Max Planck Institute for Brain Research, Frankfurt am Main, Germany.
School of Life Sciences, Technical University of Munich, Freising, Germany.
PLoS Comput Biol. 2022 Dec 2;18(12):e1010682. doi: 10.1371/journal.pcbi.1010682. eCollection 2022 Dec.
Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.
突触变化被认为是大脑学习和记忆形成的基础。但是，兴奋性突触的赫布式突触可塑性本身并不稳定，导致突触强度无限制地增长，或者神经元活动沉默，而没有其他的动态平衡机制。为了控制兴奋性突触强度，我们提出了一种抑制性突触可塑性的新形式。通过计算建模，我们提出了抑制性可塑性的两个关键特征，即抑制作用对兴奋作用的主导地位，以及对突触后兴奋性神经元放电率的非线性依赖性，即抑制性突触强度随兴奋性突触强度的相同符号（增强或减弱）而变化。我们证明，这种新型抑制性可塑性模型实现的稳定突触强度与实验结果一致，影响兴奋性/抑制性权重比。施加去抑制信号可以门控可塑性，并在一个递归网络中产生感受野和强双向连接。因此，一种新的非线性抑制性可塑性形式可以同时稳定兴奋性突触强度，并在去抑制时实现学习。
