CTNN: Corticothalamic-inspired neural network

TL;DR

The paper introduces CTNN, a neural network inspired by corticothalamic connections, demonstrating improved efficiency and robustness in sensory prediction tasks, especially with multi-modal inputs and partial occlusions.

Contribution

It presents a novel corticothalamic-inspired neural network architecture that enhances processing efficiency and robustness over existing predictive coding models.

Findings

CTNN is input agnostic and multi-modal.

It maintains robustness during partial sensory occlusion.

It has higher processing efficiency proportional to input similarity.

Abstract

Sensory predictions by the brain in all modalities take place as a result of bottom-up and top-down connections both in the neocortex and between the neocortex and the thalamus. The bottom-up connections in the cortex are responsible for learning, pattern recognition, and object classification, and have been widely modelled using artificial neural networks (ANNs). Here, we present a neural network architecture modelled on the top-down corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN), consisting of an auto-encoder connected to a difference engine with a threshold. We demonstrate that the CTNN is input agnostic, multi-modal, robust during partial occlusion of one or more sensory inputs, and has significantly higher processing efficiency than other predictive coding models, proportional to the number of sequentially similar inputs in a sequence. This increased efficiency could be highly significant in more complex implementations of this architecture, where the predictive nature of the cortex will allow most of the incoming data to be discarded.