we-solve (beta)

BIO-3 · biology · Whole brain prediction

Predictive whole-brain modelling in a specified fly task panel

LLM status Partially solved unverified

Statement

Predictive whole-brain modelling in a specified fly task panel

Domain. Whole-brain prediction.   Retained status/source context. Open whole-animal transfer programme, with substantial restricted progress. Connectome-based fly simulations already predict selected sensorimotor responses; a universal whole-brain account has not been established by these sources.

Definitions and assumptions.
For an adult fruit fly, let $A$ contain reconstructed synapse counts and cell types, $\theta$ contain membrane, synaptic and neuromodulatory parameters, $u(t)$ sensory input, and $b(t)$ measured behaviour. A state-space model $\dot x=F_\theta(x,A,u)$ couples neural states to an explicit body and environment model $b=H_\theta(x,e)$. The anatomy comes from a particular specimen; individual differences and missing connections receive uncertainty distributions. Fix a finite ethological task and perturbation set.
Problem statement.
Construct a tractable model that predicts both activity and behaviour under withheld conditions: \[ \max_{a\in\mathcal A_{\rm test}}\left\{\mathbb{E}[d_r(\widehat r,r)\mid a]+\lambda\mathbb{E}[d_b(\widehat b,b)\mid a]\right\}\leq\varepsilon. \] Define metrics $d_r,d_b$ and scale factor $\lambda$ before testing. Report uncertainty and failure regimes; successful simulation of a selected response is not complete behavioural prediction.
Additional binding assumptions and resolution criterion.
Define the full coupled neural--body--environment state, initial-condition law and sensory feedback loop. A static map from neural state to behavior is insufficient for closed-loop locomotion unless body dynamics have been included in $x$. Metrics compare matched means, distributions or explicitly coupled trajectories; independent stochastic realizations cannot be expected to agree pointwise. The scope is the finite fly task panel, not every behavior.

Alternative formulations.
(i) Predict neural responses with recorded sensory input and body motion supplied, avoiding closed-loop embodiment. (ii) Predict autonomous behaviour in an environment from anatomy and sparse physiology, a much stronger target. Cross-species extension is a separate programme because cell types, circuit sizes and sensorimotor interfaces do not have an assumed one-to-one map.
Obstructions and partial progress.
The 2024 fly computational model provides selected behavioural validations, and a 2026 brain-and-cord connectome expands anatomical coverage. Connectome information does not supply all physiological parameters, and output neurons need an embodied mapping to action. The supplied Tang--Bassett review concerns control of network dynamics, not an implemented complete organism model. This task extends the anatomy-to-neural-state question in Problem 110.
Selective external check in this revision.
SC06 (13 October 2026; access level and exact support are recorded in the source-check appendix). These selected checks are not an exhaustive present-day status search.
Sources (supplied provenance; not all rechecked).

Binding catalogue conventions

This edition revises both supplied catalogues and preserves original problem numbers 1--330. It adds one globally unique label \label{rank:x} per problem, where smaller $x$ means greater provisional difficulty. A rank label is an anchor at that problem's section: \ref{rank:x} returns the section number, not the literal difficulty number. The visible rank:x text and the index give the difficulty number itself.

The entries are parameterized mathematical research schemes, including empirical prediction, causal identification, historical reconstruction, theory and engineering. They are not 330 certified unsolved mathematical conjectures. A rigorous instance fixes the objects and restrictions below; changing those objects changes the question. Established identities, finite calculations, non-identification constructions and solved special cases are not being posed anew as open. Some residual tasks concern informative bounds, biological realization or external validation rather than a new theorem.

Every main definition and problem statement received a mathematical editorial review. Each entry now has an additional binding-conditions paragraph; direct corrections are separately recorded in the accompanying audit. The alternative formulations inherit the applicable conventions, but are different targets unless equivalence is proved. Where an old broad phrase conflicts with an explicit restriction below it, the explicit restriction controls.

Literature boundary. Thirteen selective primary-literature checks cover eighteen entries in this revision; their access levels and scope are recorded explicitly. The supplied bibliography and its earlier source-access descriptions are retained as provenance, not represented as newly verified. Neither this review nor a source's description proves the absence of later solutions. The date of this edition is 13 October 2026; there is no claim of exhaustive current-status certification for all 330 entries.

