Gradient Diffusion: Sensitivity-Matrix Co-Simulation Enables Activity Adaptation and Learnable Plasticity in Neural Simulators

TL;DR

This paper introduces a method for online tuning of neural model parameters using sensitivity equations, enabling activity adaptation and plasticity in brain simulations, which enhances biological realism and flexibility.

Contribution

It demonstrates that sensitivity equations can be integrated into brain simulators for real-time parameter tuning and plasticity modeling, a novel approach in computational neuroscience.

Findings

Sensitivity equations match neuron model shapes.

Enables online and offline activity tuning.

Facilitates study of biological plasticity mechanisms.

Abstract

Computational neuroscience relies on large-scale dynamical-systems models of neurons, with a vast amount of offline, pre-simulation, tuned parameters, with models often tied to their brain simulators. These fixed parameters lead to stiff models, that show unnatural behaviour when introduced to new environments, or when combined into larger networks. In contrast to offline tuning, in biology, cells continuously adapt via homeostatic plasticity to stay in desired dynamical regimes. In this work, we aim to introduce such online tuning of cellular parameters into brain simulation. We show that the sensitivity equation of a biorealistic neural models has the same shape as a general neuron model, and can be simulated within existing brain simulators. Via co-simulation with the sensitivity equation, we enable both offline, and online tuning of activity of arbitrary biophysically realistic brain models. Furthermore, we show that this opens the possibility to study the biological mechanisms underlying homeostatic plasticity, via both meta-learning plasticity mechanism as well as treating online tuning as a black-box plasticity mechanism. Through the generality of our methods, we hope that more computational science fields can capitalize on the similarity between the simulated model and its gradient system.