A Kernel-Spectral Index for Single-Neuron Input-Output Complexity
The computational capacity of individual neurons in the brain is a central problem in computational neuroscience and an increasingly relevant link between neuroscience and AI. Recent work fitting artificial neural networks to detailed cortical pyramidal neurons showed that reproducing their millisecond-precision input-output behavior can require temporal convolutional networks with five to eight…
A novel metric, the Spectral Complexity Index (SCI), has been developed to quantify the computational capacity of individual neurons in the brain. This measure provides an interpretable spectral representation of presynaptic histories, addressing the current challenges in single-neuron complexity studies that rely on surrogate-based methods and demanding significant computational resources.
The SCI was applied to 35 biophysically detailed human and mouse cortical neuron models and demonstrated a statistically significant distinction between the two species. Moreover, the SCI showed strong agreement with the Functional Complexity Index (FCI) based on deep neural networks (Spearman correlation: ρ=0.797). The SCI also proved to be computationally efficient, requiring approximately one-tenth or less of the resources compared to existing methods.
By transforming single-neuron complexity from a black-box surrogate property into an interpretable spectral object, the SCI enables reproducible, rapid, and efficient comparisons of neuronal computation across cells and species.
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