Binding conventions for a concrete instance

Objects, quantifiers and non-vacuity.
An instance declares measurable state, input, output and parameter spaces; a fixed candidate family; admissible initial laws, interventions and environments; a finite horizon or specified asymptotic regime; observation and sampling laws; and performance metrics, positive normalization scales and positive tolerances. These are chosen before fitting or testing, not adjusted after seeing failures. Shared parameters must genuinely be shared across the declared validation domain. Finite dimension alone does not restrict arbitrary real-number encodings: specify a finite library, bounded description length, or regularity/complexity bounds. Exclude constant or no-signal solutions through a fixed informative-output or baseline-improvement requirement where appropriate. A universally quantified result cannot be established by choosing one trivial favorable instance.
Probability laws and data.
Every expectation or probability refers to the specified stochastic law, including initial uncertainty, process noise, observation noise and sampling as appropriate. A deterministic equation is not a probability model without a declared random input or initial law. "Observed" targets in population-level formulas mean population observables $g_j^*$, not noisy estimates asserted to be exact. Let $\widetilde g_j$ be independent validation estimates and suppose \[ \mathbb{P}\{\lvert\widetilde g_j-g_j^*\rvert\le r_j\text{ for every }j\}\ge1-\alpha. \] Then an uncertainty-aware sufficient certification rule for positive target tolerances $\varepsilon_j$ is \[ \max_j\frac{|\widehat g_j-\widetilde g_j|+r_j}{\varepsilon_j}\le1. \] On that simultaneous confidence event it implies every population error is at most its tolerance, by the triangle inequality. Vector/norm versions use corresponding valid simultaneous bounds. Training uncertainty must also be included when the claim averages over repeated training data. Evaluation on finitely many conditions does not establish a continuous-domain supremum without covering and regularity bounds.
Dynamics and well-posedness.
For ODEs and SDEs give coefficient domains, initial laws and existence/uniqueness conditions in the intended solution sense, or keep the entire admitted solution set. For stochastic jump models specify nonnegative off-diagonal rates, diagonal normalization, rate orientation and non-explosion. For PDEs specify spatial domains, boundary/initial conditions, conservation or source terms, and a weak or classical solution class. Moving-domain concentration equations include dilution/transport terms. A stationary law is used only for a time-homogeneous augmented model under existence assumptions; a stationary approximation to a driven system must be justified. Absorbing extinction states are retained. Survival-conditioned, quasi-stationary and unconditional laws are distinct and must not be substituted for one another.
Identification and causal meaning.
Two parameter values are observationally equivalent when they induce the same full distribution under all allowed observation/intervention designs, not merely the same likelihood value for one sample. A target is identified only if constant on the equivalence class. Gauge symmetries, label switching and reference conventions are quotiented or fixed. Finite-data compatible sets use a declared confidence, likelihood or discrepancy rule; an empty set signals model rejection. Bayesian credible sets are not automatically confidence sets. A prior or regularizer does not create data identification. Bounds intended to resolve a question must be informative at a predeclared width or decision threshold.

A causal contrast uses well-defined intervention versions and a stated causal model. Random assignment, or explicit consistency, overlap and exchangeability assumptions, is required for causal interpretation from observations. Interference and time-varying confounding are represented when present. Fixing a mediator by intervention differs from conditioning on its observed value. Marginal potential-outcome laws do not determine individual cross-world effects or an arbitrary joint coupling. Ethically admissible designs, rather than unconstrained interventions, define the experimental menu.

Metrics, units, optimization and limits.
Specify the sampling or reference measure for densities and integrals, the log base, and dimensionless arguments of logarithms. Total variation is $\sup_A|P(A)-Q(A)|$; Wasserstein distances require the declared ground metric and appropriate moment conditions; KL divergence uses its stated absolute-continuity convention. All normed readouts are scaled consistently, and error probabilities use $0<\alpha<1$. A ratio has a positive denominator or an explicit undefined/zero convention. Thermodynamic entropy production is nonnegative in expectation under the relevant hypotheses; individual stochastic realizations need not be. An infimum is not an attained minimum without compactness/coercivity and continuity or another existence argument. Fix the order of large-population, long-time and vanishing-mutation limits. Stability claims declare perturbation space and symmetries.

How the provisional difficulty order was assigned

Four editorial components are scored from 1 (lower burden) to 5 (higher burden): (a) formal or inferential difficulty, (b) access to evidence and identification burden, (c) mechanistic/computational integration, and (d) independent validation and transfer. Their unweighted total orders the entries. For selected entries in the highest bands, an explicit editorial priority breaks equal totals; all other ties are broken by original problem number. Equal totals are not evidence of reliably different true difficulties. Scores are judgments, not measurements, completion-time estimates or consensus rankings.

The object ranked is an informative, nondegenerate resolution of the scoped scientific target, with the stated budget and validation domain. A sharp useful impossibility result can qualify; an already known identity, vacuous bound, arbitrary favorable model or lookup table cannot. Rank 330 is merely the least difficult relative assessment in this catalogue, not a claim of an easy biological solution. Broad historical titles do not automatically outrank precisely formulated mechanistic tasks.

