The study by Beggs and Plenz investigates the spontaneous activity in neocortical circuits, focusing on the emergence of "neuronal avalanches." They used organotypic cultures and acute slices of rat cortex to record spontaneous local field potentials (LFPs) using a 60-channel multielectrode array. The results show that the propagation of LFPs follows a power law with an exponent of −3/2, indicating a critical branching process. This critical state is characterized by a branching parameter close to 1, which optimizes information transmission in feedforward networks while preventing runaway excitation. The findings suggest that neuronal avalanches are a generic property of cortical networks, differing from oscillatory, synchronized, or wave-like states. The critical state allows the network to balance information transmission and stability, potentially maximizing the efficiency of information processing. The study also explores the implications of this critical state on information transmission in artificial neural networks, showing that the critical branching parameter of σ = 1 maximizes information transmission.The study by Beggs and Plenz investigates the spontaneous activity in neocortical circuits, focusing on the emergence of "neuronal avalanches." They used organotypic cultures and acute slices of rat cortex to record spontaneous local field potentials (LFPs) using a 60-channel multielectrode array. The results show that the propagation of LFPs follows a power law with an exponent of −3/2, indicating a critical branching process. This critical state is characterized by a branching parameter close to 1, which optimizes information transmission in feedforward networks while preventing runaway excitation. The findings suggest that neuronal avalanches are a generic property of cortical networks, differing from oscillatory, synchronized, or wave-like states. The critical state allows the network to balance information transmission and stability, potentially maximizing the efficiency of information processing. The study also explores the implications of this critical state on information transmission in artificial neural networks, showing that the critical branching parameter of σ = 1 maximizes information transmission.