# Astrocytes as a mechanism for meta-plasticity and contextually-guided network function

arXiv: https://arxiv.org/abs/2311.03508 · HTML: https://mlsanity.com/paper/2311.03508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Hierarchical Multiscale Recurrent Neural Networks](https://mlsanity.com/paper/1609.01704/) (2016)
- [Attractor and integrator networks in the brain](https://mlsanity.com/paper/2112.03978/) (2021)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Linking connectivity, dynamics and computations in low-rank recurrent neural networks](https://mlsanity.com/paper/1711.09672/) (2017)
- [Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks](https://mlsanity.com/paper/2005.11633/) (2020)
- [Dynamics of random recurrent networks with correlated low-rank structure](https://mlsanity.com/paper/1909.04358/) (2019)
- [Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior](https://mlsanity.com/paper/2211.09906/) (2022)
- [Local Dynamics in Trained Recurrent Neural Networks](https://mlsanity.com/paper/1511.05222/) (2015)
- [Formation of working memory in a spiking neuron network accompanied by astrocytes](https://mlsanity.com/paper/2011.01750/) (2020)
- [Properties of networks with partially structured and partially random connectivity](https://mlsanity.com/paper/1311.4672/) (2013)
- [Transitions among metastable states underlie context-dependent working memories in a multiple timescale network](https://mlsanity.com/paper/2104.10829/) (2021)
- [Energy Regularized RNNs for Solving Non-Stationary Bandit Problems](https://mlsanity.com/paper/2303.06552/) (2023)

## Built on by (most cited first, 1 of 1)

- [Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics](https://mlsanity.com/paper/2506.00138/) (2025)

## Abstract (arXiv)

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes may play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably versus dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results indicate how the presence of neuron-astrocyte interaction in the brain may benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.