I’m currently studying a neuroscience based model with synchronized gamma cycles:
Input, place, and grid cells are modeled as rate-based neurons. The set of firing rates (i.e., the ensemble activity) is computed iteratively. Each iteration cycle t is assumed to correspond to one gamma cycle; the assumption of a time resolution of one gamma period (10–25 ms) was motivated by the use of a competitive 10%-max winner-take-all mechanism to select which cells fire (see below), which was postulated to occur within a gamma cycle (de Almeida et al., 2009b). A theta cycle is defined as a sequence of seven gamma cycles to reflect the ratio between theta and gamma frequencies (∼8 Hz and ∼40–100 Hz, respectively); however, using a lower number of gamma cycles per theta leads to similar results because network convergence occurs within two to four gamma cycles (see Fig. 3). The ensemble neural activity computed in a gamma cycle is used as input in the computation of the ensemble activity in the next gamma cycle. In brief, the ensemble activity of input cells is defined based on the current position and context; the ensemble activity of grid and place cells is computed applying a population-wide competition over the integrated input (de Almeida et al., 2009b).