量化存储在突触连接中的信息,而不是神经网络的触发模式
bioRxiv : the preprint server for biology
|December 9, 2024
概括
这项研究引入了一个新的框架来量化神经网络连接中存储的信息. 它揭示了突触之间协同作用的相互作用,表明它们的综合信息存储超过了单个贡献.
科学领域:
- 计算神经科学是一种计算神经科学.
- 信息理论是信息理论.
- 机器学习是机器学习.
背景情况:
- 神经网络以突触连接强度存储信息.
- 在神经发射模式中确定了量化信息,但不是对于突触连接.
- 关联神经网络对于学习和记忆至关重要.
研究的目的:
- 开发一个理论框架来量化由突触连接编码的信息.
- 在连续的霍普菲尔德网络中分析信息存储.
- 研究突触之间协同作用的相互作用.
主要方法:
- 使用连续的霍普菲尔德网络开发了一个理论框架.
- 在多变量日志正常分布的混合后应用的数据.
- 分析性地获得的Shannon相互信息,用于n-tuples的突触连接.
主要成果:
- 证实了关于存储容量的现有见解和神经发射模式中的分布式编码.
- 发现了突触之间协同作用的相互作用.
- 证明了通过突触编码的共同信息超过了个人贡献的总和.
结论:
- 引入了一个可解释的框架来量化神经网络中的信息存储.
- 突出了突触连接和神经人口活动在学习和记忆中的双重作用.
- 提供了对神经系统信息处理的定量理解.
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