# Modelling Gamma Synchrony

I’ve always been curious as to why gamma synchrony is not modeled in neural networks. Is there a reason for this? From what I understand; GS increases learning speed, has a heavy correlative relationship to consciousness, and - as the name implies - is key in synchronizing major functions across the brain. Is it that the very instance of modelling a neuron with software inherently includes a timing mechanism via chip clock? Or is it that we’re just not at a level of understanding as to what GS actually does and/or how it works and thereby not really sure of how it would be modeled?

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I don’t agree that Gamma cycles are not used.
Various models do use the faster cycles for various purposes.
HTM focuses on the predictive cells and has no need for the faster cycles.
The Deepleabra model uses (and depends on) splitting the Alpha into smaller time units (plus and minus phases)
I personally want to use the layer 2/3 cells to do hex-grid forming and need the Gamma cycles to do competition to form a grid within the overall Alpha cycle timing. This is also the layer that does the map-2-map communication so it could be the usage you are looking for.

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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).

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