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This paper introduces a unified spatiotemporal excitatory-inhibitory (EI) model that bridges cellular and population scales by employing a mixed-feedback architecture with memductances encoding voltage-dependent dynamics. The model demonstrates how fast excitatory and slow inhibitory feedback, coupled with short-range excitation and longer-range inhibition, generates population excitability and localized population spikes even in heterogeneous neuronal populations. Crucially, both intrinsic temporal excitability and spatial synaptic feedback are shown to be essential for coherent mean-field events and tunable population rhythms, highlighting the influence of cellular mechanisms on macroscopic dynamics.
A single EI model framework unifies neuronal excitability across cellular and population scales, revealing how microscopic feedback mechanisms generate emergent macroscopic rhythms.
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 architecture. The model retains the conductance-based circuit structure of Hodgkin-Huxley models and describes intrinsic and synaptic currents through memductances whose temporal and spatial voltage dependence encodes dynamics at the relevant scale. In its minimal form, temporal excitability arises from fast excitatory feedback opposed by slower inhibitory feedback, while spatial excitability arises from short-range excitation opposed by longer-range inhibition, recovering the core lateral-inhibition motif of Amari neural fields. We show that combining these mechanisms gives rise to population excitability in strongly heterogeneous populations. In particular, the model generates population spikes that are localized in both space and time and possess population-level scales distinct from those of individual neuronal spikes, despite substantial cell-to-cell parameter variability. We then show that both intrinsic temporal excitability and spatial synaptic feedback are required for coherent mean-field events and robust spatiotemporal population rhythms. Finally, selective modulation of memductances in a subset of the population tunes the frequency of the population rhythm, illustrating how changes at the cellular scale can shape macroscopic dynamics. By separating circuit architecture from modeling scale, the proposed framework provides a physically interpretable approach to analyzing and designing neuronal excitability across scales.