School of Physics, The University of Sydney, Sydney, NSW, Australia.
ARC Centre of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia.
PLoS Comput Biol. 2018 Nov 12;14(11):e1006590. doi: 10.1371/journal.pcbi.1006590. eCollection 2018 Nov.
Diverse plasticity mechanisms are orchestrated to shape the spatiotemporal dynamics underlying brain functions. However, why these plasticity rules emerge and how their dynamics interact with neural activity to give rise to complex neural circuit dynamics remains largely unknown. Here we show that both Hebbian and homeostatic plasticity rules emerge from a functional perspective of neuronal dynamics whereby each neuron learns to encode its own activity in the population activity, so that the activity of the presynaptic neuron can be decoded from the activity of its postsynaptic neurons. We explain how a range of experimentally observed plasticity phenomena with widely separated time scales emerge from learning this encoding function, including STDP and its frequency dependence, and metaplasticity. We show that when implemented in neural circuits, these plasticity rules naturally give rise to essential neural response properties, including variable neural dynamics with balanced excitation and inhibition, and approximately log-normal distributions of synaptic strengths, while simultaneously encoding a complex real-world visual stimulus. These findings establish a novel function-based account of diverse plasticity mechanisms, providing a unifying framework relating plasticity, dynamics and neural computation.
多种可塑性机制被协调来塑造大脑功能的时空动态。然而，这些可塑性规则为何出现，以及它们的动态如何与神经活动相互作用，从而产生复杂的神经回路动态，在很大程度上仍然未知。在这里，我们表明赫布和平衡型可塑性规则都源自神经元动态的功能视角，即每个神经元都学会在群体活动中编码自身的活动，从而可以从其突触后神经元的活动中解码出前突触神经元的活动。我们解释了一系列具有广泛分离时间尺度的实验观察到的可塑性现象如何从学习这种编码功能中出现，包括 STDP 及其频率依赖性和超可塑性。我们表明，当这些可塑性规则在神经回路中实现时，它们会自然地产生基本的神经响应特性，包括具有平衡兴奋和抑制的可变神经动力学，以及突触强度的近似对数正态分布，同时对复杂的现实世界视觉刺激进行编码。这些发现为多种可塑性机制建立了一种新的基于功能的解释，提供了一个将可塑性、动力学和神经计算联系起来的统一框架。
