McNamee Daniel C, Stachenfeld Kimberly L, Botvinick Matthew M, Gershman Samuel J
Neuroscience Programme, Champalimaud Research, 1400-038 Lisbon, Portugal.
Google DeepMind, London N1C 4DN, UK.
Entropy (Basel). 2022 Dec 8;24(12):1791. doi: 10.3390/e24121791.
Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas such as the prefrontal cortex, indicating that it may be a general principle of neural functionality in the brain. In a recent analysis through the lens of dynamical systems theory, we showed how grid coding can lead to the generation of a diversity of empirically observed sequential reactivations of hippocampal place cells corresponding to traversals of cognitive maps. Here, we extend this sequence generation model by describing how the synthesis of multiple dynamical systems can support compositional cognitive computations. To empirically validate the model, we simulate two experiments demonstrating compositionality in space or in time during sequence generation. Finally, we describe several neural network architectures supporting various types of compositionality based on grid coding and highlight connections to recent work in machine learning leveraging analogous techniques.
内嗅皮层中的神经元表现出多个周期性组织的放电场，这些放电场共同形成了空间的内部表征。神经成像数据表明，这种网格编码也存在于其他皮层区域，如前额叶皮层，这表明它可能是大脑神经功能的一个普遍原则。在最近通过动力系统理论进行的分析中，我们展示了网格编码如何导致产生与认知地图遍历相对应的多种经验观察到的海马位置细胞序列重激活。在这里，我们通过描述多个动力系统的合成如何支持组合认知计算来扩展这个序列生成模型。为了通过实验验证该模型，我们模拟了两个实验，展示了序列生成过程中空间或时间上的组合性。最后，我们描述了几种基于网格编码支持各种组合性类型的神经网络架构，并强调了与利用类似技术的机器学习最新工作的联系。
