Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada.
Nat Neurosci. 2021 Jul;24(7):1010-1019. doi: 10.1038/s41593-021-00857-x. Epub 2021 May 13.
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.
突触可塑性被认为是学习的关键生理机制。众所周知，它依赖于突触前和突触后的活动。然而，迄今为止，仅依靠突触前和突触后的活动来改变突触的模型还不能解释需要在分层网络中进行信用分配的复杂学习任务。在这里，我们表明，如果突触可塑性受到高频尖峰爆发的调节，那么分层电路中较高的锥体神经元可以协调较低层次连接的可塑性。我们通过模拟和数学分析证明，当与短期突触动力学、树突顶的再生活动和反馈通路中的突触可塑性相结合时，依赖爆发的学习规则可以解决需要深层网络结构的具有挑战性的任务。我们的结果表明，树突、突触和突触可塑性的已知特性足以使分层电路实现复杂的学习。
