ANTONIO DE CANDIA

Modularity-dependent storage of dynamic spiking patterns: bridging micro- and mesoscopic representations

The paper studies how modular spiking neural networks store and retrieve dynamic spatiotemporal patterns by adjusting temporal overlap between modules.

Symmetry breaking and avalanche shapes in modular neural networks

The paper studies a modular neural network model to show how symmetry breaking leads to different avalanche shapes and critical behavior in brain dynamics.

Inferring global exponents in subsampled neural systems

This study shows that certain critical exponents in neural systems remain robust under subsampling, allowing for unbiased estimation of exponents from partial observations.

Power spectrum and critical exponents in the 2D stochastic Wilson Cowan model

The paper analyzes a 2D stochastic Wilson Cowan model and finds different critical exponents compared to mean field models.

Critical behaviour of the stochastic Wilson-Cowan model

The paper analyzes the stochastic Wilson-Cowan model to show it exhibits critical behavior with scale-free avalanche dynamics and diverging relaxation times.