# Integrative neurocybernetic modeling in the era of large-scale neuroscience

arXiv: https://arxiv.org/abs/2604.23903 · HTML: https://mlsanity.com/paper/2604.23903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 111 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 30 of 34)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
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- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://mlsanity.com/paper/2406.06484/) (2024)
- [A Practical Survey on Faster and Lighter Transformers](https://mlsanity.com/paper/2103.14636/) (2021)
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- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Towards Scalable and Stable Parallelization of Nonlinear RNNs](https://mlsanity.com/paper/2407.19115/) (2024)
- [Temporal Parallelization of Bayesian Smoothers](https://mlsanity.com/paper/1905.13002/) (2019)
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- [Neural Encoding and Decoding at Scale](https://mlsanity.com/paper/2504.08201/) (2025)
- [Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems](https://mlsanity.com/paper/2111.01256/) (2021)
- [Speed Always Wins: A Survey on Efficient Architectures for Large Language Models](https://mlsanity.com/paper/2508.09834/) (2025)
- [ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models](https://mlsanity.com/paper/2510.21450/) (2025)
- [MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior](https://mlsanity.com/paper/2207.10553/) (2022)
- [Clustering units in neural networks: upstream vs downstream information](https://mlsanity.com/paper/2203.11815/) (2022)
- [ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish](https://mlsanity.com/paper/2503.02618/) (2025)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Meta-Dynamical State Space Models for Integrative Neural Data Analysis](https://mlsanity.com/paper/2410.05454/) (2024)
- [Analyzing Populations of Neural Networks via Dynamical Model Embedding](https://mlsanity.com/paper/2302.14078/) (2023)
- [eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling](https://mlsanity.com/paper/2403.01371/) (2024)
- [Predictability Enables Parallelization of Nonlinear State Space Models](https://mlsanity.com/paper/2508.16817/) (2025)
- [Interpretable Nonlinear Dynamic Modeling of Neural Trajectories](https://mlsanity.com/paper/1608.06546/) (2016)
- [Range, not Independence, Drives Modularity in Biologically Inspired Representations](https://mlsanity.com/paper/2410.06232/) (2024)

## Abstract (arXiv)

Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body and environment, treat the brain as a controller pursuing latent objectives, represent structured variation across scales, and scale to heterogeneous datasets. Such models shift the goal from predicting neural recordings in isolation to inferring the organizing principles that govern neural and behavioral dynamics. We outline a practical route toward this goal by combining nonlinear state-space models and meta-dynamical extensions with scalable inference, knowledge distillation, mixed open- and closed-loop training, and connectomics-informed architectures. By pooling complementary constraints from recordings, behavior, perturbations and anatomy, integrative neurocybernetic models can provide statistical amplification, few-shot generalization, and mechanistic insight into shared dynamical structure, individual variation, and the control objectives that govern behavior. This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.