The impact of homeostatic inhibitory plasticity in a generative biophysical model
Iván Mindlin, Carlos Coronel-Oliveros, Jacobo Sitt, Rodrigo Codorniu Cofré, Andrea I. Luppi, Thomas Andrillon, Yonatan Sanz Perl, Rubén Herzog
bioRxiv (Cold Spring Harbor Laboratory) January 13, 2026 preprint DOI: 10.64898/2026.01.12.699008 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Topics | Neuroplasticity |
| Keywords | Homeostatic plasticity Asynchronous communication Artificial neural network Field mathematics Convergence economics Local field potential Stability learning theory Synaptic scaling Inhibitory postsynaptic potential Adaptive behaviour Biological neural network Metaplasticity Network model Coupling piping Excitatory postsynaptic potential Central pattern generator Signalling |
| Key points | Proposes that embedding an inhibitory homeostatic plasticity rule into the Dynamic Mean Field model broadens the model's stability and expressiveness, enabling simulation of sleep-like slow-wave activity and dissociated states of consciousness. |
Abstract
A main characteristic of biological systems is their capacity to dynamically adapt to environmental changes. In the brain, synaptic plasticity enables the strengthening or weakening of connections between neurons, allowing neural circuits to adapt based on experience, learning, and environmental changes. Yet, it is homeostatically regulated such that it avoids excessive proliferation of synaptic contacts. These mechanisms can be studied with large-scale models of brain activity. Here, we embed a biologically grounded inhibitory homeostatic plasticity rule into the Dynamic Mean Field (DMF) model, creating a Homeostatic Dynamic Mean Field (HDMF) model that dynamically tunes local excitation-inhibition balance. Convergence of excitatory firing rates is reached by mapping a large range of coupling strength to parameters of inhibitory synapses. The HDMF reproduces statistical observables of brain activity as well as the original DMF, and can sustain neuromodulatory perturbations without overhead computations. The HDMF can generate unprecedented sleep-like slow-wave activity, which can also coexist with wake-like asynchronous dynamics, permitting to model dissociated states of consciousness such as parasomnias. Together, these results show that a single homeostatic rule broadens the stability and expressiveness of the DMF, providing a unified platform for studying how local adaptive processes shape the diverse global dynamics of the human brain.
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Citations in the library
Cites 10
- Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD. Current Biology September 27, 2018
- Pre-sleep treatment with galantamine stimulates lucid dreaming: A double-blind, placebo-controlled, crossover study PLoS One August 8, 2018
- A whole-brain model of the neural entropy increase elicited by psychedelic drugs. Scientific Reports April 17, 2023
- What is sleep exactly? Global and local modulations of sleep oscillations all around the clock. Neuroscience & Biobehavioral Reviews December 1, 2023
- Whole brain modelling for simulating pharmacological interventions on patients with disorders of consciousness. Communications Biology September 19, 2024
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