Robust online reconstruction of continuous-time signals from a lean spike train ensemble code

• URL: http://arxiv.org/abs/2408.05950v2
• Date: Wed, 14 Aug 2024 16:43:36 GMT
• Title: Robust online reconstruction of continuous-time signals from a lean spike train ensemble code
• Authors: Anik Chattopadhyay, Arunava Banerjee,
• Abstract summary: This paper presents a signal processing framework that deterministically encodes continuous-time signals into spike trains. It addresses the questions about representable signal classes and reconstruction bounds. Experiments on a large audio dataset demonstrate excellent reconstruction accuracy at spike rates as low as one-fifth of the Nyquist rate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sensory stimuli in animals are encoded into spike trains by neurons, offering advantages such as sparsity, energy efficiency, and high temporal resolution. This paper presents a signal processing framework that deterministically encodes continuous-time signals into biologically feasible spike trains, and addresses the questions about representable signal classes and reconstruction bounds. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons using a convolve-then-threshold mechanism with various convolution kernels. A closed-form solution to the inverse problem, from spike trains to signal reconstruction, is derived in the Hilbert space of shifted kernel functions, ensuring sparse representation of a generalized Finite Rate of Innovation (FRI) class of signals. Additionally, inspired by real-time processing in biological systems, an efficient iterative version of the optimal reconstruction is formulated that considers only a finite window of past spikes, ensuring robustness of the technique to ill-conditioned encoding; convergence guarantees of the windowed reconstruction to the optimal solution are then provided. Experiments on a large audio dataset demonstrate excellent reconstruction accuracy at spike rates as low as one-fifth of the Nyquist rate, while showing clear competitive advantage in comparison to state-of-the-art sparse coding techniques in the low spike rate regime.

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