Reduction of Probabilistic Chemical Reaction Networks

• URL: http://arxiv.org/abs/2606.27737v1
• Date: Fri, 26 Jun 2026 05:36:32 GMT
• Title: Reduction of Probabilistic Chemical Reaction Networks
• Abstract summary: We show how chemical reaction networks can be used to program adaptive behaviors at the cellular level.<n>By recovering the factor graph structure encoded in Napp--Adams-compiled CRNs, we transport recent factor-graph reduction results to their chemical implementations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemented in biochemical systems. Chemical reaction networks (CRNs) provide such a substrate and have been shown to realize probabilistic models, including hidden Markov models and factor graphs, with dynamics reproducing Bayesian inference and belief propagation. However, encoding these algorithms typically requires prohibitively large reaction networks, and classical CRN reduction techniques do not directly apply. By recovering the factor graph structure encoded in Napp--Adams-compiled CRNs, we transport recent factor-graph reduction results to their chemical implementations, obtaining significantly smaller CRNs while preserving the belief-propagation fixed points on surviving variables.

Related papers

• General Self-Prediction Enhancement for Spiking Neurons [71.01912385372577]
  Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
  arXiv  Detail & Related papers  (2026-01-29T15:08:48Z)
• Quantum Walks for Chemical Reaction Networks [0.061573828205377185]
  We lay the foundation for a quantum algorithmic framework to analyse fixed-structure chemical reaction networks.<n>We develop quantum algorithms that decide reachability of target species after perturbation.<n>Our approach offers new tools for analysing the structure and energetics of complex CRNs.
  arXiv  Detail & Related papers  (2025-09-09T16:14:16Z)
• RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks [26.003193122060697]
  Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. We propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes.
  arXiv  Detail & Related papers  (2023-08-13T14:59:27Z)
• Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction [45.84205238554709]
  We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. We include the Gibbs-Duhem equation explicitly in the loss function for training neural networks.
  arXiv  Detail & Related papers  (2023-05-31T07:36:45Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• Reactmine: a search algorithm for inferring chemical reaction networks from time series data [0.0]
  Inferring chemical reaction networks (CRN) from time series data is a challenge encouraged by the growing availability of quantitative temporal data at the cellular level. We present Reactmine, a CRN learning algorithm which enforces sparsity by inferring reactions in a sequential fashion within a search tree of bounded depth. We show that Reactmine succeeds both on simulation data by retrieving hidden CRNs where SINDy fails, and on the two real datasets by inferring reactions in agreement with previous studies.
  arXiv  Detail & Related papers  (2022-09-07T14:30:33Z)
• A Robust Backpropagation-Free Framework for Images [47.97322346441165]
  We present an error kernel driven activation alignment algorithm for image data. EKDAA accomplishes through the introduction of locally derived error transmission kernels and error maps. Results are presented for an EKDAA trained CNN that employs a non-differentiable activation function.
  arXiv  Detail & Related papers  (2022-06-03T21:14:10Z)
• Programming and Training Rate-Independent Chemical Reaction Networks [9.001036626196258]
  Natural biochemical systems are typically modeled by chemical reaction networks (CRNs) CRNs can be used as a specification language for synthetic chemical computation. We show a technique to program NC-CRNs using well-founded deep learning methods.
  arXiv  Detail & Related papers  (2021-09-20T15:31:03Z)
• Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions [50.674773358075015]
  We propose a simple graph-based network structure called GCHP, which utilizes only graph convolutional layers. We show that GCHP can significantly reduce training time and the likelihood ratio loss with interarrival time probability assumptions can greatly improve the model performance.
  arXiv  Detail & Related papers  (2021-07-07T16:59:14Z)
• A simple normative network approximates local non-Hebbian learning in the cortex [12.940770779756482]
  Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals. Here, adopting a normative approach, we model these instructive signals as supervisory inputs guiding the projection of the feedforward data. Online algorithms can be implemented by neural networks whose synaptic learning rules resemble calcium plateau potential dependent plasticity observed in the cortex.
  arXiv  Detail & Related papers  (2020-10-23T20:49:44Z)
• Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm [62.997667081978825]
  Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm. We show that the algorithm can be further simplified and made more biologically plausible by introducing a learnable set of backwards weights. We also investigate whether another biologically implausible assumption of the original AR algorithm -- the frozen feedforward pass -- can be relaxed without damaging performance.
  arXiv  Detail & Related papers  (2020-10-13T08:02:38Z)
• Relaxing the Constraints on Predictive Coding Models [62.997667081978825]
  Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs is the minimization of prediction errors. Standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance.
  arXiv  Detail & Related papers  (2020-10-02T15:21:37Z)
• Retrosynthesis Prediction with Conditional Graph Logic Network [118.70437805407728]
  Computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. We propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks.
  arXiv  Detail & Related papers  (2020-01-06T05:36:57Z)

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.