Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Center, DE-52428 Jülich, Germany.
Department of Physiology, University of Bern, CH-3012 Bern, Switzerland.
Proc Natl Acad Sci U S A. 2023 Aug 8;120(32):e2300558120. doi: 10.1073/pnas.2300558120. Epub 2023 Jul 31.
While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.
虽然大脑中的感觉表示依赖于上下文，但目前尚不清楚这种调制如何在生物物理层面上实现，以及处理层次结构中的更深层次如何提取每个可能的上下文状态的有用特征。在这里，我们证明树突棘 N-甲基-D-天冬氨酸(spike)可以在生理限制内实现前馈处理的上下文调制。这种神经元特异性的调制利用了稳定的前馈权重中编码的先验知识，以实现跨上下文的迁移学习。在具有与上下文无关的前馈权重的生物物理现实神经元模型网络中，我们表明，树突分支的调制输入可以使用赫布式、误差调制学习规则解决线性不可分的学习问题。我们还证明，局部预测表示是来自不同的输入，还是来自同一输入的不同上下文调制，可以导致跨处理层的层次前馈权重的表示学习，以适应多种上下文。
