System identification of biophysical neuronal models

• URL: http://arxiv.org/abs/2012.07691v1
• Date: Mon, 14 Dec 2020 16:41:27 GMT
• Title: System identification of biophysical neuronal models
• Abstract summary: The identification of neuronal dynamics from input-output data remains a challenging problem. GOBFs are particularly well-suited to tackle the identification problem. The method is illustrated on the identification of a bursting model from the crab stomatogastric ganglion.
• Score: 1.7188280334580197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: After sixty years of quantitative biophysical modeling of neurons, the identification of neuronal dynamics from input-output data remains a challenging problem, primarily due to the inherently nonlinear nature of excitable behaviors. By reformulating the problem in terms of the identification of an operator with fading memory, we explore a simple approach based on a parametrization given by a series interconnection of Generalized Orthonormal Basis Functions (GOBFs) and static Artificial Neural Networks. We show that GOBFs are particularly well-suited to tackle the identification problem, and provide a heuristic for selecting GOBF poles which addresses the ultra-sensitivity of neuronal behaviors. The method is illustrated on the identification of a bursting model from the crab stomatogastric ganglion.

Related papers

• Robust Parameter and State Estimation in Multiscale Neuronal Systems Using Physics-Informed Neural Networks [2.020688298620136]
  Inferring biophysical parameters and hidden state variables from partial and noisy observations is a fundamental challenge in computational neuroscience.<n>In this work, we developed a physics-informed neural network (PINN) framework for the joint reconstruction of unobserved state variables.<n>Results suggest that PINN can deliver robust and accurate parameter inference and state reconstruction, providing a promising alternative for inverse problems in multiscale neuronal dynamics.
  arXiv  Detail & Related papers  (2026-02-27T21:22:45Z)
• Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective [89.99666725996975]
  The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from data and their chronological age.<n>This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP)<n>VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions.
  arXiv  Detail & Related papers  (2025-10-14T17:44:45Z)
• 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 [68.89389652724378]
  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 validated on real experimental data.
  arXiv  Detail & Related papers  (2025-06-05T01:01:18Z)
• Single-neuron deep generative model uncovers underlying physics of neuronal activity in Ca imaging data [0.0]
  We propose a novel framework for single-neuron representation learning using autoregressive variational autoencoders (AVAEs) Our approach embeds individual neurons' signals into a reduced-dimensional space without the need for spike inference algorithms. The AVAE excels over traditional linear methods by generating more informative and discriminative latent representations.
  arXiv  Detail & Related papers  (2025-01-24T16:33:52Z)
• About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks [0.0]
  A rectified sigmoid activation function has been proposed to solve physical problems described by the ODE with neural networks. Numerical experiments demonstrate the superiority of neural networks with a rectified sigmoid function over neural networks with a sigmoid function.
  arXiv  Detail & Related papers  (2024-12-30T10:42:28Z)
• Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models [2.600709013150986]
  Understanding the neural basis of behavior is a fundamental goal in neuroscience. Our approach, named BeNeDiff'', first identifies a fine-grained and disentangled neural subspace. It then employs state-of-the-art generative diffusion models to synthesize behavior videos that interpret the neural dynamics of each latent factor.
  arXiv  Detail & Related papers  (2024-10-12T18:28:56Z)
• Deep Latent Variable Modeling of Physiological Signals [0.8702432681310401]
  We explore high-dimensional problems related to physiological monitoring using latent variable models. First, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. Second, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. Third, we propose a framework for the joint modeling of physiological measures and behavior.
  arXiv  Detail & Related papers  (2024-05-29T17:07:33Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• 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)
• Physically constrained neural networks to solve the inverse problem for neuron models [0.29005223064604074]
  Systems biology and systems neurophysiology are powerful tools for a number of key applications in the biomedical sciences. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators.
  arXiv  Detail & Related papers  (2022-09-24T12:51:15Z)
• 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)
• Modeling Implicit Bias with Fuzzy Cognitive Maps [0.0]
  This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets. We introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating.
  arXiv  Detail & Related papers  (2021-12-23T17:04:12Z)
• 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)
• 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)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)

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.