Integration of Contrastive Predictive Coding and Spiking Neural Networks (2506.09194v1)

Abstract: This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achieved a high classification rate in distinguishing positive sequential samples from non-sequential negative samples. The study demonstrates that CPC can be effectively combined with SNN, showing that an SNN trained for classification tasks can also function as an encoding mechanism. Project codes and detailed results can be accessed on our GitHub page: https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN

• The paper integrates contrastive predictive coding with spiking neural networks by replacing the CNN encoder with SNNs and combining STDP with contrastive loss to boost biological plausibility.
• It employs a GRU for autoregressive tasks, demonstrating that SNN-based encoders can reach up to 96.83% validation accuracy on a reduced MNIST dataset.
• The study highlights potential advancements in energy-efficient neuromorphic computing and predictive coding models, setting the stage for future research on temporally dependent datasets.

Integration of Contrastive Predictive Coding and Spiking Neural Networks

The paper "Integration of Contrastive Predictive Coding and Spiking Neural Networks" presents an innovative paper examining the synergy between Contrastive Predictive Coding (CPC) and Spiking Neural Networks (SNNs). CPC is inherently a self-supervised learning approach that utilizes contrastive loss to yield representations encapsulating temporal context. In contrast, SNNs emulate the temporal dynamics of biological neural systems, operating with discrete spike-timing rather than continuous values. This paper posits that the amalgamation of these methodologies could lead to a predictive coding model with heightened biological plausibility while also improving computational efficiency.

Methodological Framework

In integrating CPC with SNNs, the paper undertakes several substantive steps:

1. Encoder Replacement: The convolutional neural network component in traditional CPC was substituted with SNNs, specifically examining both SNN-Classifier and SNN-Autoencoder as encoder options.
2. Autoregressive Component: A Gated Recurrent Unit (GRU) was employed to handle autoregressive tasks.
3. Learning Mechanism: The paper melded Spike-Timing Dependent Plasticity (STDP) rules with CPC loss, fostering a biologically grounded learning process.

These steps enabled the CPC framework to be operational within the spike-based domain of SNNs, allowing for encoding with structures trained for disparate tasks.

Numerical Results

The paper revealed key numerical results demonstrating the effectiveness of this integration. Notably, the SNN-Autoencoder configuration achieved a maximum validation accuracy of approximately 96.83% on a reduced MNIST dataset of 2500 samples. Conversely, the CPC network utilizing random encoding hovered around 55% accuracy, underscoring the significant improvement offered by structured SNN encoding.

Implications and Future Directions

The paper suggests several practical and theoretical implications of the research:

• Biological Plausibility: The proposed model augments biological alignment in predictive coding models, contributing to the development of both biologically inspired computational structures and energy-efficient systems.
• Utility in Encoding Tasks: Demonstrates the competency of SNNs, even those trained for classification tasks, in encoding tasks, potentially diversifying their application in neuromorphic computing environments.

Future research could explore applying this model to diverse, temporally dependent datasets such as Human Action Recognition tasks. Moreover, further testing on neuromorphic hardware could yield insights into the real-world applicability and performance advantages of these biologically inspired models.

In summary, the integration of CPC with SNN presents promising results that expand the dimensions of biologically plausible models within the domain of self-supervised learning, marking a significant stride toward both theoretical comprehension and practical applications in AI advancement.