Computational Neuroscience Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
PLoS Comput Biol. 2022 Sep 8;18(9):e1010461. doi: 10.1371/journal.pcbi.1010461. eCollection 2022 Sep.
Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called "oracle-supervised Neural Engineering Framework" (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses). We show that osNEF networks exhibit the target dynamics by accounting for nonlinearities present within the neuron models: performance is comparable across all four systems and all four neuron models, with variance proportional to task and neuron model complexity. We also apply osNEF to build a model of working memory that performs a delayed response task using a combination of pyramidal cells and inhibitory interneurons connected with NMDA and GABA synapses. The baseline performance and forgetting rate of the model are consistent with animal data from delayed match-to-sample tasks (DMTST): we observe a baseline performance of 95% and exponential forgetting with time constant τ = 8.5s, while a recent meta-analysis of DMTST performance across species observed baseline performances of 58 - 99% and exponential forgetting with time constants of τ = 2.4 - 71s. These results demonstrate that osNEF can train functional brain models using biologically-detailed components and open new avenues for investigating the relationship between biophysical mechanisms and functional capabilities.
提高生物学合理性和功能能力是将低级神经细节与高级行为现象联系起来的脑模型的两个重要目标。我们开发了一种称为“oracle-supervised Neural Engineering Framework”（osNEF）的方法，用于训练具有生物学细节的尖峰神经网络，以实现各种认知相关的动力系统。具体来说，我们使用四种不同的神经元模型（泄漏积分和放电神经元、Izhikevich 神经元、4 维非线性点神经元和 4 室、6 离子通道层 V 锥体细胞重建）和各种突触模型（基于电流的突触、基于电导的突触和电压门控突触）训练网络执行常见于认知系统的计算（通信、乘法、谐波振荡和门控工作记忆）。我们表明，osNEF 网络通过考虑神经元模型中的非线性来表现出目标动态：在所有四个系统和所有四个神经元模型中，性能都是可比的，方差与任务和神经元模型的复杂性成正比。我们还应用 osNEF 构建了一个工作记忆模型，该模型使用连接 NMDA 和 GABA 突触的锥体细胞和抑制性中间神经元来执行延迟反应任务。该模型的基线性能和遗忘率与延迟匹配样本任务（DMTST）的动物数据一致：我们观察到基线性能为 95%，与时间常数 τ = 8.5s 的指数遗忘，而最近对跨物种 DMTST 性能的荟萃分析观察到基线性能为 58-99%，与时间常数 τ = 2.4-71s 的指数遗忘。这些结果表明，osNEF 可以使用具有生物学细节的组件训练功能性脑模型，并为研究生物物理机制和功能能力之间的关系开辟新的途径。
