Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Cortically Inspired Architecture for Modular Perceptual AI

Prerna Luthra

TL;DR

This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI that supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces.

Abstract

This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI. While current monolithic models such as GPT-4V achieve impressive performance, they often struggle to explicitly support interpretability, compositional generalization, and adaptive robustness - hallmarks of human cognition. Drawing on neuroscientific models of cortical modularity, predictive processing, and cross-modal integration, we advocate decomposing perception into specialized, interacting modules. This architecture supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces. Our proof-of-concept study provides empirical evidence that modular decomposition yields more stable and inspectable representations. By grounding AI design in biologically validated principles, we move toward systems that not only perform well, but also support more transparent and human-aligned inference.

A Cortically Inspired Architecture for Modular Perceptual AI

TL;DR

This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI that supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces.

Abstract

This paper bridges neuroscience and artificial intelligence to propose a cortically inspired blueprint for modular perceptual AI. While current monolithic models such as GPT-4V achieve impressive performance, they often struggle to explicitly support interpretability, compositional generalization, and adaptive robustness - hallmarks of human cognition. Drawing on neuroscientific models of cortical modularity, predictive processing, and cross-modal integration, we advocate decomposing perception into specialized, interacting modules. This architecture supports structured, human-inspired reasoning by making internal inference processes explicit through hierarchical predictive feedback loops and shared latent spaces. Our proof-of-concept study provides empirical evidence that modular decomposition yields more stable and inspectable representations. By grounding AI design in biologically validated principles, we move toward systems that not only perform well, but also support more transparent and human-aligned inference.
Paper Structure (39 sections, 4 figures, 1 table)

This paper contains 39 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Modular Specialization and Hierarchical Organization.
  3. Predictive Processing.
  4. Cross-Modal Integration.
  5. Neuroscientific Motivation
  6. Modular Specialization and Isolation of Function
  7. Predictive Feedback and Active Inference
  8. Cross-Modal Integration and Semantic Convergence
  9. Architectural Blueprint for Modular Cortical AI
  10. Specialist Encoders for Each Modality
  11. Shared Cross-Modal Latent Space
  12. Routing Controller for Expert Selection
  13. Recurrent Predictive Feedback Loops
  14. Information Flow and Feedback-Driven Reasoning
  15. Hallucinations as Predictive Artifacts of Generative Inference
  16. ...and 24 more sections

Figures (4)

  • Figure 1: Architectural blueprint for modular perceptual AI, illustrating specialist encoders, a shared multimodal workspace, routing control, and predictive feedback loops.
  • Figure 2: SAE training convergence (MSE=0.0026 at epoch 20).
  • Figure 3: Domain-feature clustering: real (left), shuffled control (center), sparsity metrics (right)
  • Figure 4: Modular factorization: feature uniqueness (left), within-domain stability +15.4pp (center), reconstruction fidelity (right).