Petrantonakis Panagiotis C, Poirazi Panayiota
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece.
PLoS One. 2015 Jan 30;10(1):e0117023. doi: 10.1371/journal.pone.0117023. eCollection 2015.
Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2-4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm "Iterative Soft Thresholding" (IST) by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG.
齿状回（DG）中与记忆相关的活动具有稀疏性特征。记忆表征被视为颗粒细胞的激活神经元群体，颗粒细胞是DG中的主要编码细胞，估计占细胞总数的2 - 4%。这种稀疏性被认为增强了DG执行模式分离的能力，这是DG在记忆形成过程中最有价值的贡献之一。在这项工作中，我们研究了DG的兴奋性和抑制性连接图等特征如何用于开发执行稀疏逼近的理论算法，稀疏逼近是信号处理领域广泛使用的一种策略。稀疏逼近代表从一个字典中算法识别出少数几个近似某个信号的分量。这里利用DG通过使其表征稀疏化来实现模式分离的能力，通过添加受DG电路启发的新算法特征，来提高当前最先进的稀疏逼近算法“迭代软阈值法”（IST）的性能。颗粒细胞通过苔藓细胞进行的直接或间接侧向抑制，被证明能提高IST的性能。除了揭示受DG启发的理论算法的潜力外，这项工作还提供了关于DG模式分离任务中特定细胞类型功能的新见解。
