Brain-Inspired Computational Intelligence via Predictive Coding (2308.07870v1)

Abstract: AI is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.

• The paper demonstrates predictive coding as a brain-inspired framework that addresses limitations of backpropagation by employing local synaptic updates and minimization of free energy.
• It reviews key implementations including Rao and Ballard’s model, neural generative coding, and BC-DIM, all grounded in variational inference techniques.
• Practical applications discussed range from image recognition to few-shot learning, with promising implications for energy-efficient neuromorphic computing.

Overview of Brain-Inspired Computational Intelligence via Predictive Coding

The examined paper provides a comprehensive review of predictive coding (PC) as an alternative computational intelligence framework, grounded in neuroscience theories, for machine learning. This approach emerges as a response to the existing limitations of standard deep neural networks trained with backpropagation. The discussed limitations include computational inefficiencies, challenges in uncertainty quantification, lack of robustness, unreliable performance, and the implausibility of mimicking biological processes.

Predictive coding, as delineated in this paper, offers a brain-inspired schema for modeling information processing and is mathematically grounded in variational inference. It acts as a potent inversion method for continuous-state generative models by bringing the principles of the Bayesian brain hypothesis into machine learning. This framework not only models sensory processes as hierarchical inference problems but also enables an understanding of the brain's learning system through the minimization of variational free energy.

Key Implementations of Predictive Coding

The paper briefly discusses seminal PC implementations, including the classical architecture proposed by Rao and Ballard, capturing cortical processes in hierarchical structures, as well as the formulations of neural generative coding (NGC) and biased competition with divisive input modulation (BC-DIM). These models vary in their architectural specifics and message-passing paradigms, yet they share a fundamental commitment to local synaptic updates based on prediction errors or free energy gradients. The locality offered by PC not only affords compatibility with arbitrary neural topologies but also translates into stronger convergence properties and robustness when compared to backpropagation.

Application Domain Insight

In practice, predictive coding frameworks have been applied across a range of machine learning problems, demonstrating efficacy in image and speech recognition, video prediction, object classification, and more. For instance, convolutional applications extend these methods to large visual datasets, while temporal neural coding networks address sequence processing tasks, reflecting the diverse applicability of PC. Importantly, these networks manifest advantages in scenarios involving few-shot learning, transfer learning, and situations plagued by catastrophic forgetting—scenarios that remain challenging for conventional backprop-trained models.

The Promise of Neuro-Inspired Computational Intelligence

One central claim of the paper is that predictive coding holds potential for foundational shifts in AI, especially when applied to energy-efficient hardware environments, such as neuromorphic computing systems. Here, PC's focus on local learning and inference aligns intimately with the topological and operational designs of emerging computational substrates, allowing for promising integrations that could redefine the efficiency and dimensions of AI applications.

Future Research and Implications

From a theoretical perspective, PC models continue to be ripe subjects for research, examining their connections to other computational principles, such as a broader class of variational inference techniques and complex generative models. Practically, developing more scalable and computationally efficient implementations is suggested, necessitating novel optimizations akin to advancements that bolstered deep learning.

In conclusion, while predictive coding is far from supplanting deep neural networks in all areas, the research highlights its capability to complement existing models and address backpropagation-related challenges in specific scenarios. Continued exploration is anticipated in uncovering the broader potential of PC in AI, leveraging its neuroscientific credibility and pushing it towards large-scale applicability in next-generation artificial intelligence systems.