LIPIcs.DISC.2019.48.pdf
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Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit
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33rd International Symposium on Distributed Computing (DISC 2019)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Distributed Computing (DISC) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2019-10-08
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Nancy Lynch and Mien Brabeeba Wang. Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 48:1-48:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.DISC.2019.48
https://doi.org/10.4230/LIPIcs.DISC.2019.48
Abstract
In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting" and "Total Spikes Counting", which model temporal-coding and rate-coding aspects of temporal-to-spatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(log T) neurons and terminating in time T+1. We also prove that these bounds are tight.
Subject Classification
ACM Subject Classification
- Theory of computation → Distributed computing models
- Theory of computation → Distributed algorithms
- Applied computing → Biological networks
Keywords
- Spiking Neural Network
- Distributed Algorithm
- Biological Networks
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References
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Chi-Ning Chou, Kai-Min Chung, and Chi-Jen Lu. On the Algorithmic Power of Spiking Neural Networks. ITCS, 2019.
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Nancy A. Lynch, Cameron Musco, and Merav Parter. Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. ITCS, 2017.