Neural oscillation in low-rank SNNs: bridging network dynamics and cognitive function

TL;DR

This paper explores how gamma neural oscillations influence cognitive functions using a low-rank spiking neural network model.

Contribution

The study introduces a biologically plausible low-rank SNN model that captures gamma oscillations and their role in cognitive tasks.

Findings

The model reproduces phase-dependent response modulation in a Go-Nogo task, matching in vivo observations.

Gamma oscillations are shown to enhance and prolong signal responses in the network.

The model extends low-rank connectivity to population-level synchronous activity while adhering to biological constraints.

Abstract

Neural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity, suggesting a direct link between oscillatory dynamics and cognition. However, there remains a lack of computational models that can systematically simulate and investigate this effect. To address this, we construct a low-rank spiking neural network (low-rank SNN) based on the voltage-dependent theta model to explore how structured connectivity shapes oscillatory dynamics and cognitive function. Using macroscopic model analysis, we identify different network states, ranging from stationary firing to gamma oscillation. Our model successfully reproduces phase-dependent response modulation in a Go-Nogo task, consistent with in vivo findings, providing an explanation for how neural oscillation influences task performance. Besides phase dependency, our findings suggest that gamma oscillation can enhance and prolong signal response. Compared to prior studies that applied low-rank connectivity to SNNs but remained limited to stationary or weak oscillatory regimes, our work extends to population-level synchronous activity while maintaining biological plausibility under Dale's principle. Our study offers a theoretical framework for understanding how neural oscillations emerge in structured spiking networks and provides a foundation for future experimental and computational investigations into oscillatory modulation of cognition.

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