Neuronal computation underlying the generation of a transitive inference in mice

小鼠传递推理生成的神经元计算

• 批准号: 2397874
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2397874
• 关键词: Neuronal; computation; underlying; generation; transitive

Inferential reasoning entails the ability to form flexible relationships between items or events that were never directly experienced together. Humans and animals can draw on previous experience and combine information from multiple discrete, loosely related items or events into a 'cognitive map'. This internal model of the world generalises the structure of problems onto a common framework of spatial or abstract relational knowledge. We aim to understand the mechanism underlying the generalisation of information enabling adaptive behaviour across related stimuli. We designed a transitive inference task with two parallel groups of odour triplets, while simultaneously recording spiking activity and local field potential (LFP) in multiple hippocampal and cortical subregions implicated in this task using high-density silicon probes. We ask whether the hippocampus (HPC) conveys a conjunctive code, continuously mapping the current specific sensory stimuli coded by lateral entorhinal cortex (LEC) onto the general abstract structure of the task coded by medial entorhinal cortex (MEC). First, we aim to identify neurons selectively active upon presentation of each specific odour. We will examine the temporal relationship between firing patterns of neurons representing different odours to determine whether the sequentiality of odour presentation is maintained in the neural representation. We will also assess the temporal relationship across brain areas participating in the task to determine the directionality of information flow during both the formation and retrieval of the inference. Finally, we will focus on brain oscillations at different frequencies during active and inactive behavioural states to determine their importance in synchronizing activity between representations for individual odours and between brain areas, potentially nesting cognitive short-cuts giving rise to flexible inferences.

推理需要在从未直接经历过的项目或事件之间形成灵活关系的能力。人类和动物可以利用先前的经验，将来自多个离散的、松散相关的项目或事件的信息联合收割机组合成“认知地图”。这个世界的内部模型将问题的结构概括为空间或抽象关系知识的公共框架。我们的目标是了解信息泛化的机制，使相关刺激的适应性行为。我们设计了一个传递性推理任务与两个平行组的气味三联体，同时记录尖峰活动和局部场电位（LFP）在多个海马和皮质亚区牵连在这项任务中使用高密度硅探针。我们问是否海马（HPC）传达一个连接代码，不断映射当前特定的感官刺激编码的外侧内嗅皮层（LEC）到一般的抽象结构的任务编码的内侧内嗅皮层（MEC）。首先，我们的目标是确定神经元选择性地活跃在每一个特定的气味。我们将研究代表不同气味的神经元的放电模式之间的时间关系，以确定气味呈现的顺序性是否保持在神经表征中。我们还将评估参与任务的大脑区域之间的时间关系，以确定推理形成和检索过程中信息流的方向性。最后，我们将专注于在活跃和不活跃的行为状态下不同频率的脑振荡，以确定它们在同步活动中的重要性，这些活动在各个气味的表征之间以及大脑区域之间，可能嵌套认知捷径，从而产生灵活的推断。