ABSTRACTGrid cells in the medial entorhinal cortex manifest multiple firing fields, patterned to tessellate external space with triangles. Although two-dimensional continuous attractor network (CAN) models have offered remarkable insights about grid-patterned activity generation, their functional stability in the presence of biological heterogeneities remains unexplored. In this study, we systematically incorporated three distinct forms of intrinsic and synaptic heterogeneities into a rate-based CAN model driven by virtual trajectories, developed here to mimic animal traversals and improve computational efficiency. We found that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in neural activity. Quantitatively, grid score and spatial information associated with neural activity reduced progressively with increasing degree of heterogeneities, and perturbations were primarily confined to low-frequency neural activity. We postulated that suppressing low-frequency perturbations could ameliorate the disruptive impact of heterogeneities on grid-patterned activity. To test this, we formulated a strategy to introduce intrinsic neuronal resonance, a physiological mechanism to suppress low-frequency activity, in our rate-based neuronal model by incorporating filters that mimicked resonating conductances. We confirmed the emergence of grid-patterned activity in homogeneous CAN models built with resonating neurons and assessed the impact of heterogeneities on these models. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing, through suppression of low-frequency components in neural activity. Our analyses suggest a universal role for intrinsic neuronal resonance, an established mechanism in biological neurons to suppress low-frequency neural activity, in stabilizing heterogeneous network physiology.SIGNIFICANCE STATEMENTA central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. However, several theoretical and modeling frameworks employ unnatural homogeneous networks in assessing network function owing to the enormous analytical or computational costs involved in assessing heterogeneous networks. Here, we investigate the impact of biological heterogeneities on a powerful two-dimensional continuous attractor network implicated in the emergence of patterned neural activity. We show that network function is disrupted by biological heterogeneities, but is stabilized by intrinsic neuronal resonance, a physiological mechanism that suppresses low-frequency perturbations. As low-frequency perturbations are pervasive across biological systems, mechanisms that suppress low-frequency components could form a generalized route to stabilize heterogeneous biological networks.
Resonating neurons stabilize heterogeneous grid-cell networks
