The Brain’s Balancing Act: How Excitatory and Inhibitory Neurons Enable Complex Learning
A new computational study investigates how the brain’s fundamental excitatory-inhibitory (E-I) balance may underpin a powerful learning mechanism. Researchers applied “force learning”—a method for training recurrent neural networks to generate complex dynamics—to a biologically inspired E-I network model of the cerebral cortex. They found that the network’s ability to learn and produce specific output signals, such as periodic patterns, was not uniform but peaked at a specific, optimal balance between excitation and inhibition. This optimal point exists near a dynamical regime known as the “edge of chaos,” where the network exhibits rich, transiently synchronized activity.
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