Bidirectional optimization of firing rate in a mouse neuronal brain–machine interface

Neuroplasticity enables the brain to adapt neural activity, but whether this can be harnessed for abstract optimization tasks like seeking curve extrema remains unclear. Here, we used a brain-machine interface in mice, pairing auditory feedback of neuronal firing rate with water rewards, to investigate whether motor cortex neurons can optimize activity along a unimodal curve ([Formula: see text]). The curve maps firing rate ([Formula: see text]) to sound frequency increase speed ([Formula: see text]), where the curve extremum accelerates reward acquisition. Over conditioning sessions, mice learned to modulate firing rates towards this peak, reducing reward time from 18.64 ± 7.30 s to 11.59 ± 4.38 s and increasing high-response events from 66 to 104 occurrences. Putative neurons increasingly prioritized high-response intervals, with positive proportion increments in upper intervals versus negative trends in lower ones. These findings demonstrate that cortical neurons can dynamically optimize activity along non-monotonic reward landscapes, revealing neuroplasticity as a substrate for adaptive self-optimization. This expands our understanding of how the brain learns abstract rules via feedback, with implications for neuroprosthetic design that leverage neural adaptability.