Oct 2026· International Journal of Neural Systems· 0 citations
TL;DR
Connectome- and morphology-constrained framework is introduced that embeds electron-microscopy–reconstructed cortical microcircuits in near-critical propagation dynamics and generates field potentials under fixed synaptic, membrane, and forward-modeling parameters, defining the level-specific interpretability of spectral slope.
Abstract
Aperiodic ([Formula: see text]) spectral components are widely interpreted as markers of excitation–inhibition (E/I) balance, but this interpretation spans levels from synaptic currents and microcircuit spiking to population field potentials. Whether macroscopic spectral slope can index microcircuit-level E/I input independently of circuit architecture remains unresolved.
This study introduces a connectome- and morphology-constrained framework that embeds electron-microscopy–reconstructed cortical microcircuits in near-critical propagation dynamics and generates field potentials under fixed synaptic, membrane, and forward-modeling parameters. By linking controlled synaptic E/I input, microcircuit spiking propagation, and macroscopic spectra, the framework directly tests the explanatory level of the aperiodic exponent. Across matched E/I regimes, the direction and magnitude of the E/I input–[Formula: see text] relationship differed between biological and topology-randomized networks, yielding a significant E/I input × topology interaction.
To test whether this architecture-dependent mapping extends to biological systems, paired cortical ECoG and thalamic EThG recordings from juvenile pigs were analyzed. Under the same systemic propofol perturbation, exponent changes were stronger in thalamus than cortex and remained so after accounting for global state shifts, baseline spectra, and distance from criticality.
These findings identify circuit architecture as a determinant of how microcircuit E/I input is transformed into macroscopic aperiodic spectra, defining the level-specific interpretability of spectral slope.
Neuronal excitability is commonly modeled using different principles at the cellular and population scales, making it difficult to study how microscopic mechanisms shape macroscopic activity. We introduce a spatiotemporal excitatory-inhibitory (EI) model that unifies these scales through a common mixed-feedback archite...
To understand neural computations, one would like to relate circuit dynamics to neuronal intrinsic excitability and connectivity. We developed tools for large-scale all-optical electrophysiology and applied them to approximately 300 layer 1 interneurons per mouse in vivo. Across two mice, targeted stimulation and volta...
Yitong Ǫi, Vicente J. Parot, Xiang Wu et al.· bioRxiv· 0 citations
Highlights What are the main findings? The spectral slope of input noise remained decodable from population activity. Decoding was highest in the stimulated layer-4 (L4) and second-highest in L2/3, and improved with noise contrast. What are the implications of the main findings? Population statistics remain informative...
Mario Treviño, Oscar Arias-Carrión· Brain Science· 0 citations
The results support interpreting parameter dependence through effective drive, coupling, noise, and collective timing rather than nominal parameters alone.
Biophysically detailed models of human cortical microcircuit provide key insights into brain function and disease biomarkers, yet their computational cost lengthens simulation runtime and limits scalability. To address this, we systematically evaluated recent simplification methods for morphological reduction and synap...
A key function of the human brain is its ability to dynamically adapt to novel contexts by integrating prior experience. While neural adaptation is widely observed across cortical systems, the microcircuit-level mechanisms governing its intensity and temporal dynamics remain unclear. To bridge this gap, we develop a re...
Diletta Bartolini, T. P. Reber, T. Tchumatchenko et al.· bioRxiv· 0 citations
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