Datensatz

Zusammenfassung

Spontaneous cortical activity unfolds across different spatial scales. On a local scale of individual columns, activity spontaneously transitions between episodes of vigorous (On) and faint (Off) spiking, synchronously across cortical layers. On a wider spatial scale of interacting columns, activity propagates as neural avalanches, with sizes distributed as an approximate power-law with exponential cutoff, suggesting that brain operates close to a critical point. We investigate how local On-Off dynamics can coexist with critical avalanches. To this end, we developed a branching network model capable of capturing both of these dynamics. Each unit in the model represents a cortical column, that spontaneously transitions between On and Off episodes and has spatially structured connections to other columns. We found that models with local connectivity do not exhibit critical dynamics in the limit of a large system size. While for a critical network, it is expected that the cut-off of the avalanche-size distribution scales with the system size, in models with nearest-neighbor connectivity, it stays constant above a characteristic size. We demonstrate that the scaling can be recovered by increasing the radius of connections or by rewiring a small fraction of local connections to long-range random connections. Our results highlight the possible role of long-range connections in defining the operating regime of the brain dynamics.