小柯机器人

加拿大渥太华大学Richard Naud和麦吉尔大学Blake A. Richards研究小组合作揭示，突触依赖的突触可塑性可以协调层次回路中的学习行为。相关论文于2021年5月13日发表在《自然-神经科学》杂志上。

研究人员认为如果突触可塑性受高频尖峰脉冲所调节，则层次电路中较高的锥体神经元可以协调较低级别连接的可塑性。使用模拟和数学分析，研究人员证明，与短期突触动力学、顶端树突中的再生活动和反馈途径中的突触可塑性相结合时，依赖于突触的学习行为可以解决需要深度网络架构的挑战性任务。该研究结果表明，众所周知的树突、突触和突触可塑性特性足以在层次电路中调控复杂的学习行为。

据了解，突触可塑性被认为是调控学习的关键生理机制。众所周知，它取决于突触前和突触后的活动。但是，现有仅依靠突触前和突触后活动进行突触变化的模型尚不能解决学习需要分层网络中信用分配复杂任务的问题。

附：英文原文

Title: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Author: Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A. Richards, Richard Naud

Issue&Volume: 2021-05-13

Abstract: 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. The authors propose a synaptic plasticity rule for pyramidal neurons based on postsynaptic bursting that captures experimental data and solves the credit assignment problem for deep networks.

DOI: 10.1038/s41593-021-00857-x

Source: https://www.nature.com/articles/s41593-021-00857-x

Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex