**Spiking Neural Networks** (**SNNs**)  are a class of artificial neural networks inspired by the structure and functioning of the brain's neural networks. Unlike traditional artificial neural networks that operate based on continuous firing rates, SNNs simulate the behavior of individual neurons through discrete spikes or action potentials. These spikes are triggered when the neuron's membrane potential reaches a certain threshold, and they propagate through the network, communicating information and triggering subsequent neuron activations. This spike-based communication allows SNNs to capture the temporal dynamics of information processing and exhibit asynchronous, event-driven behavior, making them well-suited for tasks such as temporal pattern recognition, event detection, and real-time processing. SNNs have gained attention due to their potential in efficiently processing and encoding information, offering advantages in energy efficiency, robustness, and compatibility with neuromorphic hardware architectures.

**Clipped Double Q-learning** is a variant on [Double Q-learning](https://paperswithcode.com/method/double-q-learning) that upper-bounds the less biased Q estimate $Q\_{\theta\_{2}}$ by the biased estimate $Q\_{\theta\_{1}}$. This is equivalent to taking the minimum of the two estimates, resulting in the following target update:

$$ y\_{1} = r + \gamma\min\_{i=1,2}Q\_{\theta'\_{i}}\left(s', \pi\_{\phi\_{1}}\left(s'\right)\right) $$

The motivation for this extension is that vanilla double [Q-learning](https://paperswithcode.com/method/q-learning) is sometimes ineffective if the target and current networks are too similar, e.g. with a slow-changing policy in an actor-critic framework.

Clipped Double Q-learning

Addressing Function Approximation Error in Actor-Critic Methods

Self-Normalizing Neural Networks

**CTAL** is a pre-training framework for strong audio-and-language representations with a [Transformer](https://paperswithcode.com/method/transformer), which aims to learn the intra-modality and inter-modalities connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs: masked language modeling and masked cross-modal acoustic modeling. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream

Source	Self-Normalizing Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com