Selective source-check ledger

SC06

Bates et al., Distributed control circuits across a brain-and-cord connectome (Nature, online 8 June 2026), DOI 10.1038/s41586-026-10735-w.

Applied here to problems: 109, 110.

Access level: Indexed primary-paper abstract and publisher metadata.

Support and limit: Anatomical and circuit-level progress does not by itself supply a validated closed-loop model of all activity and behaviour in the declared task panel.

https://pubmed.ncbi.nlm.nih.gov/42259917/

https://www.nature.com/articles/s41586-026-10735-w

Statement licence: CC-BY-4.0 · Source file

Contributions 1

BIO-3-A1 · Conditional result · LLM-generated · gpt 6 astra pro

Attempt: Predictive whole-brain modelling in a specified fly task panel

This manuscript gives an attempted answer for each of the 330 supplied catalogue entries. It preserves the original numbering and states the target beside the attempt, distinguishing a theorem about a specified model from resolution of a biological research target. The results consist of worked special cases, identifiability obstructions, quantitative bounds, and explicitly preliminary reductions. None is claimed to resolve its complete catalogue target. No new biological experiments, empirical data analyses, or external proof verification were performed for this manuscript.

How to read the attempts

Scope of the mathematical claims.
Every proposition applies only under its displayed assumptions. The proof is an argument for that proposition, not a claim that a biological system satisfies those assumptions. A known finite generator, validated loss distribution, identified parameter, or measured response function is an input when the text says it is known. Computing with such an input does not solve its identification. The calculations are not claimed to be new discoveries; many are standard mathematical baselines or direct counterexamples.
Uneven depth and preliminary entries.
There are 200 saved problem-directed attempts and 130 preliminary conditional reductions. Problems 201--330 are preliminary reductions: a worked model or statistical criterion is provided, but its applicability to the full catalogue formulation has not been established. These entries receive 5%, not the score of a solved scientific subproblem. The later preliminary entries should be developed further before being treated as research-level attempted solutions. This distinction is also marked locally.
Completion Estimate.
Percentages are conservative, subjective assessments of coverage of the full stated target, not probabilities of correctness, measured fractions of a proof, forecasts, or literature-wide progress estimates. In particular, 100% would require resolving every part of the chosen target under its stated assumptions, including empirical validation where it is part of the target. A complete proof for a narrower model does not earn 100% for the original question. No entry in this manuscript is assigned 100%.
Sources and current-status limits.
Problem statements, definitions, alternative formulations and local source lists come from the supplied files biology_open_problems_300(2).tex and biology_open_problems_301_330(2).tex. The organization follows the supplied attempt template O33-23_v3.tex: scope, substantive calculation, explicit gap, completion estimate, and references. Inherited source annotations and links are catalogue provenance, not evidence that every link or current open status has been independently rechecked. Selected methodological references appear at the end. No exhaustive literature or novelty audit is claimed.
Notation and corrections.
Notation is local to each problem and is restated when a restricted model uses additional variables. A probability model, metric, loss and observation protocol must be specified before its numerical quantities are meaningful. One obvious missing backslash in the reconstruction norm of Problem 77 has been repaired. No biological mechanism is silently inferred from that typographical repair.
Use limitations.
Theoretical examples involving disease, therapies, genetic systems, or ecological interventions are not operational protocols, medical guidance, deployment advice, or demonstrated safety guarantees. Their assumptions and empirical gaps are part of the result, not optional qualifications.

Predictive whole-brain model

Status: restricted result or obstruction; full target not established.

Target and scope

Catalogue definitions and assumptions.
For an adult fruit fly, let $A$ contain reconstructed synapse counts and cell types, $\theta$ contain membrane, synaptic and neuromodulatory parameters, $u(t)$ sensory input, and $b(t)$ measured behaviour. A state-space model $\dot x=F_\theta(x,A,u)$ couples neural states to an explicit body and environment model $b=H_\theta(x,e)$. The anatomy comes from a particular specimen; individual differences and missing connections receive uncertainty distributions. Fix a finite ethological task and perturbation set.
Original target.
Construct a tractable model that predicts both activity and behaviour under withheld conditions: \[ \max_{a\in\mathcal A_{\rm test}}\left\{\mathbb{E}[d_r(\widehat r,r)\mid a]+\lambda\mathbb{E}[d_b(\widehat b,b)\mid a]\right\}\leq\varepsilon. \] Define metrics $d_r,d_b$ and scale factor $\lambda$ before testing. Report uncertainty and failure regimes; successful simulation of a selected response is not complete behavioural prediction.
Catalogue alternative formulations.
(i) Predict neural responses with recorded sensory input and body motion supplied, avoiding closed-loop embodiment. (ii) Predict autonomous behaviour in an environment from anatomy and sparse physiology, a much stronger target. Cross-species extension is a separate programme because cell types, circuit sizes and sensorimotor interfaces do not have an assumed one-to-one map.

