Habashy Karim G, Evans Benjamin D, Goodman Dan F M, Bowers Jeffrey S
School of Psychological Science, University of Bristol, Bristol, South West England, United Kingdom.
Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, East Sussex, United Kingdom.
PLoS Comput Biol. 2024 Dec 13;20(12):e1012673. doi: 10.1371/journal.pcbi.1012673. eCollection 2024 Dec.
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.
大脑已经进化出具有不同形态和动力学的多种神经元，这些会影响时间信息处理。相比之下，大多数神经网络模型使用仅在空间参数（权重和偏差）上有所不同的同质单元。为了探究时间参数的重要性，我们在具有不同时间复杂度的任务上训练脉冲神经网络，保持不同的参数子集不变。我们发现，在严格的资源限制下，调整传导延迟对于解决所有测试条件至关重要。值得注意的是，这些任务仅使用具有恒定权重的时间参数（延迟和时间常数）就能解决。在更复杂的时空任务中，一个可调整的爆发参数至关重要。总体而言，允许时间和空间参数都进行调整可增强网络对噪声的鲁棒性，这是生物大脑和神经形态计算系统的一个重要特征。我们的研究结果表明，丰富且可适应的动力学可能是进化生物中有效解决时间结构化任务的关键，这将有助于解释生物神经元多样的生理特性。
