Longer inhibitory connections can push simulated neural networks beyond critical tipping point
In neuroscience, the brain criticality hypothesis proposes that neurons in the brain are arranged in such a way that neural activity lies at or near a transition point between two extremes: an ordered state where neural activity dies out quickly and a disordered state where neural activity tends to spread quickly and potentially grow uncontrollably. This transition point is known as criticality,…
A research team led by Zenas C. Chao from the International Research Center for Neurointelligence at The University of Tokyo investigated how the physical arrangement of inhibitory connections impacts the criticality of simulated neural networks. Criticality refers to a balance between order and disorder in neural activity, where small bursts can either remain localized or spread widely, akin to avalanches of sand.
Previous studies have shown that factors like network structure, synaptic plasticity, and homeostatic mechanisms influence criticality. However, the spatial distribution of inhibitory connections and their delays have not been extensively studied. Chao and colleagues developed a computational model with 80 excitatory and 20 inhibitory neurons on a two-dimensional disk.
They varied the distance and probability of inhibitory connections across different conditions. The simulations revealed that longer inhibitory connections led to supercritical activity more frequently, while shorter-range connections tended towards subcriticality. This is because the delays caused by longer-range connections weaken inhibition, allowing larger neural avalanches and pushing the network towards a supercritical state.
The findings suggest that considering both the number and arrangement of inhibitory connections, along with transmission delays, is crucial for understanding neural network dynamics. This could aid in designing more stable and adaptable artificial neural systems and understanding how genetic or developmental factors affecting inhibitory connectivity influence brain function.
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