Deep Spiking Networks (1602.08323v2)

Abstract: We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only update their states when receiving signals from other neurons. Total computation of the network thus scales with the number of spikes caused by an input rather than network size. We show that the spiking Multi-Layer Perceptron behaves identically, during both prediction and training, to a conventional deep network of rectified-linear units, in the limiting case where we run the spiking network for a long time. We apply this architecture to a conventional classification problem (MNIST) and achieve performance very close to that of a conventional Multi-Layer Perceptron with the same architecture. Our network is a natural architecture for learning based on streaming event-based data, and is a stepping stone towards using spiking neural networks to learn efficiently on streaming data.

• The paper introduces spiking neural networks into deep learning using a spiking backpropagation method, enabling event-driven computation for potential efficiency gains.
• Empirical tests on MNIST demonstrated performance parity with conventional ReLU networks (2.07% vs 1.63% error), with a novel Fractional SGD method improving training outcomes.
• The research implies enhanced efficiency for event-based data, potential for efficient hardware implementation, and a path towards biologically plausible machine learning models.

Analysis of "Deep Spiking Networks" by O'Connor and Welling

The paper "Deep Spiking Networks" by Peter O'Connor and Max Welling introduces a novel approach within the field of neural networks, focusing specifically on the integration of spiking neural networks (SNNs) with traditional deep learning architectures. The authors aim to leverage the event-based nature of SNNs to improve computational efficiency and responsiveness in certain applications. Let's explore the methodology, empirical findings, and broader implications of this research.

Overview of Methodology

The authors present a spiking variant of the Multi-Layer Perceptron (MLP) by employing spiking neurons that mimic biological counterparts more closely than conventional units. In this architecture, neurons accumulate potentials via incoming signals and emit spikes when certain thresholds are exceeded. The spiking MLP approximates the functionality of a network with Rectified Linear Units (ReLU) under extended execution times.

Key contributions include:

• Spiking Backpropagation: The proposed method facilitates backpropagation in spiking networks by quantifying and discretizing error signals, allowing gradient transmission in this non-continuous domain.
• Event-Driven Computation: The computational workload scales based on the number of emitted spikes rather than the dimensions of the entire network, which is advantageous for sparse input data or event-based sensors such as silicon retinas.
• Early Guessing Capability: The network can approximate and generate intermediate predictions before processing the entire input stream, which is beneficial in real-time processing contexts.

Empirical Findings

The spiking MLP was evaluated using the conventional MNIST digit classification task to benchmark its capabilities against traditional ReLU-based networks. Several notable results include:

• Performance Parity: The spiking network's accuracy closely approached that of a conventional ReLU network, with test error differences being marginal (Spiking Network: 2.07%, ReLU MLP: 1.63%).
• Training Techniques: A notable distinction was observed between networks trained with standard Stochastic Gradient Descent (SGD) versus a novel Fractional SGD technique, with the latter achieving superior results (2.07% vs. 3.6% test error).

Implications and Future Directions

The development of spiking neural networks has profound implications for both theoretical research and practical applications:

• Efficiency in Event-Based Data: The event-centric operation of SNNs promises enhanced processing efficiency for data streams, such as those generated by dynamic visual sensors, reducing the need for data pre-processing and potentially leading to faster response times in real-world applications.
• Hardware Implementation: Due to their reliance solely on integer operations—such as addition and comparison—SNNs are candidates for efficient implementation on hardware that thrives under discrete mathematics frameworks.
• Biological Inspirations: By emulating certain biological aspects, spiking networks can explore the boundaries between artificial systems and natural intelligence, opening up avenues for biologically plausible machine learning models.

Overall, this research serves as a foundational effort towards mature, highly efficient spiking neural networks integrated with deep learning principles. Further exploration could concentrate on expanding the applicability of such models across varied data domains and real-time applications, potentially paving the way for new advancements in both AI and neurological studies. Future work might also seek to optimize the algorithmic components of spiking networks to enhance their adaptability and scalability, especially in large-scale problems or tasks that require immediate responses without sacrificing accuracy.