Short-term plasticity (STP) is a mechanism that stores decaying memories in
synapses of the cerebral cortex. In computing practice, STP has been used, but
mostly in the niche of spiking neurons, even though theory predicts that it is
the optimal solution to certain dynamic tasks. Here we present a new type of
recurrent neural unit, the STP Neuron (STPN), which indeed turns out strikingly
powerful. Its key mechanism is that synapses have a state, propagated through
time by a self-recurrent connection-within-the-synapse. This formulation
enables training the plasticity with backpropagation through time, resulting in
a form of learning to learn and forget in the short term. The STPN outperforms
all tested alternatives, i.e. RNNs, LSTMs, other models with fast weights, and
differentiable plasticity. We confirm this in both supervised and reinforcement
learning (RL), and in tasks such as Associative Retrieval, Maze Exploration,
Atari video games, and MuJoCo robotics. Moreover, we calculate that, in
neuromorphic or biological circuits, the STPN minimizes energy consumption
across models, as it depresses individual synapses dynamically. Based on these,
biological STP may have been a strong evolutionary attractor that maximizes
both efficiency and computational power. The STPN now brings these neuromorphic
advantages also to a broad spectrum of machine learning practice. Code is
available at this https URL

本研究通过引入一种名为 STP 神经元的新型递归神经单元，利用可塑性存储短期内的遗忘和学习，从而提高了神经网络的效率和计算能力，并在测试中超越了其他模型。

神经元的短期可塑性：学习和遗忘

Short-Term Plasticity Neurons Learning to Learn and Forget

Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.

本文提出利用短期可塑性为脉冲神经网络提供与非脉冲神经网络不同的计算优势，并使用节约的局部突触可塑性模拟温度相关技术来解决网络计算困难性问题。这些网络在处理生成任务时表现出出色性能，在任务不平衡的情况下，它们甚至可以胜过传统算法。