论文标题
评估稳态可塑性学会在多大程度上计算非结构化神经元网络中的预测错误
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks
论文作者
论文摘要
人们认为，大脑在某种程度上通过对感觉刺激进行预测并在“预测误差神经元”活动中对这些预测的偏差进行编码。该原理定义了广泛影响的预测编码理论。动物学会计算和更新预测的精确电路和可塑性机制尚不清楚。稳态抑制性突触可塑性是训练神经元网络进行预测编码的有前途的机制。稳态可塑性会导致神经元保持稳定的基线发射速率，以响应与训练网络的输入相匹配的输入，但是射击速率可能会偏离此基线，以响应与训练不匹配的刺激。我们系统地结合了计算机模拟和数学分析，以测试以稳态抑制性突触可塑性训练后，随机连接的非结构化网络计算预测错误。我们发现，仅体内稳态可塑性足以计算琐碎的时构刺激的预测误差，但不能用于更现实的时变刺激。我们使用塑料网络的平均场理论来解释我们的发现并表征它们所应用的假设。
The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply.
