'Backpropagation and the brain' realized in cortical error neuron microcircuits

Clinical Snapshot

Key Findings

• P Value: Not applicable for computational study

• Effect Size: Not specified in abstract

• Primary Outcome: Successful implementation of biologically plausible error backpropagation in cortical microcircuits

• Nnt Or Sensitivity: Model demonstrates superior scalability compared to other theories across multiple cortical areas

• Confidence Interval: Not applicable for computational study

Clinical Application

Theoretical model requiring experimental validation before clinical application Relevant to Australian neuroscience research institutions and may inform future therapeutic approaches for learning disabilities, though clinical translation is distant Researchers in computational neuroscience, neurologists studying learning disorders, neurosurgeons planning cortical interventions

Abstract

Neural responses to mismatches between expected and actual stimuli have been widely reported across different species. How does the brain use such error signals for learning? While global error signals can be useful, their ability to learn complex computation at the scale observed in the brain is lacking. In comparison, more local, neuron-specific error signals enable superior performance, but their computation and propagation remain unclear. Motivated by the breakthrough of deep learning, this has inspired the 'backpropagation and the brain' hypothesis, i.e., that the brain implements a form of the error backpropagation algorithm. In this work, we introduce a biologically motivated, multi-area cortical microcircuit model, implementing error backpropagation under consideration of recent physiological evidence. We model populations of cortical pyramidal cells acting as representation and error neurons, with bio-plausible local and inter-area connectivity, guided by experimental observations of connectivity of the primate visual cortex. In our model, all information transfer is biologically motivated, inference and learning occur without phases, and network dynamics demonstrably approximate those of error backpropagation. We show the capabilities of our model on a wide range of benchmarks, and compare to other models, such as dendritic hierarchical predictive coding. In particular, our model addresses shortcomings of other theories in terms of scalability to many cortical areas. Finally, we make concrete predictions, which differentiate it from other theories, and which can be tested experimentally.