Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks

• URL: http://arxiv.org/abs/2507.18467v1
• Date: Thu, 24 Jul 2025 14:41:18 GMT
• Title: Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks
• Authors: Pradeep Singh, Lavanya Sankaranarayanan, Balasubramanian Raman,
• Abstract summary: We present a unified, dynamical-systems treatment that weaves together functional analysis, random attractor theory and recent neuroscientific findings.<n>First, we prove that the Echo-State Property (wash-out of initial conditions) together with global Lipschitz dynamics necessarily yields the Fading-Memory Property.<n>Second, employing a Stone-Weierstrass strategy we give a streamlined proof that ESNs with nonlinear reservoirs and linear read-outs are dense in the Banach space of causal, time-in fading-memory filters.<n>Third, we quantify computational resources via memory-capacity spectrum, show how
• Score: 13.857230672081489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Echo-State Networks (ESNs) distil a key neurobiological insight: richly recurrent but fixed circuitry combined with adaptive linear read-outs can transform temporal streams with remarkable efficiency. Yet fundamental questions about stability, memory and expressive power remain fragmented across disciplines. We present a unified, dynamical-systems treatment that weaves together functional analysis, random attractor theory and recent neuroscientific findings. First, on compact multivariate input alphabets we prove that the Echo-State Property (wash-out of initial conditions) together with global Lipschitz dynamics necessarily yields the Fading-Memory Property (geometric forgetting of remote inputs). Tight algebraic tests translate activation-specific Lipschitz constants into certified spectral-norm bounds, covering both saturating and rectifying nonlinearities. Second, employing a Stone-Weierstrass strategy we give a streamlined proof that ESNs with polynomial reservoirs and linear read-outs are dense in the Banach space of causal, time-invariant fading-memory filters, extending universality to stochastic inputs. Third, we quantify computational resources via memory-capacity spectrum, show how topology and leak rate redistribute delay-specific capacities, and link these trade-offs to Lyapunov spectra at the \textit{edge of chaos}. Finally, casting ESNs as skew-product random dynamical systems, we establish existence of singleton pullback attractors and derive conditional Lyapunov bounds, providing a rigorous analogue to cortical criticality. The analysis yields concrete design rules-spectral radius, input gain, activation choice-grounded simultaneously in mathematics and neuroscience, and clarifies why modest-sized reservoirs often rival fully trained recurrent networks in practice.

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