Learning spatial hearing via innate mechanisms
Fig 2
Bootstrapping spatial hearing from an innate circuit.
(A) Interactive learning procedure with the left/right Teacher circuit. The agent makes an initial prediction of the sound location with its learned Student network, turns its head towards that sound, and uses the coarse-grained feedback (left/right) from the Teacher circuit to update the learned localiser based on whether it undershot or overshot the target. (B) A Teacher circuit implementation using a single lateral superior olive (LSO) neuron as the left/right discriminator (C) Another Teacher circuit using a population ensemble of LSO neurons. (D) Mean normalized firing rates of two Teachers—single LSO neuron (yellow) and LSO neural population (blue)—as functions of sound source angle, with variance indicated by vertical bars. Inset shows responses near midline (0°), where LSO neural population Teacher exhibits a slight leftward bias in the 0.5 firing rate crossing point, while the single LSO neuron Teacher shows a minimal rightward bias. (E) Response variance across sound positions. Both Teachers approach theoretical maximum Bernoulli variance (0.25) near their respective midline positions and minimal variance at lateral positions, indicating increased uncertainty for left-right discrimination at positions approaching the midline. LSO neural population Teacher shows narrower variance peaks and lower variance magnitude compared to single LSO neuron, with peak variance position reflecting the same directional biases observed in the mean responses. (F) Learning trajectories for Student networks trained with each Teacher type (mean shown as solid line, individual repeated experiments as shaded lines). All achieve mean absolute errors (MAE) below 5° after training, with LSO neural population Teacher enabling faster initial convergence but slightly higher final error. (G) Spatial distribution of localization errors after training (solid lines show mean across runs, shaded regions show ±1 SD). Both Teacher types enable precise mapping across all positions, with lowest error near the midline and increased errors at ±90°. Students that learns from the LSO neural population Teacher show marginally higher average error (dashed horizontal lines), consistent with the Teacher’s inherent directional bias.