arXiv subjects

Niels Leadholm

Publications and source records attributed to Niels Leadholm.

4 recordsLinked to original sources

arXiv2026

The Thousand Brains Theory 2.0: An Extension for the Long-Range Connections of the Neocortical Heterarchy

Vernon Mountcastle hypothesized that the basis for intelligence in mammals is the replication of a general computational unit, the cortical column. The Thousand Brains Theory proposed that each column is a sensorimotor system, capable of learning structured models of objects by integrating sensory input over multiple movements. Previous papers on the Thousand Brains Theory focused on the computations that occur within individual cortical columns, and how columns can use long-range connections to rapidly reach a consensus. However, several prominent long-range connection types in the neocortex were not addressed by the theory. These include hierarchical feedforward and feedback connections, as well as those that go through the thalamus. In addition, several theoretical requirements were not addressed. These include how the cortex learns compositional objects, and how information is converted from the egocentric perspective of sensors to the allocentric perspective of models in the cortex. In this paper, we extend the Thousand Brains Theory to address these issues. We begin by reviewing the anatomy of long-range neocortical connections, arguing that they form a heterarchy, rather than hierarchy, which has made their functions challenging to understand through existing theoretical models. We then propose specific roles for each of these connections. First, the thalamus converts the orientation of features and movement information from an egocentric perspective to the allocentric perspective of learned models. Second, hierarchical feedforward, feedback, and cortico-thalamo-cortical projections enable columns to learn compositional models. We discuss the relationship of our proposals to experimental findings at the levels of anatomy, neurophysiology, and behavior, along with testable predictions for future experimental work.

q-bio.NC↗

arXiv2025

Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference

Current AI systems achieve impressive performance on many tasks, yet they lack core attributes of biological intelligence, including rapid, continual learning, representations grounded in sensorimotor interactions, and structured knowledge that enables efficient generalization. Neuroscience theory suggests that mammals evolved flexible intelligence through the replication of a semi-independent, sensorimotor module, a functional unit known as a cortical column. To address the disparity between biological and artificial intelligence, thousand-brains systems were proposed as a means of mirroring the architecture of cortical columns and their interactions. In the current work, we evaluate the unique properties of Monty, the first implementation of a thousand-brains system. We focus on 3D object perception, and in particular, the combined task of object recognition and pose estimation. Utilizing the YCB dataset of household objects, we first assess Monty's use of sensorimotor learning to build structured representations, finding that these enable robust generalization. These representations include an emphasis on classifying objects by their global shape, as well as a natural ability to detect object symmetries. We then explore Monty's use of model-free and model-based policies to enable rapid inference by supporting principled movements. We find that such policies complement Monty's modular architecture, a design that can accommodate communication between modules to further accelerate inference speed via a novel `voting' algorithm. Finally, we examine Monty's use of associative, Hebbian-like binding to enable rapid, continual, and computationally efficient learning, properties that compare favorably to current deep learning architectures. While Monty is still in a nascent stage of development, these findings support thousand-brains systems as a powerful and promising new approach to AI.

cs.AI↗

arXiv2024

The Thousand Brains Project: A New Paradigm for Sensorimotor Intelligence

Artificial intelligence has advanced rapidly in the last decade, driven primarily by progress in the scale of deep-learning systems. Despite these advances, the creation of intelligent systems that can operate effectively in diverse, real-world environments remains a significant challenge. In this white paper, we outline the Thousand Brains Project, an ongoing research effort to develop an alternative, complementary form of AI, derived from the operating principles of the neocortex. We present an early version of a thousand-brains system, a sensorimotor agent that is uniquely suited to quickly learn a wide range of tasks and eventually implement any capabilities the human neocortex has. Core to its design is the use of a repeating computational unit, the learning module, modeled on the cortical columns found in mammalian brains. Each learning module operates as a semi-independent unit that can model entire objects, represents information through spatially structured reference frames, and both estimates and is able to effect movement in the world. Learning is a quick, associative process, similar to Hebbian learning in the brain, and leverages inductive biases around the spatial structure of the world to enable rapid and continual learning. Multiple learning modules can interact with one another both hierarchically and non-hierarchically via a "cortical messaging protocol" (CMP), creating more abstract representations and supporting multimodal integration. We outline the key principles motivating the design of thousand-brains systems and provide details about the implementation of Monty, our first instantiation of such a system. Code can be found at https://github.com/thousandbrainsproject/tbp.monty, along with more detailed documentation at https://thousandbrainsproject.readme.io/.

cs.AI↗

arXiv2021

Grid Cell Path Integration For Movement-Based Visual Object Recognition

Grid cells enable the brain to model the physical space of the world and navigate effectively via path integration, updating self-position using information from self-movement. Recent proposals suggest that the brain might use similar mechanisms to understand the structure of objects in diverse sensory modalities, including vision. In machine vision, object recognition given a sequence of sensory samples of an image, such as saccades, is a challenging problem when the sequence does not follow a consistent, fixed pattern - yet this is something humans do naturally and effortlessly. We explore how grid cell-based path integration in a cortical network can support reliable recognition of objects given an arbitrary sequence of inputs. Our network (GridCellNet) uses grid cell computations to integrate visual information and make predictions based on movements. We use local Hebbian plasticity rules to learn rapidly from a handful of examples (few-shot learning), and consider the task of recognizing MNIST digits given only a sequence of image feature patches. We compare GridCellNet to k-Nearest Neighbour (k-NN) classifiers as well as recurrent neural networks (RNNs), both of which lack explicit mechanisms for handling arbitrary sequences of input samples. We show that GridCellNet can reliably perform classification, generalizing to both unseen examples and completely novel sequence trajectories. We further show that inference is often successful after sampling a fraction of the input space, enabling the predictive GridCellNet to reconstruct the rest of the image given just a few movements. We propose that dynamically moving agents with active sensors can use grid cell representations not only for navigation, but also for efficient recognition and feature prediction of seen objects.

cs.AI↗