# Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics

arXiv: https://arxiv.org/abs/2504.12480 · HTML: https://mlsanity.com/paper/2504.12480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model](https://mlsanity.com/paper/1803.04779/) (2018)
- [Efficient multivariate entropy estimation via k-nearest neighbour distances](https://mlsanity.com/paper/1606.00304/) (2016)
- [Do Reservoir Computers Work Best at the Edge of Chaos?](https://mlsanity.com/paper/2012.01409/) (2020)
- [Parallel Machine Learning for Forecasting the Dynamics of Complex Networks](https://mlsanity.com/paper/2108.12129/) (2021)
- [Bayesian optimization of hyper-parameters in reservoir computing](https://mlsanity.com/paper/1611.05193/) (2016)
- [Biological neurons act as generalization filters in reservoir computing](https://mlsanity.com/paper/2210.02913/) (2022)
- [Optimal modularity and memory capacity of neural reservoirs](https://mlsanity.com/paper/1706.06511/) (2017)
- [Bayesian optimisation of large-scale photonic reservoir computers](https://mlsanity.com/paper/2004.02535/) (2020)
- [Unveiling the role of plasticity rules in reservoir computing](https://mlsanity.com/paper/2101.05848/) (2021)
- [Learning Reservoir Dynamics with Temporal Self-Modulation](https://mlsanity.com/paper/2301.09235/) (2023)
- [Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex](https://mlsanity.com/paper/1410.2610/) (2014)
- [RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization](https://mlsanity.com/paper/2207.05870/) (2022)
- [Tuning the activation function to optimize the forecast horizon of a reservoir computer](https://mlsanity.com/paper/2312.13151/) (2023)
- [Benchmarking Learning Efficiency in Deep Reservoir Computing](https://mlsanity.com/paper/2210.02549/) (2022)

## Abstract (arXiv)

Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles. By using an internal neural network with random, fixed connections-the 'reservoir'-and training only the output weights, RCs simplify the training process but remain sensitive to the choice of hyperparameters that govern activation functions and network architecture. Moreover, typical RC implementations overlook a critical aspect of neuronal dynamics: the balance between excitatory and inhibitory (E-I) signals, which is essential for robust brain function. We show that RCs characteristically perform best in balanced or slightly over-inhibited regimes, outperforming excitation-dominated ones. To reduce the need for precise hyperparameter tuning, we introduce a self-adapting mechanism that locally adjusts E/I balance to achieve target neuronal firing rates, improving performance by up to 130% in tasks like memory capacity and time series prediction compared with globally tuned RCs. Incorporating brain-inspired heterogeneity in target neuronal firing rates further reduces the need for fine-tuning hyperparameters and enables RCs to excel across linear and non-linear tasks. These results support a shift from static optimization to dynamic adaptation in reservoir design, demonstrating how brain-inspired mechanisms improve RC performance and robustness while deepening our understanding of neural computation.