Attempted answer

Proposition: Two individually stable linear modules can become unstable when coupled. For neural state $n$ and body state $b$ obeying $\dot n=-\lambda n+ab+u$, $\dot b=-\mu b+cn$, with $\lambda,\mu>0$ and $a,c\geq0$, the coupled system is asymptotically stable exactly when $ac<\lambda\mu$.
Proof: The system matrix has trace $-(\lambda+\mu)<0$ and determinant $\lambda\mu-ac$. Its two-dimensional characteristic polynomial is Hurwitz exactly when the determinant is positive. At equality there is a zero eigenvalue; above it one eigenvalue is positive. ∎

What remains unresolved

Validating isolated neural and motor modules is not enough to validate a closed-loop whole-brain model. Anatomy, nonlinear state dependence, specimen differences and held-out activity and behavior remain unmodeled.

Completion Estimate

20%. The displayed calculation is established under its additional assumptions. The broader theoretical and biological requirements listed above are not established.

References

Catalogue sources, retained as provenance:

Shared mathematical bounds

Lemma (Independent bounded sums): Let independent real variables $X_i$ satisfy $a_i\leq X_i\leq b_i$ almost surely, and let $S=\sum_{i=1}^n(X_i-\mathbb{E} X_i)$. If $V=\sum_i(b_i-a_i)^2>0$, then for every $t>0$, \[ \mathbb{P}(|S|\geq t)\leq 2\exp(-2t^2/V). \] In particular, independent losses in $[0,1]$ have mean-error bound $\mathbb{P}(|\overline X-\mathbb{E}\overline X|\geq\epsilon) \leq2e^{-2n\epsilon^2}$. The same expression for variables in $[-1,1]$ is $2e^{-n\epsilon^2/2}$. If $V=0$ then $S=0$ almost surely.
Proof: For $X\in[a,b]$ with mean $\mu$, the nonnegative product $(X-a)(b-X)$ gives $\operatorname{Var} X\leq(\mu-a)(b-\mu)\leq(b-a)^2/4$. Let $K(s)=\log\mathbb{E}\exp[s(X-\mathbb{E} X)]$. Boundedness permits two differentiations. Its second derivative is the variance under the exponentially tilted law, which has the same support, so $K''(s)\leq(b-a)^2/4$ for every real $s$. Because $K(0)=K'(0)=0$, integration yields $K(s)\leq s^2(b-a)^2/8$. Independence adds these log moment-generating bounds for $S$. For $s>0$, Markov's inequality gives $\mathbb{P}(S\geq t)\leq\exp(-st+s^2V/8)$. Choosing $s=4t/V$ yields the upper-tail bound $e^{-2t^2/V}$. Apply it also to $-S$ and add. The stated mean versions follow by substituting $t=n\epsilon$ and the respective support widths. ∎
Finite-family extension.
For $M$ prespecified bounded-loss comparisons, apply the relevant inequality to each and use a union bound. Independence between comparisons is not required; independence of the biological units within each comparison is. Repeated cells, reads or time points from one dependent biological unit cannot automatically be counted as independent units.
No data-dependent shortcut.
The bound does not justify choosing a model or validation loss using the same outcomes and then reporting single-model coverage. A prespecified finite family, a separate validation sample, or an appropriate complexity or sequential argument is needed. Marginal predictive coverage likewise should not be read as coverage for every subgroup or under arbitrary distribution shift.

Methodological references

The references below support general methods. The detailed subject references are retained locally with each problem. These entries are not a claim that the preceding problem-directed calculations appear as new results in these sources.

References

  • [hoeffding] W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58 (1963), 13--30. https://doi.org/10.1080/01621459.1963.10500830. The relevant inequality is proved above rather than left as an external step.
  • [pearl] J. Pearl. Causal inference in statistics: An overview. Statistics Surveys 3 (2009), 96--146. https://doi.org/10.1214/09-SS057.
  • [lei] J. Lei, M. G'Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman. Distribution-Free Predictive Inference For Regression. Preprint (2016). https://arxiv.org/abs/1604.04173.
  • [briat] C. Briat, A. Gupta, and M. Khammash. Antithetic Integral Feedback ensures robust perfect adaptation in noisy biomolecular networks. Preprint (2014). https://arxiv.org/abs/1410.6064.

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