合成生物神经网络:从当前的实施到未来的前景
Ana Halužan Vasle1, Miha Moškon1
1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Bio Systems
|February 25, 2024
概括
合成生物神经网络 (SYNBIONNs) 模仿大脑在医学和生物传感方面的应用. 目前的SYNBIONN实现是有限的,但未来的研究目标是可扩展的,具有在线学习能力的体内网络.
科学领域:
- 合成生物学 合成生物学
- 计算神经科学是一种计算神经科学.
- 生物工程是生物工程.
背景情况:
- 人工神经网络 (ANN) 是由人类大脑启发的强大的计算模型.
- 合成生物学旨在创造ANN的生物对应物,称为合成生物神经网络 (SYNBIONNs).
- 协同生物具有医学,生物传感器和生物技术的潜力.
研究的目的:
- 审查目前的SYNBIONNs的实施和模型.
- 探索适合构建SYNBIONNs的生物平台.
- 讨论体内SYNBIONNs的未来可能性和挑战.
主要方法:
- 审查现有的SYNBIONN文献和模型.
- 对SYNBIONN设计的生物平台的识别.
- 对可扩展,体内SYNBIONN实施的挑战进行分析.
主要成果:
- 目前,SYNBIONN的实施很少,并且严重依赖于in silico预培训和人类投入.
- 各种生物平台显示出开发感知子和多层SYNBIONN的前景.
- 通过在线学习实现可扩展的体内生物神经网络仍然存在重大挑战.
结论:
- 尽管目前的局限性,SYNBIONNs为各种应用提供了令人兴奋的可能性.
- 需要进一步的研究,以克服在实践中活体SYNBIONN部署的技术障碍.
- 开发可扩展的,自我学习的生物神经网络是未来的一个关键目标.
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