Vestibular reservoir computing
- URL: http://arxiv.org/abs/2604.09943v1
- Date: Fri, 10 Apr 2026 22:47:41 GMT
- Title: Vestibular reservoir computing
- Abstract summary: Reservoir computing (RC) is a computational framework known for its training efficiency.<n>This paper proposes a physical RC scheme inspired by the biological vestibular system.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However, realizing the complex interconnectivity of traditional reservoirs in physical systems remains a significant challenge. This paper proposes a physical RC scheme inspired by the biological vestibular system. To overcome hardware complexity, we introduce a designed uncoupled topology and demonstrate that it achieves performance comparable to fully coupled networks. We theoretically analyze the difference between these topologies by deriving a memory capacity formula for linear reservoirs, identifying specific conditions where both configurations yield equivalent memory. These analytical results are demonstrated to approximately hold for nonlinear reservoir systems. Furthermore, we systematically examine the impact of reservoir size on predictive statistics and memory capacity. Our findings suggest that uncoupled reservoir architectures offer a mathematically sound and practically feasible pathway for efficient physical reservoir computing.
Related papers
- Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
arXiv Detail & Related papers (2025-10-02T15:15:50Z) - Optimizing the Network Topology of a Linear Reservoir Computer [0.0]
Reservoir computing is a machine learning tool that processes temporal data for prediction and observation tasks.<n>Traditionally, the connectivity of a reservoir computer (RC) is generated at random, lacking a principled design.<n>Here, we focus on optimizing the topology of a linear RC to improve its performance and interpretability.
arXiv Detail & Related papers (2025-09-27T16:24:53Z) - Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective [13.135043580306224]
Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps from the state space.<n>This work presents a systematic exploration of RC, addressing its foundational properties such as the echo state property, fading memory, and reservoir capacity through the lens of dynamical systems theory.<n>We formalize the interplay between input signals and reservoir states, demonstrating the conditions under which reservoirs exhibit stability and expressive power.
arXiv Detail & Related papers (2025-04-16T04:28:05Z) - Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions [46.75046795995564]
In AIMC hardware, the trainable weights are represented by the conductance of resistive elements and updated using consecutive electrical pulses.<n>While the conductance changes by a constant in response to each pulse, in reality, the change is scaled by asymmetric and non-linear response functions, leading to a non-ideal training dynamic.<n>This paper provides a theoretical foundation for gradient-based training on AIMC hardware with non-ideal response functions.
arXiv Detail & Related papers (2025-02-10T09:56:15Z) - Deep Photonic Reservoir Computer for Speech Recognition [49.1574468325115]
Speech recognition is a critical task in the field of artificial intelligence and has witnessed remarkable advancements.
Deep reservoir computing is energy efficient but exhibits limitations in performance when compared to more resource-intensive machine learning algorithms.
We propose a photonic-based deep reservoir computer and evaluate its effectiveness on different speech recognition tasks.
arXiv Detail & Related papers (2023-12-11T17:43:58Z) - Restrictions on Physical Stochastic Reservoir Computers [0.0]
We study the impact of noise on the learning capabilities of analog reservoir computers.<n>We show that the information processing capacity (IPC) is a useful metric for quantifying the degradation of the performance due to noise.<n>We conclude that any physical, analog reservoir computer that is exposed to noise can only be used to perform a amount of learning.
arXiv Detail & Related papers (2023-07-26T19:41:05Z) - Optimization of a Hydrodynamic Computational Reservoir through Evolution [58.720142291102135]
We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir.
We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm.
Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
arXiv Detail & Related papers (2023-04-20T19:15:02Z) - Simulation platform for pattern recognition based on reservoir computing
with memristor networks [1.5664378826358722]
We develop a simulation platform for reservoir computing (RC) with memristor device networks.
We show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks.
arXiv Detail & Related papers (2021-12-01T03:06:13Z) - Efficient Micro-Structured Weight Unification and Pruning for Neural
Network Compression [56.83861738731913]
Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices.
Previous unstructured or structured weight pruning methods can hardly truly accelerate inference.
We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
arXiv Detail & Related papers (2021-06-15T17:22:59Z) - The Computational Capacity of LRC, Memristive and Hybrid Reservoirs [1.657441317977376]
Reservoir computing is a machine learning paradigm that uses a high-dimensional dynamical system, or emphreservoir, to approximate and predict time series data.
We analyze the feasibility and optimal design of electronic reservoirs that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements called memristors.
Our electronic reservoirs can match or exceed the performance of conventional "echo state network" reservoirs in a form that may be directly implemented in hardware.
arXiv Detail & Related papers (2020-08-31T21:24:45Z) - Understanding the Effects of Data Parallelism and Sparsity on Neural
Network Training [126.49572353148262]
We study two factors in neural network training: data parallelism and sparsity.
Despite their promising benefits, understanding of their effects on neural network training remains elusive.
arXiv Detail & Related papers (2020-03-25T10:49:22Z)
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.