Um circuito neural canônico com inibição feedback e feedforward

Abstract

Gamma oscillation is present in several areas of the brain, such as the hippocampus, playing an important mechanism for memory functioning. We found several models capable of explaining the generation of the gamma oscillations and explain their two functionalities, that of synchronously grouping the synapses of the neurons and of selecting which neurons must trigger in each cycle of this synchronism. These functionalities impart a computational character of neural processing to this system, such as the separation of patterns and the formation of neural assemblies. However, the analysis of these existent models shows to be very sensitive to the variations of the cerebral activities, being strongly affected by variations and their layers of entrance, in order to appear not to have a good robustness, generating much variation of their frequency of exit, as in between these neurons. However, when considering an important part of the biological circuit not considered in previous studies, a fed-in inhibition network enabled us to create a new model. Based on the Izhikevich neuron model, we generated a new model with greater robustness to the variations in the input layer, as well as a reduced computational cost and proximity of the biological model. In the possession of this new model, it will be possible to create neural networks with greater capacity of neurons, with reduced computational cost, besides the possibility of analyzing the individual behavior in each neuron of the model.