神经网络中的共享输入和反复性,以实现代谢效率高的信息传输
Tomas Barta1,2, Lubomir Kostal1
1Laboratory of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
PLoS computational biology
|February 23, 2024
概括
具有抑制反的神经网络可以有效地传输更多信息. 最佳的突触强度平衡了信息获取与代谢成本的平衡,特别是在相关输入的情况下.
科学领域:
- 计算神经科学是一种神经科学.
- 神经编码 神经编码
- 神经网络的神经网络的神经网络
背景情况:
- 对神经元群体的共享输入会产生噪声相关性,减少信息传输.
- 经常性神经网络中的抑制反可以降低噪声相关性,增强信息传输.
- 抑制性神经元活动会产生代谢成本,可能会抵消减少噪音带来的好处.
研究的目的:
- 调查抑制反的信息传输好处是否超过其代谢成本.
- 确定最佳的网络参数,以最大限度地提高每代谢成本的信息传输.
- 分析输入相关性在具有抑制反的循环网络效率中的作用.
主要方法:
- 分析一个由泄漏的整合和发射神经元组成的循环神经网络模型.
- 在代谢成本限制下最大限度地提高相互信息的数学框架.
- 经常性网络与抑制反网络相比,反网络的比较.
主要成果:
- 存在一个最佳的反复连接强度,最大限度地提高了每单位成本的相互信息.
- 代谢效率高的编码参数可以预测神经网络中最佳的突触强度.
- 对于高输入相关性,具有抑制反的循环网络与前网络相比,提供更优质的信息成本.
结论:
- 循环神经网络中的抑制反可以通过增强的信息传输来补偿增加的成本,从而在代谢上提高效率.
- 抑制反的脱关系效应证明了其代谢费用,特别是在高输入相关性条件下.
- 为了高效的神经计算,最佳的网络设计需要平衡信息容量和能源支出.
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