A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP
This work addresses the challenge of temporal encoding for AI systems and neuroscience, though it appears incremental as it builds on existing SNN and STDP approaches without claiming major breakthroughs.
The authors tackled the problem of encoding temporal information in neural networks by developing a deep spiking neural network with biologically-inspired learning rules, demonstrating that it can encode temporal data through self-organized synchronous neural groups. They analyzed network entropy as a metric for information transfer, aiming to advance both artificial intelligence and understanding of brain coding mechanisms.
The brain is known to be a highly complex, asynchronous dynamical system that is highly tailored to encode temporal information. However, recent deep learning approaches to not take advantage of this temporal coding. Spiking Neural Networks (SNNs) can be trained using biologically-realistic learning mechanisms, and can have neuronal activation rules that are biologically relevant. This type of network is also structured fundamentally around accepting temporal information through a time-decaying voltage update, a kind of input that current rate-encoding networks have difficulty with. Here we show that a large, deep layered SNN with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data. We argue that the randomness inherent in the network weights allow the neurons to form groups that encode the temporal data being inputted after self-organizing with STDP. We aim to show that precise timing of input stimulus is critical in forming synchronous neural groups in a layered network. We analyze the network in terms of network entropy as a metric of information transfer. We hope to tackle two problems at once: the creation of artificial temporal neural systems for artificial intelligence, as well as solving coding mechanisms in the brain.