMPnets for deep learning of molecular kinetics. Nat. Commun. 9, 5.
Nair, G. N., Evans, R. J. (2004). Stabilizability of stochastic linear systems with finite feedback data rates. SIAM J. Control Optim. 43, 413–436.
Matveev, A. S., Pogromsky, A. Y. (2016). Observation of nonlinear systems via finite capacity channels: constructive data rate limits. Automatica 70, 217–229. (Restoration entropy.)
Ruelle, D. (1978). An inequality for the entropy of differentiable maps. Bol. Soc. Bras. Mat. 9, 83–87; Pesin, Ya. B. (1977). Characteristic Lyapunov exponents and smooth ergodic theory. Russ. Math. Surv. 32, 55–114. (Entropy and positive Lyapunov exponents.)
Shannon, C. E. (1949). Communication in the presence of noise. Proc. IRE 37, 10–21. (Sampling theorem; capacity of bandlimited Gaussian channels.)