The Neural Coding Framework for Learning Generative Models (2012.03405v4)

Abstract: Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive processing theory, the neurons in the brain form a hierarchy in which neurons in one level form expectations about sensory inputs from another level. These neurons update their local models based on differences between their expectations and the observed signals. In a similar way, artificial neurons in our generative models predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality. In this work, we show that the neural generative models learned within our framework perform well in practice across several benchmark datasets and metrics and either remain competitive with or significantly outperform other generative models with similar functionality (such as the variational auto-encoder).

• The paper introduces the Neural Generative Coding (NGC) framework, a biologically plausible approach for training generative models inspired by the brain's predictive processing.
• Experiments show NGC models compete on datasets like MNIST, demonstrating data efficiency and generating high-quality data patterns.
• This framework offers a biologically-inspired alternative to backpropagation, potentially leading to more generalizable and adaptive AI systems for complex environments.

The Neural Coding Framework for Learning Generative Models

This paper introduces a computational framework inspired by predictive processing in the brain for developing neural generative models. It proposes a systematic approach, referred to as Neural Generative Coding (NGC), which uses biologically plausible learning mechanisms to enhance the efficacy of generative models. These models have broad applicability in estimating complex probability distributions, synthesizing novel data patterns, and offering competitive solutions against traditional methods like variational auto-encoders (VAEs).

Overview of Neural Generative Coding (NGC)

Inspired by predictive processing theory, NGC emulates a hierarchy of neurons that align their local models through prediction errors, mirroring the brain's adaptive capabilities in the absence of teaching signals. Neurons interact to predict neighboring neurons' states, adjusting their synaptic parameters based on the deviation from expected signals. This error-correction process is integral to the learning mechanism proposed, aiming to mimic brain-like computation more accurately than conventional artificial neural networks trained by backpropagation.

Experimental Findings

The authors demonstrate that models based on NGC achieved impressive performance in reconstructing and generating data patterns. They tested various configurations of NGC on prominent datasets like MNIST, FMNIST, KMNIST, and CalTech, measuring binary cross-entropy (BCE) and marginal log likelihood $\log p(\mathbf{x})$. In reconstructing and estimating probability distributions, NGC-based models either matched or exceeded the performance of competing generative models like the GAN-AE, GVAE, and GMM.

1. Data Efficiency: The NGC models exhibited effective generalization with limited data. This capability is particularly beneficial as it compensates for the increased computational cost per sample, offering a more data-efficient learning process compared to backpropagation-based models.
2. Generative Performance: In synthesizing data patterns, the NGC models produced visually competitive samples. The GNCN-PDH model demonstrated particularly robust performance across various data sets, in terms of both reconstruction accuracy and likelihood estimation.
3. Downstream Tasks: Beyond generative modeling, the latent representations developed by NGC models were adept at supporting downstream tasks like image classification and pattern completion. NGC models outperformed other baselines, suggesting that the brain-inspired learning framework fosters more versatile representations.

Implications and Future Directions

The paper argues that embracing biologically motivated learning paradigms could significantly enhance the generalizability and efficiency of machine learning systems. NGC's methodological departure from backpropagation offers a promising direction for developing learning algorithms that not only improve pattern synthesis but also adapt more dynamically to environmental changes, akin to the human brain.

The potential applications of NGC models are significant, ranging from enhancing AI's ability to interact with complex real-world environments to offering insights into the cognitive processes underlying prediction and learning. Future research could explore the integration of NGC with other neurobiological insights, developing more intricate models that further align with neural computation in the brain.

In conclusion, this paper provides compelling evidence that biologically inspired generative models can achieve competitive performance without the constraints and limitations of traditional backpropagation, paving the way for more adaptive, efficient, and general-purpose AI systems.