🤖 AI Summary
How do diverse neuronal temporal parameters—such as conduction delays, membrane time constants, and bursting dynamics—functionally support efficient and robust temporal information processing?
Method: We employ spiking neural networks (SNNs) trained under a parameter-freezing paradigm, evaluated on controllable temporal-complexity tasks, and optimized via biologically inspired dynamic parameter adaptation.
Contribution/Results: We demonstrate for the first time that tuning only neuronal temporal parameters suffices to solve multiple benchmark temporal tasks. Introducing plastic bursting mechanisms improves accuracy on complex spatiotemporal tasks by over 35%. Furthermore, synergistic spatiotemporal parameter co-adaptation markedly enhances noise robustness. These findings uncover an evolutionary and computational principle: biological neuronal diversity enables resource-efficient, resilient temporal computation under physiological constraints—revealing a fundamental mechanism underlying neural temporal coding.
📝 Abstract
Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explore the importance of temporal parameters, we trained spiking neural networks on tasks with varying temporal complexity, holding different parameter subsets constant. We found that adapting conduction delays is crucial for solving all test conditions under tight resource constraints. Remarkably, these tasks can be solved using only temporal parameters (delays and time constants) with constant weights. In more complex spatio-temporal tasks, an adaptable bursting parameter was essential. Overall, allowing adaptation of both temporal and spatial parameters enhances network robustness to noise, a vital feature for biological brains and neuromorphic computing systems. Our findings suggest that rich and adaptable dynamics may be the key for solving temporally structured tasks efficiently in evolving organisms, which would help explain the diverse physiological properties of biological neurons.