Dumont Nicole Sandra-Yaffa, Stöckel Andreas, Furlong P Michael, Bartlett Madeleine, Eliasmith Chris, Stewart Terrence C
Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Applied Brain Research Inc., Waterloo, ON N2T 1G9, Canada.
Brain Sci. 2023 Jan 31;13(2):245. doi: 10.3390/brainsci13020245.
The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together these ongoing research strands, presenting them in a common framework. We expand on the NEF's core principles of (a) specifying the desired tuning curves of neurons in different parts of the model, (b) defining the computational relationships between the values represented by the neurons in different parts of the model, and (c) finding the synaptic connection weights that will cause those computations and tuning curves. In particular, we show how to extend this to include complex spatiotemporal tuning curves, and then apply this approach to produce functional computational models of grid cells, time cells, path integration, sparse representations, probabilistic representations, and symbolic representations in the brain.
神经工程框架（伊莱亚斯密斯和安德森，2003年）是一种长期存在的方法，用于实现受低级神经生物学细节约束的高级算法。近年来，该方法已得到扩展，纳入了更多生物学细节，并应用于新的任务。本文将这些正在进行的研究线索汇集在一起，在一个通用框架中呈现它们。我们详细阐述了神经工程框架的核心原则：（a）指定模型不同部分中神经元的期望调谐曲线；（b）定义模型不同部分中神经元所代表的值之间的计算关系；（c）找到将导致这些计算和调谐曲线的突触连接权重。特别是，我们展示了如何将其扩展到包括复杂的时空调谐曲线，然后应用这种方法来生成大脑中网格细胞、时间细胞、路径积分、稀疏表示、概率表示和符号表示的功能性计算模型。
