Deep Predictive Learning in Neocortex and Pulvinar

TL;DR

This paper proposes a biologically plausible predictive learning mechanism involving the pulvinar nucleus and layer 5 neurons, demonstrating its effectiveness in a large-scale visual system model that categorizes 3D objects in line with human and primate neural data.

Contribution

It introduces a detailed neural mechanism for predictive error-driven learning and implements it in a large-scale visual model, showing biologically plausible error backpropagation.

Findings

Model learns invariant 3D object categories from raw visual input.

Categories align with human judgments and primate neural data.

Demonstrates a biologically plausible error-driven learning process.

Abstract

How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely-embraced idea that learning is based on the differences between predictions and actual outcomes (i.e., predictive error-driven learning). Specifically, numerous weak projections into the pulvinar nucleus of the thalamus generate top-down predictions, and sparse, focal driver inputs from lower areas supply the actual outcome, originating in layer 5 intrinsic bursting (5IB) neurons. Thus, the outcome is only briefly activated, roughly every 100 msec (i.e., 10 Hz, alpha), resulting in a temporal difference error signal, which drives local synaptic changes throughout the neocortex, resulting in a biologically-plausible form of error backpropagation learning. We implemented these mechanisms in a large-scale model of the visual system, and found that the simulated inferotemporal (IT) pathway learns to systematically categorize 3D objects according to invariant shape properties, based solely on predictive learning from raw visual inputs. These categories match human judgments on the same stimuli, and are consistent with neural representations in IT cortex in primates.