The brain as an efficient and robust adaptive learner

TL;DR

This paper proposes a biologically plausible model where neural circuits learn complex dynamic tasks through local plasticity rules, top-down feedback, and excitation-inhibition balance, achieving efficient and robust computation despite noisy activity.

Contribution

It introduces a novel framework combining adaptive control and efficient coding to enable learning in recurrent neural networks with local rules.

Findings

Networks can learn arbitrary dynamical systems.

Neural variability can coexist with efficient computation.

The model aligns with experimental neural activity patterns.

Abstract

Understanding how the brain learns to compute functions reliably, efficiently and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent network, e.g. the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level.