Dentate Gyrus circuitry features improve performance of sparse approximation algorithms.

ABSTRACT: 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

SUBMITTER: Petrantonakis PC 

PROVIDER: S-EPMC4312091 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature