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Resonating neurons stabilize heterogeneous grid-cell networks.

Divyansh Mittal¹, Rishikesh Narayanan¹

¹Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.

|July 30, 2021

Summary

Biological networks maintain stability despite variability. This study shows that intrinsic resonance in continuous attractor networks (CANs) prevents disruptions from heterogeneity, preserving grid-patterned activity.

Keywords:

continuous attractor network grid cells heterogeneities negative feedback loop neuronal resonance neuroscience none stability

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Area of Science:

Computational neuroscience
Systems neuroscience
Network dynamics

Background:

Biological networks must maintain stable function amidst parametric variability.
Continuous attractor networks (CANs) are crucial for generating grid-patterned activity.
Biological heterogeneities can disrupt network stability and function.

Purpose of the Study:

To investigate the impact of biological heterogeneities on CAN stability.
To explore mechanisms for ameliorating heterogeneity-induced disruptions in grid-patterned activity.
To assess the role of intrinsic resonance in stabilizing heterogeneous biological networks.

Main Methods:

Modeling a two-dimensional continuous attractor network (CAN).
Introducing varying degrees of biological heterogeneities.
Implementing intrinsic resonance via phenomenological (high-pass filter) and mechanistic (negative feedback loop) approaches.

Main Results:

Increasing heterogeneities disrupted grid-patterned activity and increased low-frequency neural activity perturbations.
CAN models with resonating neurons demonstrated resilience to heterogeneities.
Mechanistic resonators, utilizing slow negative feedback, were more effective in suppressing low-frequency activity and stabilizing networks.

Conclusions:

Intrinsic resonance, particularly through mechanistic resonators, stabilizes heterogeneous biological networks.
Suppression of low-frequency activity is a universal mechanism for network stability.
Findings suggest implications for understanding and engineering biological systems with inherent variability.