随机聚类网络的内在动态产生了位置场和新环境的预测
Jordan Breffle1, Hannah Germaine1, Justin D Shin1,2,3
1Neuroscience Program, Brandeis University, 415 South St., Waltham, MA 02454.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
具有集群连接性的神经回路可以自发地为过去 (重播) 和未来 (预播) 空间轨迹生成序列. 这表明内部神经活动模式与新体验有关.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 认知神经科学 认知神经科学
背景情况:
- 海马的位置细胞表现出经历过的轨迹的重复和未来轨迹的预演.
- 在神经电路中产生这些空间序列的机制仍然不完全理解.
研究的目的:
- 模拟自发的神经活动模式如何产生重播和预播.
- 调查网络连接在形成空间表示和序列中的作用.
主要方法:
- 开发了一个具有集群连接性的神经电路的计算模型.
- 模拟自发的神经活动和分析序列生成.
- 在计算和实验数据中应用场地,场地和预测分析.
主要成果:
- 集群连接,而不是预先配置的地图,可以生成现实的位置字段和空间序列.
- 在小世界网络中,预播质量优化,具有特定的集群和路径长度属性.
- 模型的发现使用现有的大鼠海马位细胞数据进行了验证.
结论:
- 集群网络中的自发神经活动可以成为重播和预播现象的基础.
- 网络拓,特别是小世界属性,对于有效的预游戏至关重要.
- 这支持了一个框架,新奇的经验与现有的可适应的神经活动模式联系在一起.
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