Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems (1311.0966v3)

Abstract: Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

• The paper introduces an event-driven Contrastive Divergence algorithm that enables real-time RBM training using spiking neuromorphic systems.
• The method integrates leaky Integrate-and-Fire neurons with Spike Time Dependent Plasticity for online, asynchronous updates.
• Experimental results demonstrated up to 91.9% recognition accuracy on MNIST, highlighting power-efficient performance amidst hardware constraints.

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems: An Expert Analysis

The paper introduces an event-driven adaptation of Contrastive Divergence (CD) for training Restricted Boltzmann Machines (RBMs) using spiking neuromorphic systems. This work addresses the inherent challenges in implementing machine learning models, such as RBMs, on neuromorphic hardware. These hardware platforms are characterized by their ability to emulate the dynamics of biological neural networks, offering considerable advantages in terms of power efficiency and scalability.

Key Contributions and Results

The primary contribution of this research is the development of a novel training paradigm that leverages the continuous-time dynamics of spiking neurons for neural sampling. The authors propose a method that integrates spiking Integrate-and-Fire (IF) neurons with an online learning rule, which they term as event-driven CD. This rule is characterized by asynchronous, spike-triggered updates, a feature that is particularly well-suited for neuromorphic systems where batch updates are impractical.

The approach presented allows for RBM training by using neural sampling to approximate the target Boltzmann distribution. By replacing the traditional discrete steps in CD with recurrent network activities and employing Spike Time Dependent Plasticity (STDP) for weight updates, the model effectively operates in real-time. The authors demonstrate the efficacy of their method by implementing an RBM with leaky IF neurons to learn a generative model of the MNIST dataset, achieving recognition accuracies close to those obtained using standard CD and Gibbs sampling methods.

Remarkably, their spiking neural network prototype achieved up to 91.9% accuracy in recognition tasks, compared to a 93.6% accuracy using standard methods. Furthermore, it exhibited robust performance despite approximations inherent to the spiking model and showed sensitivity to finite-precision weights, simulating practical hardware constraints.

Implications and Future Directions

The event-driven CD method proposed has several theoretical and practical implications. Theoretically, it bridges the gap between stochastic computing models (e.g., RBMs) and their neuromorphic counterparts, representing a significant stride toward implementing deep learning architectures on physical neural hardware. Practically, it paves the way for deploying neural networks in power-constrained environments, such as edge devices or embedded systems where real-time processing is critical.

This work implies potential future developments in several directions. Firstly, extending this method to Deep Belief Networks (DBNs) and deeper network architectures could enhance the computational capabilities and efficiency of neuromorphic systems. Secondly, implementing this approach on custom neuromorphic hardware could shed light on the adaptability and resilience of spiking network models to hardware-specific constraints such as noise and device variability.

Furthermore, the paper presents a compelling case for developing more sophisticated synaptic plasticity mechanisms that align with biological neural processes, which would be critical for advancing the fidelity of neuromorphic models. The exploration of different neuron models and synaptic dynamics compatible with neuromorphic devices, such as memristive synapses, could open new avenues for research and innovation in this domain.

Conclusion

In summary, this work elucidates a significant methodological advancement by integrating spiking neuromorphic systems with probabilistic models. The authors provide evidence of the effectiveness of event-driven CD through compelling experimental results, exposing the profound possibilities for neuromorphic computing in machine learning tasks. The research presents a foundational step toward realizing practical, power-efficient, and scalable neuromorphic processors capable of performing complex cognitive tasks, providing a substantiated groundwork for future explorations and innovations in artificial intelligence and computational neuroscience.