pith. sign in

arxiv: 2507.05888 · v1 · pith:7QRXVHREnew · submitted 2025-07-08 · 🧬 q-bio.NC · cs.AI

Hierarchy or Heterarchy? A Theory of Long-Range Connections for the Sensorimotor Brain

classification 🧬 q-bio.NC cs.AI
keywords regionsconnectionshierarchyobjectsheterarchyhierarchicalinformationlearn
0
0 comments X
read the original abstract

In the traditional understanding of the neocortex, sensory information flows up a hierarchy of regions, with each level processing increasingly complex features. Information also flows down the hierarchy via a different set of connections. Although the hierarchical model has significant support, many anatomical connections do not conform to the standard hierarchical interpretation. In addition, hierarchically arranged regions sometimes respond in parallel, not sequentially as would occur in a hierarchy. This and other evidence suggests that two regions can act in parallel and hierarchically at the same time. Given this flexibility, the word "heterarchy" might be a more suitable term to describe neocortical organization. This paper proposes a new interpretation of how sensory and motor information is processed in the neocortex. The key to our proposal is what we call the "Thousand Brains Theory", which posits that every cortical column is a sensorimotor learning system. Columns learn by integrating sensory input over multiple movements of a sensor. In this view, even primary and secondary regions, such as V1 and V2, can learn and recognize complete 3D objects. This suggests that the hierarchical connections between regions are used to learn the compositional structure of parent objects composed of smaller child objects. We explain the theory by examining the different types of long-range connections between cortical regions and between the neocortex and thalamus. We describe these connections, and then suggest the specific roles they play in the context of a heterarchy of sensorimotor regions. We also suggest that the thalamus plays an essential role in transforming the pose between objects and sensors. The novel perspective we argue for here has broad implications for both neuroscience and artificial intelligence.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Error Highways: Scaling Predictive Coding to Very Deep Networks

    cs.LG 2026-06 unverdicted novelty 6.0

    Highway error propagation augments predictive coding with feedback matrices V to deliver depth-independent error corrections, allowing effective training of 128-layer MLPs while preserving local synaptic updates.