Reproduction of AdEx dynamics on neuromorphic hardware through data embedding and simulation-based inference

• URL: http://arxiv.org/abs/2412.02437v1
• Date: Tue, 03 Dec 2024 13:19:21 GMT
• Title: Reproduction of AdEx dynamics on neuromorphic hardware through data embedding and simulation-based inference
• Authors: Jakob Huhle, Jakob Kaiser, Eric Müller, Johannes Schemmel,
• Abstract summary: We use an autoencoder to automatically extract relevant features from the membrane trace of a complex neuron model. We then leverage sequential neural posterior estimation to approximate the posterior distribution of neuron parameters. This suggests that the combination of an autoencoder with the SNPE algorithm is a promising optimization method for complex systems.
• Score: 0.8437187555622164
• License:
• Abstract: The development of mechanistic models of physical systems is essential for understanding their behavior and formulating predictions that can be validated experimentally. Calibration of these models, especially for complex systems, requires automated optimization methods due to the impracticality of manual parameter tuning. In this study, we use an autoencoder to automatically extract relevant features from the membrane trace of a complex neuron model emulated on the BrainScaleS-2 neuromorphic system, and subsequently leverage sequential neural posterior estimation (SNPE), a simulation-based inference algorithm, to approximate the posterior distribution of neuron parameters. Our results demonstrate that the autoencoder is able to extract essential features from the observed membrane traces, with which the SNPE algorithm is able to find an approximation of the posterior distribution. This suggests that the combination of an autoencoder with the SNPE algorithm is a promising optimization method for complex systems.

Related papers

• Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations [1.5467259918426441]
  We propose a framework for developing computational models of biological neural systems. We employ a system of coupled differential equations with differentiable drift and diffusion functions. 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)
• 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)
• Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics [3.909855210960908]
  Equi Probabilistic Neural Simulation (EPNS) is a framework for autoregressive modeling of equivariant distributions over system evolutions. EPNS considerably outperforms existing neural network-based methods for probabilistic simulation.
  arXiv  Detail & Related papers  (2023-05-23T17:30:10Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware [1.843681725117436]
  This study investigates the suitability of the sequential neural posterior estimation algorithm for parameterizing a multi-compartmental neuron model emulated on the BrainScaleS-2 system. The SNPE algorithm belongs to the class of simulation-based inference methods and estimates the posterior distribution of the model parameters.
  arXiv  Detail & Related papers  (2023-03-28T15:37:30Z)
• Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach [0.0]
  This study presents a systematic machine learning approach for creating efficient hybrid models and discovering sorption uptake models in non-fusion advection-difsorption systems. It demonstrates an effective method to train these complex systems using gradient based analysis, adjoint sensitivity analysis, and JIT-compiled vector Jacobian products, combined with spatial discretization and adaptive discretization.
  arXiv  Detail & Related papers  (2023-03-22T23:05:28Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Automatic Evolution of Machine-Learning based Quantum Dynamics with Uncertainty Analysis [4.629634111796585]
  The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics. This work builds an effective machine learning approach to simulate the dynamics evolution of open quantum systems.
  arXiv  Detail & Related papers  (2022-05-07T08:53:55Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• 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)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)

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.