Network dynamics contribute to a gamma rhythm highly robust to synaptic variation
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KeywordsInhibitory Neuron Gamma Oscillation Synaptic Conductance Dynamic Compensation Regular Spike
Dynamic homeostatic compensation in neural networks has recently attracted study. The gamma rhythm is often the focus of computational studies, as it is a local phenomenon and clinically significant: its disruption has been linked with mental illnesses such as schizophrenia and epilepsy. However little is known about dynamic compensation in relation to gamma rhythms. In (Reimers et al, in preparation) we observed that in all human brain genomics data sets, there is surprisingly high variability in mRNA levels for the key components of the GABA-A receptors, which are the mediators of fast inhibition. Further work showed that the variation was not compensated by substitution of different components of the same class of GABA-A receptor, suggesting at least a 20-fold variation in inhibitory signaling strength between individuals. Nevertheless most people seem to produce comparable gamma rhythms even with very different genetics.
We find that the modified model is much more robust to variation in synaptic conductance, even on the scale suggested by transcriptomic variation (Figure 1A, B). Additionally, we find that each of our significant modifications increases the close tracking of inhibition to excitation during the gamma oscillation, in which we see a high correlation in our modified version (Figure 1D, F). This tracking seems to be the core of the robustness and has recently been shown to be an important modulator of the gamma rhythm . The robustness of the modified model stems from network interaction rather than compensating parameters.
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