Integrative neurocybernetic modeling in the era of large-scale neuroscience
- URL: http://arxiv.org/abs/2604.23903v1
- Date: Sun, 26 Apr 2026 22:26:28 GMT
- Title: Integrative neurocybernetic modeling in the era of large-scale neuroscience
- Abstract summary: We argue that understanding behavior requires integrative neurocybernetic models.<n>Such models shift the goal from predicting neural recordings to inferring the organizing principles that govern neural and behavioral dynamics.<n>This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.
- Score: 10.509674860067108
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- JEDI: Jointly Embedded Inference of Neural Dynamics [12.034164909682366]
We introduce JEDI, a hierarchical model that captures neural dynamics across tasks and contexts.<n>Using simulated RNN datasets, we demonstrate that JEDI accurately learns robust, generalizable, condition-specific embeddings.<n>Our work shows that joint learning of contextual embeddings and recurrent weights provides scalable and generalizable inference of brain dynamics from recordings alone.
arXiv Detail & Related papers (2026-03-11T07:31:20Z) - Neuronal Group Communication for Efficient Neural representation [85.36421257648294]
This paper addresses the question of how to build large neural systems that learn efficient, modular, and interpretable representations.<n>We propose Neuronal Group Communication (NGC), a theory-driven framework that reimagines a neural network as a dynamical system of interacting neuronal groups.<n>NGC treats weights as transient interactions between embedding-like neuronal states, with neural computation unfolding through iterative communication among groups of neurons.
arXiv Detail & Related papers (2025-10-19T14:23:35Z) - Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
arXiv Detail & Related papers (2025-07-15T17:57:48Z) - NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models [63.592664795493725]
NOBLE is a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection.<n>It predicts distributions of neural dynamics accounting for the intrinsic experimental variability.<n>NOBLE is the first scaled-up deep learning framework that validates its generalization with real experimental data.
arXiv Detail & Related papers (2025-06-05T01:01:18Z) - BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation [6.3559178227943764]
We propose BLEND, a behavior-guided neural population dynamics modeling framework via privileged knowledge distillation.<n>By considering behavior as privileged information, we train a teacher model that takes both behavior observations (privileged features) and neural activities (regular features) as inputs.<n>A student model is then distilled using only neural activity.
arXiv Detail & Related papers (2024-10-02T12:45:59Z) - Graph-Based Representation Learning of Neuronal Dynamics and Behavior [2.3859858429583665]
We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework that models time-varying neuronal connectivity.<n>TAVRNN learns latent dynamics at the single-unit level while maintaining interpretable population-level representations.<n>We validate TAVRNN on three diverse datasets: (1) electrophysiological data from a freely behaving rat, (2) primate somatosensory cortex recordings during a reaching task, and (3) biological neurons in the DishBrain platform interacting with a virtual game environment.
arXiv Detail & Related papers (2024-10-01T13:19:51Z) - Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - Ranking of Communities in Multiplex Spatiotemporal Models of Brain
Dynamics [0.0]
We propose an interpretation of neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMs)
This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques.
We produce a new tool for determining important communities of brain regions using a random walk-based procedure.
arXiv Detail & Related papers (2022-03-17T12:14:09Z) - EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting.
We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
arXiv Detail & Related papers (2022-02-21T18:59:03Z) - Overcoming the Domain Gap in Contrastive Learning of Neural Action
Representations [60.47807856873544]
A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior.
We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies.
This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
arXiv Detail & Related papers (2021-11-29T15:27:51Z) - Is the brain macroscopically linear? A system identification of resting
state dynamics [7.312557272609717]
A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity.
We provide a rigorous and data-driven investigation of this hypothesis at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics.
Our results can greatly facilitate our understanding of macroscopic neural dynamics and the principled design of model-based interventions for the treatment of neuropsychiatric disorders.
arXiv Detail & Related papers (2020-12-22T20:51:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.