认知与发展心理学研究室知识库

数量表征是大脑完成各种数学任务的基础，也是大脑完成其它相关认知过程的重要环节。Nieder及其合作者(2002; 2003; 2008; 2009)发现数量选择性神经元在数量表征功能中起重要作用，并且指出，这些具有数量选择性的神经元既可能是兴奋性神经元，也可能是抑制性神经元。

目前己有计算模型只考察了兴奋性神经元在模拟数量表征中的作用，尚没有对抑制性神经元的相关模拟研究。但无可否认的是，抑制性神经元是脑神经系统的一个重要组成部分。并且Diester和Nieder (2008)发现，抑制性神经元参与数量表征过程。因此我们提出抑制性神经元在数量表征中有重要作用，可以帮助兴奋性神经元修正并完善其数量选择性功能。

本文拟构建基于神经生理学的最小完备的神经计一算模型，来模拟抑制性神经元在数量表征认知过程中的作用，探一讨其神经机制，并提供可检验的预测。我们认为，抑制性前馈输入形成的冗余信息滤除能力很可能是抑制性神经元影响兴奋性神经元在表征过程中数量选择性功能的关键因素。

Numerosity representation is the basis of mathematical cognition and many other cognitive tasks in brains. 1Viedcr ct al. (2002； 2003; 2008；2009) found that numerosity-selective neurons may play an important role in numerosity representation, and considered these neurons as either excitatory or inhibitory.

Previous models only used excitatory neurons and did not attend inhibitory neurons. Flowever, the latter are important part in cerebral neural system, and have been found that they related in numerosity representation (Diestcter & Nieder, 2000. Therefore, we suggested that inhibitory neurons should make significant contribution on process of numerosity cognition，and could help excitatory neurons to modify and perfect their numerosity-selectivity.

We will build a neural computation model based on psychology and neurophysiology, to simulate the role of inhibitory neurons in process of numerosity representation and learning and search their neural mechanisms, We infer that the ability of filtering redundant information by inhibitory forward input may be the reason with which inhibitory neurons could influence selectivity of excitatory neurons in numerosity representation.