Scaling Spiking Neural Networks Trained Using Equilibrium Propagation to Convolutional Architectures

Core Concepts

Equilibrium Propagation (EP) can be used to efficiently train spiking convolutional neural networks, achieving performance comparable to state-of-the-art methods while being more memory-efficient.

Abstract

The paper explores how spiking convolutional neural networks can be trained using Equilibrium Propagation (EP), a biologically plausible local learning algorithm. The key contributions are: Formulation of spiking convolutional layers in the context of energy-based systems trained by EP. This bridges the gap between spiking and non-spiking convergent recurrent neural networks (RNNs) in the regime of EP. Theoretical and experimental analysis on the issues with maximum pooling and unpooling operations in spiking convergent RNNs trained by EP. The information mismatch between forward and backward connections significantly degrades the performance. Proposal to replace maximum pooling and unpooling with average pooling and nearest neighbor upsampling, which solves the aforementioned problem. This enables spiking convergent RNNs trained by EP to achieve state-of-the-art performance on MNIST (0.97% test error) and FashionMNIST (8.89% test error) datasets, comparable to networks trained by backpropagation. Demonstration of the memory efficiency of EP compared to backpropagation through time (BPTT) for training spiking neural networks. The findings highlight EP as an optimal choice for on-chip training and a biologically plausible method for computing error gradients in spiking neural networks.

Stats

The average activation summation of the middle 3 convolutional layers in the forward route is 0.9x, 0.8x, and 0.8x of the backward route in convergent RNNs, while it is 0.9x, 1.0x, and 0.9x in spiking neural networks. The standard deviation of activations in the forward route is 0.9x, 0.7x, and 0.7x of the backward route in convergent RNNs, while it is 3.1x, 2.6x, and 2.3x in spiking neural networks.

Quotes

"Equilibrium Propagation (EP) leverages unified circuitry and inherent dynamics of neural systems to adjust synaptic weights, which is analogous to how learning might occur in biological systems." "Spiking convergent RNNs trained by EP with the proposed modifications achieve performance at par with SNNs trained by BPTT and convergent RNNs trained by BPTT and EP, while they consume notable reduction in memory footprint compared to those trained by BPTT."

Key Insights Distilled From

Scaling SNNs Trained Using Equilibrium Propagation to Convolutional Architectures

by Jiaqi Lin,Ma... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02546.pdf

Deeper Inquiries

How can the proposed modifications to the pooling and unpooling operations be extended to other types of neural network architectures beyond convolutional networks

The modifications proposed for pooling and unpooling operations in spiking neural networks can be extended to various types of neural network architectures beyond convolutional networks. One way to extend these modifications is by adapting them to recurrent neural networks (RNNs). In RNNs, pooling operations are often used to downsample the temporal sequence data. By replacing maximum pooling with average pooling and adjusting the unsampling methods, the same benefits of mitigating information loss and activation magnitude imbalances can be achieved in RNN architectures. Additionally, these modifications can be applied to feedforward neural networks where pooling operations are utilized for feature extraction and dimensionality reduction. By incorporating average pooling and nearest neighbor unsampling, the challenges related to indexing mismatch and activation imbalances can be addressed in these architectures as well.

What are the potential challenges in scaling Equilibrium Propagation to larger and more complex spiking neural network models

Scaling Equilibrium Propagation to larger and more complex spiking neural network models poses several potential challenges. One significant challenge is the computational complexity associated with training deep spiking neural networks using Equilibrium Propagation. As the network size increases, the memory and computational requirements grow exponentially, making it challenging to train large-scale models efficiently. Additionally, the optimization process in Equilibrium Propagation may become more unstable with larger networks, leading to difficulties in convergence and training stability. Another challenge is the scalability of the proposed modifications for pooling and unpooling operations. Adapting these modifications to complex network architectures with multiple types of layers and connections may require intricate adjustments to ensure compatibility and effectiveness across the entire network.

Can Equilibrium Propagation be combined with other biologically-inspired learning mechanisms, such as Spike-Timing-Dependent Plasticity, to further improve the training and performance of spiking neural networks

Equilibrium Propagation can be combined with other biologically-inspired learning mechanisms, such as Spike-Timing-Dependent Plasticity (STDP), to enhance the training and performance of spiking neural networks. By integrating STDP principles into the weight update process of Equilibrium Propagation, the network can leverage the temporal correlations between pre- and post-synaptic spikes to adjust synaptic strengths. This combination can lead to more biologically plausible learning behavior in spiking neural networks, mimicking the synaptic plasticity observed in biological systems. Furthermore, the incorporation of STDP can help improve the network's ability to learn from temporal patterns and reinforce relevant connections while weakening irrelevant ones. By synergizing Equilibrium Propagation with STDP, spiking neural networks can achieve enhanced learning capabilities and better adapt to dynamic input patterns.