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Macroscopic Dynamics of Neural Networks with Heterogeneous Spiking Thresholds
The Izhikevich single neuron model can account for a broad range of different neuron types and spiking patterns, thus rendering it an optimal candidate for a mean-field theoretic treatment of brain dynamics in heterogeneous networks.
Effects of Neural Heterogeneity on Spiking Neural Network Dynamics
The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics.
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.
