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Synaptic delays modulate population phase and amplitude responses in oscillatory excitatory-inhibitory networks

Aug 2026 · 0 citations · 62 references
Biology

TL;DR

These findings identify synaptic delay as a key parameter governing the balance between phase resetting and amplitude modulation and provide a computational framework for understanding delay-dependent control of oscillatory brain networks.

Abstract

Synaptic delays are fundamental determinants of neuronal communication and can profoundly influence the emergence and stability of cortical oscillations. Although their role in shaping network synchronization is well established, how synaptic delays regulate the collective response of neuronal populations to transient perturbations remains poorly understood. Here, we investigate the effects of synaptic delays on the phase and amplitude responses of oscillatory activity in a conductance-based excitatory-inhibitory spiking network operating in the pyramidal-interneuron gamma (PING) regime. By systematically varying the synaptic delay and applying brief external perturbations to the excitatory population, inhibitory population, or the entire network, we computed network phase response curves (nPRCs) and network amplitude response curves (nARCs) to quantify changes in oscillation timing and population coherence. Increasing synaptic delay slowed network oscillations while enhancing population synchrony, demonstrating a trade-off between oscillation frequency and coherence. Excitatory perturbations produced relatively robust phase responses across delays but exhibited a pronounced delay-dependent reduction in amplitude enhancement. In contrast, inhibitory perturbations generated substantially stronger delay-dependent modulation of both phase resetting and amplitude suppression, whereas whole-network stimulation combined features of both excitatory and inhibitory responses. Taken toghether, these findings identify synaptic delay as a key parameter governing the balance between phase resetting and amplitude modulation and provide a computational framework for understanding delay-dependent control of oscillatory brain networks.

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