SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding

• URL: http://arxiv.org/abs/2511.06761v2
• Date: Wed, 19 Nov 2025 03:13:12 GMT
• Title: SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
• Authors: Fei Yang,
• Abstract summary: This paper introduces the Spatiotemporal Network (SRNN), a model that establishes a unified representation for neural object attributes, relations and timeline.<n>On the CLEVR benchmark, SRNN achieves competitive performance, thereby confirming its capability to represent essential language relations from the visual stream.<n>Our work provides a proof-of-concept that confirms the viability of translating key neural intelligence into engineered systems for intuitive physics understanding in constrained environments.
• Score: 5.9229807497571665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human prowess in intuitive physics remains unmatched by machines. To bridge this gap, we argue for a fundamental shift towards brain-inspired computational principles. This paper introduces the Spatiotemporal Relational Neural Network (SRNN), a model that establishes a unified neural representation for object attributes, relations, and timeline, with computations governed by a Hebbian ``Fire Together, Wire Together'' mechanism across dedicated \textit{What} and \textit{How} pathways. This unified representation is directly used to generate structured linguistic descriptions of the visual scene, bridging perception and language within a shared neural substrate. On the CLEVRER benchmark, SRNN achieves competitive performance, thereby confirming its capability to represent essential spatiotemporal relations from the visual stream. Cognitive ablation analysis further reveals a benchmark bias, outlining a path for a more holistic evaluation. Finally, the white-box nature of SRNN enables precise pinpointing of error root causes. Our work provides a proof-of-concept that confirms the viability of translating key principles of biological intelligence into engineered systems for intuitive physics understanding in constrained environments.

Related papers

• Concept-Guided Interpretability via Neural Chunking [64.6429903327095]
  We show that neural networks exhibit patterns in their raw population activity that mirror regularities in the training data.<n>We propose three methods to extract recurring chunks on a neural population level.<n>Our work points to a new direction for interpretability, one that harnesses both cognitive principles and the structure of naturalistic data.
  arXiv  Detail & Related papers  (2025-05-16T13:49:43Z)
• Steinmetz Neural Networks for Complex-Valued Data [23.80312814400945]
  We introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valuedworks with coupled outputs.<n>Our proposed class of architectures, referred to as Steinmetz Neural Networks, incorporates multi-view learning to construct more interpretable representations in the latent space.<n>Our numerical experiments depict the improved performance and robustness to additive noise, afforded by our proposed networks on benchmark datasets and synthetic examples.
  arXiv  Detail & Related papers  (2024-09-16T08:26:06Z)
• GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks [5.2969467015867915]
  We introduce the concept of interpretability pipelineing, to incorporate multiple interpretability techniques to outperform each individual technique. We evaluate two recent models selected for their potential to incorporate interpretability into standard neural network architectures. We introduce a novel interpretable neural network GINN-KAN that synthesizes the advantages of both models.
  arXiv  Detail & Related papers  (2024-08-27T04:57:53Z)
• Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning [91.29876772547348]
  Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning.
  arXiv  Detail & Related papers  (2024-05-27T05:53:30Z)
• Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale [54.15522908057831]
  We propose an adapted version of the computationally-Mixer for STTD forecast at scale. Our results surprisingly show that this simple-yeteffective solution can rival SOTA baselines when tested on several traffic benchmarks. Our findings contribute to the exploration of simple-yet-effective models for real-world STTD forecasting.
  arXiv  Detail & Related papers  (2023-07-04T05:19:19Z)
• Neural-Symbolic Recursive Machine for Systematic Generalization [113.22455566135757]
  We introduce the Neural-Symbolic Recursive Machine (NSR), whose core is a Grounded Symbol System (GSS) NSR integrates neural perception, syntactic parsing, and semantic reasoning. We evaluate NSR's efficacy across four challenging benchmarks designed to probe systematic generalization capabilities.
  arXiv  Detail & Related papers  (2022-10-04T13:27:38Z)
• Knowledge Enhanced Neural Networks for relational domains [83.9217787335878]
  We focus on a specific method, KENN, a Neural-Symbolic architecture that injects prior logical knowledge into a neural network. In this paper, we propose an extension of KENN for relational data.
  arXiv  Detail & Related papers  (2022-05-31T13:00:34Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Geometry Perspective Of Estimating Learning Capability Of Neural Networks [0.0]
  The paper considers a broad class of neural networks with generalized architecture performing simple least square regression with gradient descent (SGD) The relationship between the generalization capability with the stability of the neural network has also been discussed. By correlating the principles of high-energy physics with the learning theory of neural networks, the paper establishes a variant of the Complexity-Action conjecture from an artificial neural network perspective.
  arXiv  Detail & Related papers  (2020-11-03T12:03:19Z)
• A Graph Neural Network Framework for Causal Inference in Brain Networks [0.3392372796177108]
  A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static backbone. We present a graph neural network (GNN) framework to describe functional interactions based on structural anatomical layout. We show that GNNs are able to capture long-term dependencies in data and also scale up to the analysis of large-scale networks.
  arXiv  Detail & Related papers  (2020-10-14T15:01:21Z)
• Neural Networks Enhancement with Logical Knowledge [83.9217787335878]
  We propose an extension of KENN for relational data. The results show that KENN is capable of increasing the performances of the underlying neural network even in the presence relational data.
  arXiv  Detail & Related papers  (2020-09-13T21:12:20Z)
• Neural Networks with Recurrent Generative Feedback [61.90658210112138]
  We instantiate this design on convolutional neural networks (CNNs) In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks.
  arXiv  Detail & Related papers  (2020-07-17T19:32:48Z)
• Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory [5.33024001730262]
  Recurrent neural networks (RNNs) are brain-inspired models widely used in machine learning for analyzing sequential data. We show how RNNs process input signals using the response theory from nonequilibrium statistical mechanics.
  arXiv  Detail & Related papers  (2020-06-19T10:09:09Z)

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.