Evolutionary neuroeconomic adaptations of fast-spiking neurons in the human neocortex

Homo sapiens has evolved a large and complex neocortex that underlies advanced cognitive capabilities. Neural computation, however, is inherently energy-intensive, and evolutionary pressures have shaped mechanisms that optimize both computational performance and energy efficiency in the human brain. Fast-spiking interneurons, particularly basket cells, are among the most active neuron types in the neocortex, where they play a key role in coordinating time and space in the activity of neuronal networks, but their high activity levels require high metabolic resources. Because the human neocortex is significantly larger than that of rodents—and contains a higher proportion of inhibitory interneurons relative to pyramidal cells—this expansion may have created evolutionary pressure to reduce the energetic cost of fast-spiking neurons. Compared with rodents, human fast-spiking neurons exhibit adaptations that appear to lower energy expenditure while preserving rapid and precise inhibition. One such adaptation is increased input resistance, which allows both excitation and inhibition to occur with reduced transmembrane ion currents, thereby decreasing the energy required to maintain ionic gradients across the plasma membrane. Since higher input resistance also slows down membrane potential changes, these cells show secondary adaptations that maintain rapid electrical signaling. Additional modifications—such as optimized ion channel composition in soma and axon initial segment, enhanced axon myelination, simplified structure of dendritic tree, and multivesicular synapses—further improve electrical signaling and are likely to reduce metabolic demand, collectively reducing ATP consumption in the neuronal network. By integrating cellular and synaptic perspectives, this review highlights how fast-spiking neurons in the human neocortex have evolved differently from those in rodents to balance energy efficiency while maintaining computational power, providing insight into the metabolic constraints of the human brain.