Adapting to time: Why nature may have evolved a diverse set of neurons.

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 par

SUBMITTER: Habashy KG 

PROVIDER: S-EPMC11676785 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature