Supervised Local Learning in Spiking Neural Networks with Paired Competing Neurons and Stabilized Spike Timing-Dependent Plasticity

Core Concepts

The authors propose Stabilized Supervised STDP (S2-STDP), a supervised STDP learning rule that teaches neurons to align their spikes with dynamically computed desired timestamps derived from the average firing time within the layer. They also introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of the classification layer trained with S2-STDP.

Abstract

The paper focuses on efficiently training a spiking classification layer with one spike per neuron and temporal decision-making. The authors first analyze the behavior of an existing supervised STDP (SSTDP) learning rule and identify two issues: the limited number of STDP updates per epoch and the saturation of firing timestamps toward the maximum firing time.
To address these issues, the authors propose S2-STDP, which employs error-modulated weight updates with dynamically computed desired timestamps derived from the average firing time within the layer. This helps increase the number of updates per epoch and stabilize the output firing timestamps at earlier timestamps.
Furthermore, the authors introduce the PCN training architecture, which associates each class with paired neurons and encourages neuron specialization toward target or non-target samples through intra-class competition. This enhances the learning capabilities of the classification layer trained with S2-STDP.
The authors evaluate their methods on image recognition datasets, including MNIST, Fashion-MNIST, and CIFAR-10. The results show that S2-STDP outperforms the existing supervised STDP learning rules, and the use of PCN further improves the performance, achieving state-of-the-art accuracy on MNIST and Fashion-MNIST. The authors also analyze the impact of their methods on the issues identified in SSTDP and demonstrate the effectiveness of PCN in enabling neuron specialization.

Stats

"Training with SSTDP results in a limited number of STDP updates per epoch, which may lead to premature training convergence and suboptimal model performance."
"Training with SSTDP causes the saturation of firing timestamps toward the maximum firing time, which may limit the expressivity of the SNN and its ability to separate classes."
"S2-STDP significantly increases the number of updates per epoch and reduces the saturation of firing timestamps toward the maximum firing time, enabling training convergence at higher accuracies compared to SSTDP."

Quotes

"Through intra-class competition, the use of the PCN architecture enables neuron specialization toward one type of sample, which helps them reach their desired firing timestamp and improve their learning capabilities."

Key Insights Distilled From

Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks

by Gaspard Goup... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2308.02194.pdf

Deeper Inquiries

How can the proposed methods be extended to train deeper spiking neural network architectures in a layer-wise fashion

The proposed methods, S2-STDP and PCN, can be extended to train deeper spiking neural network architectures in a layer-wise fashion by following a similar approach to the one outlined in the context. In a layer-wise training setup, each layer of the network is trained sequentially, starting from the input layer and moving towards the output layer.
To extend these methods to deeper architectures, the training process can be repeated for each layer of the network. The feature extraction network, such as the CSNN trained with unsupervised STDP, can be considered as the initial layers, and the classification layer equipped with S2-STDP and PCN can be treated as the final layer. This layer-wise training approach allows for the gradual learning of features at different levels of abstraction, leading to improved performance and interpretability of the network.
Additionally, for deeper architectures, it is essential to carefully design the connectivity patterns between layers to ensure effective information flow and learning. By incorporating S2-STDP and PCN in each layer of the network, it is possible to train deep spiking neural networks with supervised local learning in a systematic and efficient manner.

What are the potential limitations of the temporal coding scheme used in this work, and how could alternative coding schemes be integrated with the proposed learning rules

The temporal coding scheme used in this work, specifically the latency coding scheme, has certain limitations that may impact the performance of the spiking neural network. One potential limitation is the sensitivity to the choice of the maximum firing time (Tmax) parameter. Setting Tmax too low may result in information loss, while setting it too high may lead to overlapping spikes and reduced discriminability between input patterns.
Another limitation of latency coding is its reliance on precise spike timings, which can be challenging to implement accurately in hardware and may be sensitive to noise and variability. This can affect the robustness and generalization capabilities of the network, especially in real-world noisy environments.
To address these limitations and explore alternative coding schemes, other temporal coding methods such as rate coding or rank order coding could be integrated with the proposed learning rules. Rate coding involves encoding information in the firing rate of neurons over time, providing a more continuous representation of input data. Rank order coding, on the other hand, encodes information based on the relative timing of spikes across neurons, offering a distributed representation of features.
By combining different coding schemes with the proposed learning rules, the network can benefit from the strengths of each method and potentially overcome the limitations associated with a single coding scheme. This hybrid approach could enhance the network's performance, robustness, and adaptability to various types of input data.

Can the principles of S2-STDP and PCN be applied to other supervised learning tasks beyond image recognition, such as speech recognition or natural language processing

The principles of S2-STDP and PCN can indeed be applied to other supervised learning tasks beyond image recognition, such as speech recognition or natural language processing. These methods offer a framework for training spiking neural networks with supervised local learning, which can be adapted to different types of data and tasks.
For speech recognition, S2-STDP can be used to train the classification layer of a spiking neural network to recognize phonemes or words based on temporal patterns in the input speech signals. By defining desired timestamps for specific phonetic features or linguistic units, the network can learn to classify spoken words accurately.
In natural language processing, S2-STDP and PCN can be employed to train spiking neural networks for tasks like sentiment analysis, text classification, or language modeling. By mapping words or phrases to temporal representations and defining target firing times for different linguistic categories, the network can learn to make predictions based on the temporal structure of the input text data.
Overall, the adaptability and flexibility of S2-STDP and PCN make them suitable for a wide range of supervised learning tasks beyond image recognition, opening up possibilities for applying spiking neural networks to various domains and applications in the field of artificial intelligence.