Resonating neurons stabilize heterogeneous grid-cell networks.

Divyansh Mittal,Rishikesh Narayanan

doi:10.7554/elife.66804

Abstract

A central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. Here, we investigated the impact of distinct forms of biological heterogeneities on the stability of a two-dimensional continuous attractor network (CAN) implicated in grid-patterned activity generation. We show that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that targeted suppression of low-frequency perturbations could ameliorate heterogeneity-induced disruptions of grid-patterned activity. To test this, we introduced intrinsic resonance, a physiological mechanism to suppress low-frequency activity, either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We found CAN models with mechanistic resonators to be more effective in targeted suppression of low-frequency activity, with the slow kinetics of the negative feedback loop essential in stabilizing these networks. As low-frequency perturbations (1/f noise) are pervasive across biological systems, our analyses suggest a universal role for mechanisms that suppress low-frequency activity in stabilizing heterogeneous biological networks.

Highlights

46 Stability of network function, defined as the network’s ability to elicit robust functional outcomes despite perturbations to or widespread variability in its constitutive components, is a central theme that governs the functional design of several biological networks
We quantitatively address questions on the 66 impact of distinct forms of biological heterogeneities on the functional stability of a two67 dimensional continuous attractor network (CAN), which has been implicated in the generation of patterned neuronal activity in grid cells of the medial entorhinal cortex (Burak and Fiete, 2009; Knierim and Zhang, 2012; Couey et al, 2013; Domnisoru et al, 2013; Schmidt-Hieber and Hausser, 2013; Yoon et al, 2013; Tukker et al, 2021)
We hypothesized that intrinsic neuronal resonance could stabilize the heterogeneous grid-cell network through suppression of low-frequency perturbations. To test this hypothesis, we developed two distinct strategies to introduce intrinsic resonance in our rate-based neuronal model to mimic the function of resonating conductances in biological neurons: (i) a phenomenological approach where an additional tunable high-pass filter was incorporated into single-neuron dynamics; and (ii) a mechanistic approach where resonance was realized through a slow negative feedback loop akin to the physiological mechanism behind neuronal intrinsic resonance (Hutcheon and Yarom, 2000)

Summary

Introduction

46 Stability of network function, defined as the network’s ability to elicit robust functional outcomes despite perturbations to or widespread variability in its constitutive components, is a central theme that governs the functional design of several biological networks. A central question that spans different scales of organization is on the ability of biological networks to achieve physiological stability in the face of ubiquitous parametric variability (Turrigiano and Nelson, 2000; Edelman and Gally, 2001; Maslov and Sneppen, 2002; Stelling et al, 2004; Marder and Goaillard, 2006; Barkai and Shilo, 2007; Kitano, 2007; Félix and Barkoulas, 2015). We systematically assessed the impact of biological heterogeneities on stability of emergent spatial representations in a 2-D CAN model, and unveiled a physiologically plausible neural mechanism that promotes stability despite the expression of heterogeneities

Methods

Results

Conclusion