Single-Entity Spiking Neuron Models: Survey
- URL: http://arxiv.org/abs/2607.07429v1
- Date: Wed, 08 Jul 2026 13:58:04 GMT
- Title: Single-Entity Spiking Neuron Models: Survey
- Abstract summary: In addition to spiking models, different types of discrete and continuous analogs are considered to accurately simulate biological processes.<n>The models under investigation include neurons and various components encountered in neural systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we reviewed different approaches in mathematical modeling of biologically plausible neural systems. Models are characterized and classified based on their common features and special use cases. In addition to spiking models, different types of discrete and continuous analogs are considered to accurately simulate biological processes, including membrane potential dynamics. The models under investigation include neurons and various components encountered in neural systems and affected the dynamics. The selection of specific approaches was driven by their prevalence and innovative perspectives in order to enhance the relevance of the presented information.
Related papers
- Learning dynamical systems with biochemically informed neural ordinary differential equations [0.0]
We propose a neural-ODE framework that retains the stoichiometric structure of mechanistic models while representing individual processes by neural networks.<n>In BINODEs, the outputs of neural network processes (NNPs) are mapped to state derivatives through a linear layer analogous to a stoichiometric matrix.<n>We show that BINODEs offer a useful compromise between mechanistic interpretability and data-driven flexibility for modeling partially known biochemical or biological dynamical systems.
arXiv Detail & Related papers (2026-05-22T19:43:41Z) - Spiking Neural Models for Decision-Making Tasks with Learning [0.29998889086656577]
We propose a biologically plausible Spiking Neural Network (SNN) model for decision-making that incorporates a learning mechanism.<n>This work provides a significant step toward integrating biologically relevant neural mechanisms into cognitive models.
arXiv Detail & Related papers (2025-06-10T09:19:40Z) - 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) - Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations [1.5467259918426441]
We propose a framework for developing computational models of biological neural systems.<n>We employ a system of coupled differential equations with differentiable drift and diffusion functions.<n>We show that these hybrid models achieve competitive performance in predicting stimulus-evoked neural and behavioral responses.
arXiv Detail & Related papers (2024-12-01T09:36:03Z) - Range, not Independence, Drives Modularity in Biologically Inspired Representations [52.48094670415497]
We develop a theory of when biologically inspired networks modularise their representation of source variables (sources)<n>We derive necessary and sufficient conditions on a sample of sources that determine whether the neurons in an optimal linear autoencoder modularise.<n>Our theory applies to any dataset, extending far beyond the case of statistical independence studied in previous work.
arXiv Detail & Related papers (2024-10-08T17:41:37Z) - Modularity in Transformers: Investigating Neuron Separability & Specialization [0.0]
Transformer models are increasingly prevalent in various applications, yet our understanding of their internal workings remains limited.
This paper investigates the modularity and task specialization of neurons within transformer architectures, focusing on both vision (ViT) and language (Mistral 7B) models.
Using a combination of selective pruning and MoEfication clustering techniques, we analyze the overlap and specialization of neurons across different tasks and data subsets.
arXiv Detail & Related papers (2024-08-30T14:35:01Z) - 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) - Identifying stimulus-driven neural activity patterns in multi-patient
intracranial recordings [0.26651200086513094]
Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition.
This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case.
We will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings.
arXiv Detail & Related papers (2022-02-04T01:29:57Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Continuous Learning and Adaptation with Membrane Potential and
Activation Threshold Homeostasis [91.3755431537592]
This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model.
The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input.
Experiments demonstrate the model's ability to adapt to and continually learn from its input.
arXiv Detail & Related papers (2021-04-22T04:01:32Z) - The Neural Coding Framework for Learning Generative Models [91.0357317238509]
We propose a novel neural generative model inspired by the theory of predictive processing in the brain.
In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
arXiv Detail & Related papers (2020-12-07T01:20:38Z)
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.